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Noowads/flan-t5-large-financial-phrasebank-lora
|
Noowads
| 2023-10-09T09:07:18Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-10-09T09:07:14Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
kerwin7/ppo-Huggy
|
kerwin7
| 2023-10-09T09:01:11Z | 10 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-10-09T09:01:00Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: kerwin7/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
haruyuu/MarianMT_zh-vi_Expanded_Vocab
|
haruyuu
| 2023-10-09T08:59:45Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"vi",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-10-02T02:29:17Z |
---
license: apache-2.0
language:
- vi
- zh
metrics:
- bleu
library_name: transformers
pipeline_tag: translation
---
# MarianMT for Chinese-Vietnamese translation
<!-- Provide a quick summary of what the model is/does. -->
Finetuned model from MarianMT for Chinese MMORPG translation.
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
170k rows of system notifications, names and conversations translated from Chinese MMORPG games.
|
checkiejan/multi-qa-mpnet-base-dot-v1-covidqa-search-65-25-v2-1epoch-mean-pooling
|
checkiejan
| 2023-10-09T08:58:05Z | 16 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-10-09T08:57:16Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 803 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 80,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
gbarone77/llama-2-7b-camoscio10p-adapter
|
gbarone77
| 2023-10-09T08:52:59Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T07:58:24Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
FaryalS/Reinforce-CartPole-v1
|
FaryalS
| 2023-10-09T08:45:35Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-25T18:13:48Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
indiejoseph/mbart-translation-zh-yue
|
indiejoseph
| 2023-10-09T08:45:10Z | 15 | 4 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"generated_from_trainer",
"zh",
"yue",
"base_model:indiejoseph/mbart-translation-zh-yue",
"base_model:finetune:indiejoseph/mbart-translation-zh-yue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-19T21:01:37Z |
---
language:
- zh
- yue
license: mit
base_model: indiejoseph/mbart-translation-zh-yue
tags:
- generated_from_trainer
model-index:
- name: mbart-translation-zh-yue
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-translation-zh-yue
This model is a fine-tuned version of [indiejoseph/mbart-translation-zh-yue](https://huggingface.co/indiejoseph/mbart-translation-zh-yue) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
nommis/segformer-b0-scene-parse-150
|
nommis
| 2023-10-09T08:45:05Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/segformer-b0-finetuned-ade-512-512",
"base_model:finetune:nvidia/segformer-b0-finetuned-ade-512-512",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-10-09T08:26:29Z |
---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: segformer-b0-scene-parse-150
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5845
- Mean Iou: 0.3672
- Mean Accuracy: 0.5180
- Overall Accuracy: 0.8290
- Per Category Iou: [0.7471836875245966, 0.7577637085524198, 0.8895546109310157, 0.11085509472606246, 0.9216125389161993, 0.06317712705545406, 0.9563222390153204, 0.830028328611898, 0.9289316210582935, 0.9387814548102598, nan, 0.7060518731988472, 0.1376830208065759, 0.0, 0.0, 0.5381879054195439, 0.0, 0.527306967984934, nan, nan, 0.8465404640804165, 0.5976121816945779, 0.9477989695381, nan, nan, 0.6212450409224044, 0.4029468326443188, nan, nan, nan, nan, nan, 0.16078532846007051, nan, 0.0, nan, 0.434913217623498, nan, nan, nan, nan, nan, nan, 0.2213077571123064, nan, nan, 0.8266935514170852, nan, nan, nan, nan, nan, 0.5321733037486862, 0.1086237598575426, nan, nan, nan, 0.17764306053090842, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6124371859296482, 0.0, nan, nan, nan, 0.9673457592833405, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.013296011196641007, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
- Per Category Accuracy: [0.8046025215425907, 0.8330558233283674, 0.9275152014660044, 0.847081838930551, 0.9766833720148048, 0.9471424341131182, 0.9685170298752354, 0.9812570167259719, 0.9895076096687556, 0.9631904896312408, nan, 0.8260084286574353, 0.16703022748519789, nan, nan, 0.5728285077951002, nan, 0.6947890818858561, nan, nan, 0.9260856681921443, 0.5976838849585837, 0.9607825170660191, nan, nan, 0.877902764192298, 0.9764888095105207, nan, nan, nan, nan, nan, 0.16078532846007051, nan, 0.0, nan, 0.4390161725067385, nan, nan, nan, nan, nan, nan, 0.23217077979468353, nan, nan, 0.8289671974682272, nan, nan, nan, nan, nan, 0.8216732780382258, 0.1086237598575426, nan, nan, nan, 0.17764306053090842, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6153083303281645, 0.0, nan, nan, nan, 0.9673457592833405, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.013296011196641007, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.9318 | 1.0 | 20 | 0.2916 | 0.4543 | 0.6104 | 0.9030 | [0.757090402085336, 0.7731882375762819, 0.8908793360413805, 0.1527664054275354, 0.9195973891162452, 0.16413046640868223, 0.9279042647106907, 0.9158157876205901, 0.9211787977335971, 0.9482659144816975, nan, 0.7666795858479597, 0.27555311757176815, 0.0, 0.0, 0.6286692251841095, nan, 0.4524504084014002, 0.0, nan, 0.8171449046012361, 0.9924705069001888, 0.9540714444197914, nan, nan, 0.6286554799547224, 0.8838112472160357, nan, nan, nan, nan, nan, 0.6348761698605717, nan, 0.0, nan, 0.5885750962772786, nan, nan, nan, nan, nan, nan, 0.3801982667248582, nan, nan, 0.9391201792664229, nan, nan, nan, nan, nan, 0.3228922508700902, 0.06351626016260163, nan, nan, nan, 0.6948766948766949, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.8541389504804139, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7224854716137684, 0.0, nan, nan, nan, 0.977742761473311, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.4930264993026499, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.7718560177991242, 0.8195061445433893, 0.9355203570316918, 0.8516465601565048, 0.9854602318558662, 0.8352087651761919, 0.9375295944323926, 0.9898959146004985, 0.9839212175470009, 0.9823839014428265, nan, 0.8336544250451535, 0.4696167030227485, nan, nan, 0.6749443207126948, nan, 0.641439205955335, nan, nan, 0.9010305205281006, 0.9951784635074122, 0.9850185550006872, nan, nan, 0.9154711444408217, 0.8859323547468885, nan, nan, nan, nan, nan, 0.6450819937251351, nan, 0.0, nan, 0.6179245283018868, nan, nan, nan, nan, nan, nan, 0.41457610986470866, nan, nan, 1.0, nan, nan, nan, nan, nan, 0.8196898665705012, 0.0635970490969219, nan, nan, nan, 0.7013869752916322, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9469436250409702, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7285430941218897, 0.0, nan, nan, nan, 0.9780509910548922, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.4947515745276417, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.358 | 2.0 | 40 | 0.2648 | 0.4367 | 0.5955 | 0.9083 | [0.7702341696432783, 0.768773656441056, 0.8885412077618897, 0.2813692748091603, 0.9139945857330831, 0.1786782345788557, 0.9448199240811036, 0.9165182705320892, 0.9358270651216372, 0.9491400452017247, nan, 0.7358159912376779, 0.08692919983242564, 0.0, 0.0, 0.6708966091117121, nan, 0.36234738085860574, 0.0, nan, 0.8197892588033983, 0.9910025820992201, 0.9578030508168607, nan, nan, 0.6293571794327393, 0.8798763248422724, nan, nan, nan, nan, nan, 0.8234798614794248, nan, 0.0, nan, 0.4612054612054612, nan, nan, 0.0, nan, nan, nan, 0.3806302188090449, nan, nan, 0.9435715132124659, nan, nan, nan, nan, nan, 0.43967181467181465, 0.008649198677181379, nan, nan, nan, 0.7387119355967798, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.8496329263189928, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7909324208725407, 0.0, nan, nan, nan, 0.9716803930067909, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.43356643356643354, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.7929439186325752, 0.8167186857485692, 0.9460989114566665, 0.7691555265731985, 0.9871055290722562, 0.8050044418122594, 0.9551981588133596, 0.9793541757844462, 0.9890778871978514, 0.9831476531239104, nan, 0.8089102950030103, 0.12932377687753194, nan, nan, 0.7182628062360802, nan, 0.380479735318445, nan, nan, 0.8990880057946202, 0.9926143837320914, 0.9723278508269574, nan, nan, 0.9127993334057892, 0.8815091812245354, nan, nan, nan, nan, nan, 0.8537374260115794, nan, 0.0, nan, 0.466644204851752, nan, nan, nan, nan, nan, nan, 0.42456999116187366, nan, nan, 1.0, nan, nan, nan, nan, nan, 0.8213126577713667, 0.008649198677181379, nan, nan, nan, 0.7754202259575641, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9151098000655523, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8335737468445726, 0.0, nan, nan, nan, 0.9716803930067909, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.4338698390482855, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.2793 | 3.0 | 60 | 0.2870 | 0.4162 | 0.5814 | 0.8955 | [0.713973155874427, 0.7965737195854968, 0.8779457965401705, 0.1786823866804748, 0.9031049407573143, 0.13306080011342422, 0.8949712104653629, 0.8798668772180055, 0.9140418210693488, 0.9444516025124818, nan, 0.7374674479166666, 0.16006959547629404, 0.0, 0.0, 0.6852987012987013, nan, 0.3823088455772114, 0.0, nan, 0.8192899264392972, 0.9923064302279692, 0.9578418621339193, nan, nan, 0.6603980053862435, 0.8805556329590102, nan, nan, nan, nan, nan, 0.7359250918572953, nan, 0.0, nan, 0.3828360710693932, nan, nan, 0.0, nan, nan, nan, 0.3880670308759438, nan, nan, 0.9488334405757728, nan, nan, nan, nan, nan, 0.23578881794113066, 0.014959432048681541, nan, nan, nan, 0.5399946375904907, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6331547809401983, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.8067641060503059, 0.0, nan, nan, nan, 0.9754239403134072, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.38838348495451364, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.7274818124028818, 0.846467308872006, 0.9580452690232044, 0.7313335507010108, 0.9861562071385515, 0.8337281610897246, 0.906299897589499, 0.9860141190797861, 0.9892927484333035, 0.9736339637051918, nan, 0.818362432269717, 0.22935493923340605, nan, nan, 0.734521158129176, nan, 0.4218362282878412, nan, nan, 0.8873999934152043, 0.9944274592058019, 0.9784670362395199, nan, nan, 0.9127993334057892, 0.8818580119439638, nan, nan, nan, nan, nan, 0.8033120936701491, nan, 0.0, nan, 0.38477088948787064, nan, nan, nan, nan, nan, nan, 0.4298048813651506, nan, nan, 1.0, nan, nan, nan, nan, nan, 0.8189686260367832, 0.01500890358687357, nan, nan, nan, 0.5549738219895288, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6489265814487053, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8559322033898306, 0.0, nan, nan, nan, 0.9754239403134072, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.38838348495451364, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.3673 | 4.0 | 80 | 0.3017 | 0.4121 | 0.5854 | 0.8959 | [0.6930239160932594, 0.821582774329195, 0.8876895483450188, 0.13995459704880817, 0.9210232458111495, 0.13345570739003332, 0.8831808124129265, 0.8573353268816851, 0.9138272583201268, 0.9376992176180817, nan, 0.75151613601906, 0.19001189060642093, 0.0, 0.0, 0.6703730645328899, 0.0, 0.2974197360646051, 0.0, nan, 0.8290362731152204, 0.9922762279954571, 0.9575418994413408, nan, nan, 0.7112122728265695, 0.8889979946061821, nan, nan, nan, nan, nan, 0.38998091603053436, nan, 0.0, nan, 0.38600602611315704, nan, nan, 0.0, nan, nan, nan, 0.30914030661750436, nan, nan, 0.9430382763716098, nan, nan, nan, nan, nan, 0.20779339295491878, 0.3257726819541376, nan, nan, nan, 0.36047684548372305, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.8134631618210015, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7669374274747836, 0.0, nan, nan, nan, 0.9710893065899567, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.4184744576627012, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7327217827376749, 0.8850805228423316, 0.9546915209357176, 0.8040430388001304, 0.9727492188478261, 0.8719277465205804, 0.8900518659633745, 0.9929024032881092, 0.9911548791405551, 0.9671088678211492, nan, 0.8355809753160747, 0.2489872234340916, nan, nan, 0.7183741648106904, nan, 0.6244830438378826, nan, nan, 0.9210482994765088, 0.9939341960254542, 0.9815824437623127, nan, nan, 0.9140854254972286, 0.8968995925657197, nan, nan, nan, nan, nan, 0.39657793446970924, nan, 0.0, nan, 0.3884770889487871, nan, nan, nan, nan, nan, nan, 0.32490312053844583, nan, nan, 0.9938212689003868, nan, nan, nan, nan, nan, 0.8211323476379373, 0.3324853726787077, nan, nan, nan, 0.36107283916597777, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.848840544083907, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7746123332131266, 0.0, nan, nan, nan, 0.9710893065899567, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.4184744576627012, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.3958 | 5.0 | 100 | 0.2844 | 0.4108 | 0.5785 | 0.8932 | [0.6971130499530577, 0.7759844211067662, 0.8892159728598072, 0.2810911731148221, 0.9235574071264019, 0.14931943394188182, 0.9214596712746272, 0.8290949347794696, 0.9015033696215656, 0.9425034743544165, nan, 0.7093385605146764, 0.1964597902097902, 0.0, 0.0, 0.5280968643278485, 0.0, 0.39581114660986866, 0.0, nan, 0.7903220919310365, 0.9904506342588486, 0.9590248266607023, nan, nan, 0.6430577721034153, 0.827307670881845, nan, nan, nan, nan, nan, 0.3877828875711001, nan, 0.0, nan, 0.45463675929025776, nan, nan, nan, nan, nan, nan, 0.33792841785759453, nan, nan, 0.9053883500665159, nan, nan, nan, nan, nan, 0.3064429889052266, 0.43461733400912317, nan, nan, nan, 0.15670065215394507, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.8583444990323061, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8072214336408801, 0.0, nan, nan, nan, 0.9739396566444681, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.3331000699790063, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7604578683429863, 0.8340669933869478, 0.9468485727938695, 0.8835996087381807, 0.9747963762692813, 0.8186259994077584, 0.9322053495721884, 0.9832930565334044, 0.9963831692032229, 0.9683707184246791, nan, 0.8496688741721854, 0.2801495793081957, nan, nan, 0.5682628062360802, nan, 0.4611248966087676, nan, nan, 0.8587561320910019, 0.9923344235486509, 0.982223851193476, nan, nan, 0.9123555410643771, 0.8288776022771669, nan, nan, nan, nan, nan, 0.41456156806934696, nan, 0.0, nan, 0.45754716981132076, nan, nan, nan, nan, nan, nan, 0.36331497722482836, nan, nan, 1.0, nan, nan, nan, nan, nan, 0.8267219617742517, 0.43627575680488423, nan, nan, nan, 0.15670065215394507, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.9448541461815798, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8203209520375045, 0.0, nan, nan, nan, 0.9739396566444681, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.3331000699790063, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.4259 | 6.0 | 120 | 0.3058 | 0.4001 | 0.5730 | 0.8931 | [0.6985909995058325, 0.769530414879837, 0.8823505792125921, 0.13364543275929333, 0.9235814361253398, 0.1355318670893976, 0.9608289930555556, 0.859073263401338, 0.9031491190933664, 0.9501623220082929, nan, 0.7494519947391495, 0.14520255863539447, 0.0, 0.0, 0.6518163838867492, nan, 0.3726523133302794, 0.0, nan, 0.8326698033538424, 0.990911752182244, 0.9583520129113243, nan, nan, 0.6285787193786099, 0.8561691056910569, nan, nan, nan, nan, nan, 0.3058313253012048, nan, 0.0, nan, 0.4694694694694695, nan, nan, 0.0, nan, nan, nan, 0.27489356294084005, nan, nan, 0.9074867351409239, nan, nan, nan, nan, nan, 0.5608066312012866, 0.07498729028978139, nan, nan, nan, 0.3798442510306917, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7700660718206679, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.797708315780605, 0.0, nan, nan, nan, 0.9810270348341015, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.2827151854443667, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.742632257380986, 0.8249900568277573, 0.9633586581500463, 0.781871535702641, 0.9755505921613964, 0.8038199585430856, 0.975102135203885, 0.9895534032310239, 0.9931244404655327, 0.9816035464643278, nan, 0.8233594220349187, 0.2122156435026488, nan, nan, 0.6973273942093541, nan, 0.6728701406120761, nan, nan, 0.90896519935469, 0.9927788047922074, 0.9793833325697531, nan, nan, 0.9147103575698293, 0.9183736116537367, nan, nan, nan, nan, nan, 0.3078888637319274, nan, 0.0, nan, 0.4740566037735849, nan, nan, nan, nan, nan, nan, 0.2941056496022843, nan, nan, 0.9364796302808057, nan, nan, nan, nan, nan, 0.8173458348359178, 0.07504451793436785, nan, nan, nan, 0.38082116285478096, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7902736807604064, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.853588171655247, 0.0, nan, nan, nan, 0.9814136160039931, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.2827151854443667, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.184 | 7.0 | 140 | 0.2974 | 0.4207 | 0.5895 | 0.8928 | [0.7199834007677145, 0.7778380355408695, 0.8887971597402122, 0.18600773137269763, 0.9171883036583898, 0.15411919619854583, 0.9661967064577227, 0.9106459838504037, 0.9002978117311925, 0.9473658725547163, nan, 0.7332136249863968, 0.1433932451115939, 0.0, 0.0, 0.6779255871960091, 0.0, 0.2659248508305112, 0.0, nan, 0.8405688846395172, 0.897895937948151, 0.9592952884744699, nan, nan, 0.6414578123906418, 0.709452482088297, nan, nan, nan, nan, nan, 0.4352997145575642, nan, 0.0, nan, 0.4570664884731039, nan, nan, 0.0, nan, nan, nan, 0.3233139681737813, nan, nan, 0.951440399159919, nan, nan, nan, nan, nan, 0.7098207202919244, 0.1757743641400151, nan, nan, nan, 0.6909833927484567, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7902802706061024, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.804374844643301, 0.0, nan, nan, nan, 0.9738083041073938, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.2232330300909727, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7659009040824976, 0.8315053625718773, 0.9581154127740539, 0.8001304206064558, 0.9762494794745532, 0.7971572401539828, 0.9746286242856042, 0.9785549825890054, 0.9959534467323187, 0.9745305417655946, nan, 0.8112582781456954, 0.22623870364599563, nan, nan, 0.726391982182628, nan, 0.6819685690653433, nan, nan, 0.9262502880848121, 0.8986899640939955, 0.9803912585330096, nan, nan, 0.913089156975691, 0.9298571189373221, nan, nan, nan, nan, nan, 0.44393052366012226, nan, 0.0, nan, 0.4609164420485175, nan, nan, nan, nan, nan, nan, 0.3480862057243864, nan, nan, 0.9842392123373688, nan, nan, nan, nan, nan, 0.8067075369635773, 0.17756296107860595, nan, nan, nan, 0.7299531551391568, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.8207964601769911, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8752253876667869, 0.0, nan, nan, nan, 0.9738083041073938, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.2232330300909727, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.2589 | 8.0 | 160 | 0.2757 | 0.4402 | 0.6046 | 0.9058 | [0.7494836247418124, 0.7757339880725955, 0.8948167955362021, 0.17587661664015491, 0.9193940776219257, 0.16820850109106555, 0.9677794251019621, 0.8990195564410423, 0.9004855940433797, 0.9514046344091209, nan, 0.7424519179112496, 0.13446096019135487, 0.0, 0.0, 0.6579439252336449, 0.0, 0.45012644001123914, 0.0, nan, 0.8331806766139754, 0.9677237522469542, 0.9568646894437934, nan, nan, 0.6437899145454782, 0.8706945741259466, nan, nan, nan, nan, nan, 0.57135088448866, nan, 0.0, nan, 0.45193591455273696, nan, nan, 0.0, nan, nan, nan, 0.3413534789233113, nan, nan, 0.9682831504997991, nan, nan, nan, nan, nan, 0.6822727272727273, 0.5914169629253428, nan, nan, nan, 0.6238027669386307, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.8359320320398165, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7558560947582428, 0.0, nan, nan, nan, 0.9781823435919664, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.22883135059482154, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7784865447097048, 0.8317008554498696, 0.9477166017106308, 0.8291490055428757, 0.986045549980635, 0.8103346165235416, 0.9811806939687924, 0.9788404087302343, 0.9960966875559535, 0.9823340915505819, nan, 0.8320288982540638, 0.24524774072919914, nan, nan, 0.7055679287305122, nan, 0.6625310173697271, nan, nan, 0.9284232706680275, 0.9688977567634826, 0.9726027397260274, nan, nan, 0.8965782704778467, 0.9368337333258916, nan, nan, nan, nan, nan, 0.5777080570559886, nan, 0.0, nan, 0.45619946091644203, nan, nan, nan, nan, nan, nan, 0.3655585015976613, nan, nan, 0.9987441603456071, nan, nan, nan, nan, nan, 0.8119365308330329, 0.5924701093869245, nan, nan, nan, 0.6460916689629834, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.8807972795804654, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7709159754778219, 0.0, nan, nan, nan, 0.9781823435919664, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.22883135059482154, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1675 | 9.0 | 180 | 0.3176 | 0.4169 | 0.5805 | 0.8947 | [0.7345716657349528, 0.7692312499414133, 0.8668217310538081, 0.17813902870357776, 0.9199850922485133, 0.15081353339838086, 0.9602279820871217, 0.9039625322317529, 0.8930710283984803, 0.9514323292592949, nan, 0.7358287619662257, 0.0801781737193764, 0.0, 0.0, 0.6477130977130977, 0.0, 0.5261121856866537, 0.0, nan, 0.8446311463023966, 0.9258387861461561, 0.9518799038505148, nan, nan, 0.6401409220364263, 0.7678351464916825, nan, nan, nan, nan, nan, 0.5576983282198986, nan, 0.0, nan, 0.3928452022734871, nan, nan, nan, nan, nan, nan, 0.3433964622788924, nan, nan, 0.9686649503493932, nan, nan, nan, nan, nan, 0.5423526604628967, 0.16128211650979393, nan, nan, nan, 0.6541748264197971, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6691467242855396, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6146253925527142, 0.0, nan, nan, nan, 0.9828467105349568, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.25892232330300907, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7656890097471394, 0.8297796323385667, 0.9655681863018023, 0.8539289207694816, 0.9847933768778957, 0.8467574770506366, 0.9739899351400161, 0.9806100508058532, 0.9932676812891674, 0.9848743960550566, nan, 0.8237206502107164, 0.10096603303209722, nan, nan, 0.6938752783964365, nan, 0.674937965260546, nan, nan, 0.9201593520561024, 0.926801521561378, 0.9615613689467174, nan, nan, 0.9018222656957577, 0.930652452977619, nan, nan, nan, nan, nan, 0.5761878578128538, nan, 0.0, nan, 0.39588948787061995, nan, nan, nan, nan, nan, nan, 0.37351281528316, nan, nan, 0.9922891445220274, nan, nan, nan, nan, nan, 0.8196898665705012, 0.16128211650979393, nan, nan, nan, 0.675025259483788, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6724639462471321, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6175622069960331, 0.0, nan, nan, nan, 0.9829241701803471, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.25892232330300907, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.2161 | 10.0 | 200 | 0.3322 | 0.4249 | 0.5776 | 0.8853 | [0.7206124240435668, 0.7760577073859182, 0.865809804968437, 0.1756513626974079, 0.911514857881137, 0.13624416925153315, 0.9370541586390206, 0.890462830685554, 0.8943555398138906, 0.9489619516923823, nan, 0.7425758049390434, 0.14700339238597815, 0.0, 0.0, 0.6270483302219456, 0.0, 0.5200642054574639, 0.0, nan, 0.8321939466947749, 0.888232734506123, 0.956649982123704, nan, nan, 0.6452770748334452, 0.7136619762979565, nan, nan, nan, nan, nan, 0.5892282702168997, nan, 0.0, nan, 0.4864058355437666, nan, nan, nan, nan, nan, nan, 0.2877469351457791, nan, nan, 0.9710707377390367, nan, nan, nan, nan, nan, 0.39037848090352617, 0.048588145510048336, nan, nan, nan, 0.570629494806002, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.5433319008738003, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7658210947930574, 0.0, nan, nan, nan, 0.9707477899935637, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.16864940517844645, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.7465258157931911, 0.8340332877183285, 0.9668702296769441, 0.859471796543854, 0.9861474710471371, 0.8519395913532721, 0.9453039829976545, 0.9844347610983198, 0.9946642793196061, 0.9752112769596042, nan, 0.8580975316074654, 0.24306637581801185, nan, nan, 0.6732739420935412, nan, 0.6699751861042184, nan, nan, 0.9097882988180291, 0.8889669024849799, 0.9807119622485911, nan, nan, 0.9053001485345796, 0.9419266618295473, nan, nan, nan, nan, nan, 0.5983762978296729, nan, 0.0, nan, 0.4942722371967655, nan, nan, nan, nan, nan, nan, 0.30797470936161536, nan, nan, 0.9961194554679259, nan, nan, nan, nan, nan, 0.8164442841687702, 0.048588145510048336, nan, nan, nan, 0.5903370992927345, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.5438995411340544, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7756941940137035, 0.0, nan, nan, nan, 0.9707477899935637, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.16864940517844645, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.2867 | 11.0 | 220 | 0.3251 | 0.4367 | 0.5798 | 0.8946 | [0.6908364671638303, 0.7811886250132491, 0.8844631001150284, 0.14853779197194564, 0.9118543751676815, 0.12206634613273618, 0.9608279230513446, 0.8550465927515107, 0.8781107160995429, 0.9417482019050087, nan, 0.7406341565508581, 0.1685185185185185, 0.0, 0.0, 0.6777650608677557, 0.0, 0.5124835742444153, 0.0, nan, 0.8362949866650685, 0.9828237581741419, 0.9561372163659103, nan, nan, 0.6500238626037043, 0.9049626234517378, nan, nan, nan, nan, nan, 0.5171646535282899, nan, 0.0, nan, 0.5261255340124876, nan, nan, nan, nan, nan, nan, 0.27513328255902514, nan, nan, 0.9520947307803053, nan, nan, nan, nan, nan, 0.5688645145207528, 0.1190536759094378, nan, nan, nan, 0.37078445747800587, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6697260718048464, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8258586937177433, 0.0, nan, nan, nan, 0.976501031117416, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.244926522043387, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7176949427885294, 0.844613497097942, 0.9607019635866253, 0.7733941962830128, 0.979831276954482, 0.8232158720758069, 0.9704826507801918, 0.9934732555705669, 0.9906535362578335, 0.9652492985106842, nan, 0.8521974714027695, 0.2552196946089124, nan, nan, 0.7253897550111359, nan, 0.6451612903225806, nan, nan, 0.9188423929147598, 0.9837889722350599, 0.9807119622485911, nan, nan, 0.9005090026446401, 0.9256711503041803, nan, nan, nan, nan, nan, 0.5262476954426367, nan, 0.0, nan, 0.5394204851752021, nan, nan, nan, nan, nan, nan, 0.2947175198857842, nan, nan, 0.996621791329683, nan, nan, nan, nan, nan, 0.8229354489722323, 0.1190536759094378, nan, nan, nan, 0.37163589602277947, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6771345460504753, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8367291741795889, 0.0, nan, nan, nan, 0.976501031117416, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.244926522043387, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1295 | 12.0 | 240 | 0.3323 | 0.4194 | 0.5683 | 0.8848 | [0.7010788082853816, 0.7637095563396141, 0.8785136123592344, 0.15548340548340547, 0.9175356281036783, 0.13378924609889076, 0.9442461893312154, 0.8539986531544109, 0.885516711486746, 0.9395302915041355, nan, 0.7404405098394752, 0.07083438366523821, 0.0, 0.0, 0.6082108992393457, 0.0, 0.5076373090672733, 0.0, nan, 0.8392819108474061, 0.9144450121059997, 0.9499954747035931, nan, nan, 0.6311522253832482, 0.7531461652401036, nan, nan, nan, nan, nan, 0.45099949710837317, nan, 0.0, nan, 0.41029067824924825, nan, nan, nan, nan, nan, nan, 0.3048211508553655, nan, nan, 0.9626255406273094, nan, nan, nan, nan, nan, 0.3991655076495132, 0.03510557110150089, nan, nan, nan, 0.3435352332081004, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7266600554532392, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8336933045356372, 0.0, nan, nan, nan, 0.9783271160615773, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.23722883135059483, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.7390035668879785, 0.8229609755768725, 0.9582951561356055, 0.8431692207368764, 0.9754836154605522, 0.8429079064258217, 0.9557047053771018, 0.9893631191368714, 0.9905102954341988, 0.9637716050407611, nan, 0.8358819987959061, 0.08756622000623247, nan, nan, 0.65, nan, 0.6459884201819686, nan, nan, 0.9081420998913509, 0.9147010202993352, 0.9617904430292756, nan, nan, 0.9102180922363511, 0.9294106156164537, nan, nan, nan, nan, nan, 0.46411359446259337, nan, 0.0, nan, 0.4137466307277628, nan, nan, nan, nan, nan, nan, 0.3331293765721667, nan, nan, 0.997865072587532, nan, nan, nan, nan, nan, 0.8279841327082582, 0.03510557110150089, nan, nan, nan, 0.3443556535317351, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7355170435922649, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8699963937973314, 0.0, nan, nan, nan, 0.9783399666364556, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.23722883135059483, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0919 | 13.0 | 260 | 0.3148 | 0.4263 | 0.5733 | 0.8992 | [0.767667025262455, 0.7688925066195115, 0.8805410795856655, 0.1402587954061217, 0.9193929356826759, 0.1418159509202454, 0.9500165940337648, 0.876767711229764, 0.8975355419540788, 0.9452446310350395, nan, 0.7395883453622026, 0.12275948311796582, 0.0, 0.0, 0.6027012987012987, 0.0, 0.5034057735971457, 0.0, nan, 0.8252400632065151, 0.9831381358716402, 0.9517680531093348, nan, nan, 0.6451857516499476, 0.8784358617459966, nan, nan, nan, nan, nan, 0.2855063557266229, nan, 0.0, nan, 0.35097119892833223, nan, nan, nan, nan, nan, nan, 0.22902813299232735, nan, nan, 0.9261085561102776, nan, nan, nan, nan, nan, 0.46154622527250705, 0.20605443907402696, nan, nan, nan, 0.4981389578163772, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6865898365503412, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7961016949152543, 0.0, nan, nan, nan, 0.9777488802196215, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.22673198040587825, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.8221765079813533, 0.8309795541414156, 0.9600049100625595, 0.8800130420606456, 0.9861299988643081, 0.8556411015694403, 0.961414366101023, 0.9780031587159629, 0.9924798567591764, 0.9719404273688754, nan, 0.8328717639975918, 0.18354627609847304, nan, nan, 0.6459910913140312, nan, 0.6418527708850289, nan, nan, 0.8941164850360518, 0.9840556009811938, 0.9655472579832318, nan, nan, 0.9093033365938485, 0.9422336328626444, nan, nan, nan, nan, nan, 0.2855063557266229, nan, 0.0, nan, 0.353099730458221, nan, nan, nan, nan, nan, nan, 0.2435243728329594, nan, nan, 0.9458105189129452, nan, nan, nan, nan, nan, 0.8245582401730978, 0.20605443907402696, nan, nan, nan, 0.5163038486268026, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.6944239593575877, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.8469166967183556, 0.0, nan, nan, nan, 0.9777488802196215, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.22673198040587825, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.2044 | 14.0 | 280 | 0.3544 | 0.4077 | 0.5653 | 0.8639 | [0.7105300015653707, 0.7583391059604715, 0.8882915513592707, 0.15015921818381464, 0.9189025285680875, 0.14528139405298618, 0.9345192788639279, 0.8767106883810045, 0.8664882360263911, 0.9431698929592018, nan, 0.738849487785658, 0.15073999634569707, 0.0, 0.0, 0.6473385012919897, 0.0, 0.526615333110144, nan, nan, 0.8257736973308202, 0.7232729550695083, 0.95317981469821, nan, nan, 0.628257861870396, 0.4960114106739797, nan, nan, nan, nan, nan, 0.27435574093833864, nan, 0.0, nan, 0.3640922768304915, nan, nan, 0.0, nan, nan, nan, 0.23656605959199434, nan, nan, 0.9229412051007495, nan, nan, nan, nan, nan, 0.36572724327826367, 0.16611549224116, nan, nan, nan, 0.5021714083133918, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7236747421443991, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6962286123801845, 0.0, nan, nan, nan, 0.9753713992985774, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.23792862141357593, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7614246362480577, 0.8191522350228861, 0.9568966651030456, 0.8917508966416694, 0.9839663602239934, 0.849274503997631, 0.9464051711797029, 0.9751869541225049, 0.9970277529095792, 0.9699480316790915, nan, 0.8467188440698374, 0.2570894359613587, nan, nan, 0.6974387527839644, nan, 0.6505376344086021, nan, nan, 0.8943140289072532, 0.7234304454477586, 0.9709534063316076, nan, nan, 0.913025758069775, 0.9413406262209075, nan, nan, nan, nan, nan, 0.2744444803829608, nan, 0.0, nan, 0.3669137466307278, nan, nan, nan, nan, nan, nan, 0.24991501801618057, nan, nan, 0.9525669362535791, nan, nan, nan, nan, nan, 0.8142805625676163, 0.16611549224116, nan, nan, nan, 0.5204372187012033, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.7416420845624385, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.700685178507032, 0.0, nan, nan, nan, 0.9753713992985774, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.23792862141357593, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1597 | 15.0 | 300 | 0.3508 | 0.3996 | 0.5614 | 0.8680 | [0.7327140434561054, 0.7598310684668459, 0.8749710747428644, 0.17029559250461862, 0.9015595608148672, 0.15086708124778805, 0.9450641788713352, 0.8656128478359679, 0.8924420471328581, 0.9417474157594189, nan, 0.6976988546810969, 0.1468602986295766, 0.0, 0.0, 0.6618178066646033, 0.0, 0.5279170845870946, 0.0, nan, 0.8204749600098438, 0.7411213749039158, 0.9501971269316174, nan, nan, 0.6324974106274128, 0.5135965621606635, nan, nan, nan, nan, nan, 0.4733572130471994, nan, 0.0, nan, 0.3347826086956522, nan, nan, nan, nan, nan, nan, 0.2527394611318809, nan, nan, 0.9449091749142815, nan, nan, nan, nan, nan, 0.44752904114983266, 0.049096921902823706, nan, nan, nan, 0.42811387900355874, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.5918703150709976, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7231467473524962, 0.0, nan, nan, nan, 0.9784319134124075, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.23093072078376486, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7743943353581014, 0.8162333241204506, 0.9614428569549721, 0.8415389631561787, 0.9839488880411644, 0.8836245188036719, 0.9660393564656264, 0.9784408121325139, 0.9953804834377797, 0.9665775623038736, nan, 0.7995183624322697, 0.2237457151760673, nan, nan, 0.7143652561247216, nan, 0.6530190239867659, nan, nan, 0.8781483554472722, 0.7412234704397597, 0.9606450726164841, nan, nan, 0.9125819657283628, 0.9305408271474019, nan, nan, nan, nan, nan, 0.4830028786751625, nan, 0.0, nan, 0.33726415094339623, nan, nan, nan, nan, nan, nan, 0.26657148684478893, nan, nan, 0.9759757874114633, nan, nan, nan, nan, nan, 0.8196898665705012, 0.049096921902823706, nan, nan, nan, 0.4419950399559107, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.5968534906588004, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7326000721240534, 0.0, nan, nan, nan, 0.9784319134124075, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.23093072078376486, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0763 | 16.0 | 320 | 0.3851 | 0.4006 | 0.5551 | 0.8518 | [0.7153059778538027, 0.7569371103862725, 0.8680917334007922, 0.175148588410104, 0.9130648023953718, 0.15358483394307978, 0.9589653253216472, 0.8792287106292459, 0.9073221689028172, 0.9341358054652235, nan, 0.7209589188029604, 0.07477461552059396, 0.0, 0.0, 0.5719146303742654, 0.0, 0.47051921384570256, nan, nan, 0.8193126324518566, 0.6164846201018512, 0.9521290900941282, nan, nan, 0.6323551580322597, 0.42311201027696926, nan, nan, nan, nan, nan, 0.215643794865162, nan, 0.0, nan, 0.40680226742247416, nan, nan, 0.0, nan, nan, nan, 0.23530177745947, nan, nan, 0.9162390210204283, nan, nan, nan, nan, nan, 0.5884248517659191, 0.15899262274230475, nan, nan, nan, 0.5106901217861975, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.5716360512212085, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7312365975696926, 0.0, nan, nan, nan, 0.9751349647318438, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.1728481455563331, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.7799344893346518, 0.8189466304443082, 0.9628194280653916, 0.922399739158787, 0.9812436117331532, 0.9572105419010957, 0.9771338273997644, 0.9752440393507507, 0.9953088630259624, 0.9586411861395673, nan, 0.8093317278747743, 0.13181676534746026, nan, nan, 0.617706013363029, nan, 0.663358147229115, nan, nan, 0.8782800513614065, 0.6164900991858936, 0.973198332340679, nan, nan, 0.9118211788573706, 0.9513171847965619, nan, nan, nan, nan, nan, 0.21570656920141024, nan, 0.0, nan, 0.4110512129380054, nan, nan, nan, nan, nan, nan, 0.24569991161873683, nan, nan, 0.9327623449038027, nan, nan, nan, nan, nan, 0.8231157591056617, 0.15899262274230475, nan, nan, nan, 0.5199779553596032, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.5724557522123894, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7378290659935088, 0.0, nan, nan, nan, 0.9751349647318438, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.1728481455563331, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1299 | 17.0 | 340 | 0.4027 | 0.4042 | 0.5626 | 0.8684 | [0.711767437232596, 0.7603993344425957, 0.89004551116812, 0.16463737427210165, 0.917176195397922, 0.06863881126030945, 0.9566389648358192, 0.9064951197870452, 0.8942270562491955, 0.9379202223946211, nan, 0.722851997033584, 0.12831497348021215, 0.0, 0.0, 0.5855584415584416, 0.0, 0.5308362931869551, 0.0, nan, 0.8307268822408421, 0.8860878057442181, 0.9533040830945558, nan, nan, 0.6326260612216746, 0.6921050987903645, nan, nan, nan, nan, nan, 0.386464079486435, nan, 0.0, nan, 0.428285523682455, nan, nan, nan, nan, nan, nan, 0.2442772633744856, nan, nan, 0.9634067397066188, nan, nan, nan, nan, nan, 0.5607453416149069, 0.1912999236835411, nan, nan, nan, 0.6692953883079034, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.00272451655195018, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7687428420403488, 0.0, nan, nan, nan, 0.9761069735061932, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.2330300909727082, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7578930639920893, 0.8179253486851419, 0.9490975568054782, 0.9126181936746006, 0.9819628832596105, 0.8945809890435298, 0.9728777350761472, 0.9719901813407418, 0.995129811996419, 0.9635059522821232, nan, 0.8215532811559302, 0.19601121844811467, nan, nan, 0.6276169265033408, nan, 0.6799007444168734, nan, nan, 0.904191222467323, 0.8870249564506382, 0.9755348879827737, nan, nan, 0.9138046589138862, 0.9276804152480884, nan, nan, nan, nan, nan, 0.3874890836756477, nan, 0.0, nan, 0.4326145552560647, nan, nan, nan, nan, nan, nan, 0.2582772452240125, nan, nan, 0.9905686441955092, nan, nan, nan, nan, nan, 0.8139199423007573, 0.1912999236835411, nan, nan, nan, 0.7171856342426748, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.00272451655195018, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7866931121529029, 0.0, nan, nan, nan, 0.9761069735061932, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.2330300909727082, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.4063 | 18.0 | 360 | 0.4278 | 0.3847 | 0.5355 | 0.8529 | [0.7143884223748995, 0.7623860471202557, 0.8895063117621785, 0.15164858769197723, 0.9184909290143108, 0.0688372957772665, 0.9465640969881484, 0.8664226591225802, 0.893223819301848, 0.9395075898414136, nan, 0.729230133732638, 0.1358986848514369, 0.0, 0.0, 0.5792752569826601, 0.0, 0.5139756658993752, nan, nan, 0.8304501431443017, 0.7691829484902309, 0.951791004578508, nan, nan, 0.6314232808579753, 0.5424600121150603, nan, nan, nan, nan, nan, 0.1745269426568053, nan, 0.0, nan, 0.3247491638795987, nan, nan, nan, nan, nan, nan, 0.2419600864370154, nan, nan, 0.9616261074079039, nan, nan, nan, nan, nan, 0.45268154731845267, 0.062325108114983464, nan, nan, nan, 0.40283363802559413, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0031137332022287776, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7143369496996324, 0.0, nan, nan, nan, 0.973532463779538, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.1630510846745976, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7807820666760842, 0.8247777111154554, 0.952999302946476, 0.9207694815780894, 0.9787887700456898, 0.8771098608232158, 0.9586448778231712, 0.9819610678743363, 0.9969561324977618, 0.9649338358598017, nan, 0.8502709211318483, 0.17388594577750077, nan, nan, 0.6212694877505568, nan, 0.6464019851116626, nan, nan, 0.8977381226747441, 0.7697571900885207, 0.9714573693132359, nan, nan, 0.9049016411259646, 0.9246525646034492, nan, nan, nan, nan, nan, 0.17631076753889446, nan, 0.0, nan, 0.32715633423180596, nan, nan, nan, nan, nan, nan, 0.25882112992045686, nan, nan, 0.9910081880745466, nan, nan, nan, nan, nan, 0.8081500180310134, 0.062325108114983464, nan, nan, nan, 0.4047947092863048, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0031137332022287776, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7182654165164082, 0.0, nan, nan, nan, 0.973532463779538, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.1630510846745976, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1576 | 19.0 | 380 | 0.3667 | 0.4123 | 0.5579 | 0.8813 | [0.7369791774663774, 0.760305204088702, 0.8775772628036934, 0.12495485734922354, 0.9146936169634731, 0.08435593985181303, 0.9592746971146828, 0.8654811680351956, 0.8989921829575555, 0.9431517031866072, nan, 0.6995826122365876, 0.1809192944949225, 0.0, 0.0, 0.571029198591841, 0.0, 0.5046865846514352, nan, nan, 0.8343263672743442, 0.9787524972253052, 0.9517319447588861, nan, nan, 0.6292906033328893, 0.8740046471555752, nan, nan, nan, nan, nan, 0.2864766956690494, nan, 0.0, nan, 0.3637274549098196, nan, nan, nan, nan, nan, nan, 0.2751251443973816, nan, nan, 0.9463486238532111, nan, nan, nan, nan, nan, 0.5693256165072974, 0.19893156957517172, nan, nan, nan, 0.40896714467235434, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.16664965604523466, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7574946466809421, 0.0, nan, nan, nan, 0.9765273016248309, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.21343596920923724, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7844946320101709, 0.8137930337124097, 0.9604170045987996, 0.9025105966742746, 0.9804369792925514, 0.858010068107788, 0.9769631432315469, 0.9807622780811752, 0.9966338406445837, 0.9690680569161035, nan, 0.8274533413606261, 0.21096914926768465, nan, nan, 0.6141425389755011, nan, 0.7125723738626965, nan, nan, 0.9266782998057486, 0.9797006647943404, 0.9629816282585788, nan, nan, 0.8984711806687679, 0.9342244795445667, nan, nan, nan, nan, nan, 0.2864766956690494, nan, 0.0, nan, 0.3669137466307278, nan, nan, nan, nan, nan, nan, 0.2914542117071181, nan, nan, 0.9715677902245441, nan, nan, nan, nan, nan, 0.8159033537684818, 0.19893156957517172, nan, nan, nan, 0.4138881234499862, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.16724024909865617, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7654165164082222, 0.0, nan, nan, nan, 0.9765273016248309, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.21343596920923724, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.373 | 20.0 | 400 | 0.4207 | 0.3755 | 0.5393 | 0.8527 | [0.7240509846710884, 0.7581390694096086, 0.8861693340006754, 0.12885037067975358, 0.9125215009409189, 0.07408590336510208, 0.9159343751352755, 0.848974497922722, 0.8975971617481051, 0.940194872458889, nan, 0.7025333122662875, 0.22879177377892032, 0.0, 0.0, 0.6402981983847588, 0.0, 0.5025380710659898, 0.0, nan, 0.8334283913187044, 0.7944852696312412, 0.9478126701760755, nan, nan, 0.6271092109590427, 0.5557530611063801, nan, nan, nan, nan, nan, 0.2435719137100165, nan, 0.0, nan, 0.3537278502173186, nan, nan, nan, nan, nan, nan, 0.2492418865733273, nan, nan, 0.9105508231181941, nan, nan, nan, nan, nan, 0.29468693167056376, 0.10175527855507505, nan, nan, nan, 0.298062865497076, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.008767617174696821, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7374251764495667, 0.0, nan, nan, nan, 0.9754370755671146, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.18264520643806859, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7821373075293121, 0.8170860775365201, 0.9547309767955704, 0.8046951418324095, 0.9856262175927409, 0.8586023097423748, 0.9320016297585094, 0.9837687667687858, 0.996598030438675, 0.9644689435321855, nan, 0.8030704394942806, 0.3328139607354316, nan, nan, 0.688641425389755, nan, 0.6960297766749379, nan, nan, 0.9141013400059264, 0.7951313590955953, 0.956888257662528, nan, nan, 0.898054559287034, 0.9379360383992856, nan, nan, nan, nan, nan, 0.24358767021379824, nan, 0.0, nan, 0.3564690026954178, nan, nan, nan, nan, nan, nan, 0.26262832279556736, nan, nan, 0.9356507761089065, nan, nan, nan, nan, nan, 0.8180670753696357, 0.10175527855507505, nan, nan, nan, 0.29962340405988797, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.008767617174696821, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7441399206635413, 0.0, nan, nan, nan, 0.9754370755671146, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.18264520643806859, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.2412 | 21.0 | 420 | 0.4685 | 0.3629 | 0.5203 | 0.8294 | [0.7737332796198401, 0.7618948035293068, 0.8897191519313165, 0.1285204991087344, 0.9181924073891787, 0.06895820072140887, 0.9349274180042247, 0.8150776964542594, 0.9189700368318015, 0.9435114256563072, nan, 0.69593147751606, 0.16507475083056478, 0.0, 0.0, 0.6902061855670103, 0.0, 0.48291335864687607, 0.0, nan, 0.8250462639929618, 0.5118271542396005, 0.9468774059145872, nan, nan, 0.6267822449993868, 0.3619458279612461, nan, nan, nan, nan, nan, 0.3504480113453233, nan, 0.0, nan, 0.3302076356329538, nan, nan, nan, nan, nan, nan, 0.2151166569181777, nan, nan, 0.8895965506621497, nan, nan, nan, nan, nan, 0.3963979716733695, 0.1233782752480285, nan, nan, nan, 0.07798291540369248, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.008562766306129139, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7336859089279312, 0.0, nan, nan, nan, 0.9742286322260314, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.13296011196641008, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.830903905918915, 0.8350242343757374, 0.9531571263858871, 0.7052494294098468, 0.9778743924776429, 0.866153390583358, 0.9528361101628657, 0.9811238178600651, 0.9917636526410026, 0.9672915040927128, nan, 0.7826610475617098, 0.24774072919912746, nan, nan, 0.7455456570155902, nan, 0.5785773366418527, nan, nan, 0.8954005201988608, 0.5118294287034733, 0.9578961836257845, nan, nan, 0.8795058508133174, 0.9440196461461182, nan, nan, nan, nan, nan, 0.35168353980010997, nan, 0.0, nan, 0.33221024258760107, nan, nan, nan, nan, nan, nan, 0.22503229315385137, nan, nan, 0.9068794896267645, nan, nan, nan, nan, nan, 0.8175261449693473, 0.1233782752480285, nan, nan, nan, 0.07798291540369248, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.008562766306129139, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7379192210602236, 0.0, nan, nan, nan, 0.9742286322260314, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.13296011196641008, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0545 | 22.0 | 440 | 0.5040 | 0.3608 | 0.5181 | 0.8163 | [0.7232911433407024, 0.7690588095172654, 0.8900659365956327, 0.11912580072132206, 0.9191096782668666, 0.0635469122015971, 0.9414236549395303, 0.8360351260203001, 0.9050424818516227, 0.9375789371417468, nan, 0.692696151392728, 0.15131717597471023, 0.0, 0.0, 0.6046583850931677, 0.0, 0.4809134287661895, 0.0, nan, 0.8285566979043459, 0.4226720287955207, 0.9424728495478711, nan, nan, 0.6243991104837743, 0.32963119924788226, nan, nan, nan, nan, nan, 0.21801423027166883, nan, 0.0, nan, 0.32920294708640324, nan, nan, nan, nan, nan, nan, 0.23794732414417913, nan, nan, 0.9204450041981528, nan, nan, nan, nan, nan, 0.44425973009974573, 0.21750190791147292, nan, nan, nan, 0.2343161568843575, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.010521359998362434, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7325560538116592, 0.0, nan, nan, nan, 0.9740972796889572, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.09587123862841147, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7885868413617743, 0.848796370573603, 0.9492246923538927, 0.7215520052168243, 0.9779734015136734, 0.8931003849570625, 0.960913325478191, 0.9764428291439119, 0.9955953446732319, 0.9613807302130203, nan, 0.7845273931366646, 0.2237457151760673, nan, nan, 0.6504454342984409, nan, 0.5835401157981803, nan, nan, 0.899483093537023, 0.42267766362117387, 0.9502451092683374, nan, nan, 0.8493732565300873, 0.944201038120221, nan, nan, nan, nan, nan, 0.21803538506323383, nan, 0.0, nan, 0.33119946091644203, nan, nan, nan, nan, nan, nan, 0.2499830036032361, nan, nan, 0.9361656703672075, nan, nan, nan, nan, nan, 0.8191489361702128, 0.21750190791147292, nan, nan, nan, 0.2343161568843575, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.010529334644378892, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7363865849260729, 0.0, nan, nan, nan, 0.9740972796889572, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.09587123862841147, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1905 | 23.0 | 460 | 0.4807 | 0.3724 | 0.5352 | 0.8260 | [0.7278312873264919, 0.7678051974371366, 0.8910359987204829, 0.14236324095243405, 0.9219989372627081, 0.06286180631120783, 0.9410935050422311, 0.8664029214850882, 0.8960056648105829, 0.9392036257688572, nan, 0.7172094956106176, 0.1757188498402556, 0.0, 0.0, 0.6209367535569633, 0.0, 0.5044728434504793, 0.0, nan, 0.8347490347490347, 0.48493816627340147, 0.9454528911332515, nan, nan, 0.6319681893272888, 0.36002510998920056, nan, nan, nan, nan, nan, 0.299640253414409, nan, 0.0, nan, 0.39645958583834334, nan, nan, nan, nan, nan, nan, 0.2418979987088444, nan, nan, 0.9534181318141637, nan, nan, nan, nan, nan, 0.3949609035621199, 0.13228186212159757, nan, nan, nan, 0.434755276156264, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.006616005407509064, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.7548184670551322, 0.0, nan, nan, nan, 0.9723896967069919, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.10986703988803359, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7849228351462071, 0.8300425365537977, 0.9525170646593863, 0.8324095207042713, 0.9751865883524605, 0.8553449807521468, 0.9551926528724494, 0.9751298688942591, 0.9968845120859445, 0.9633897292002191, nan, 0.8312462372065021, 0.2228108444998442, nan, nan, 0.6658129175946548, nan, 0.6530190239867659, nan, nan, 0.9253613406644059, 0.4849532510931779, 0.9521235167453155, nan, nan, 0.8938974024562547, 0.9442847574928839, nan, nan, nan, nan, nan, 0.3044279846039396, nan, 0.0, nan, 0.3999326145552561, nan, nan, nan, nan, nan, nan, 0.2547419946971242, nan, nan, 0.9752097252222837, nan, nan, nan, nan, nan, 0.8196898665705012, 0.13228186212159757, nan, nan, nan, 0.4446587673371911, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.006616683054736152, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7591056617381897, 0.0, nan, nan, nan, 0.9723896967069919, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.10986703988803359, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.2233 | 24.0 | 480 | 0.5327 | 0.3732 | 0.5320 | 0.8150 | [0.7304387937496396, 0.7671660907254384, 0.8900531080959065, 0.13089345532723365, 0.9200685409240942, 0.0639594661917936, 0.9317053812658668, 0.8385454782000618, 0.9080253362935876, 0.9391328246249353, nan, 0.7145314713620724, 0.2300458162527128, 0.0, 0.0, 0.6243628419848122, 0.0, 0.48096253426743835, 0.0, nan, 0.8382110469909316, 0.4168462683582554, 0.9436991222884169, nan, nan, 0.6313515710530284, 0.32885029389709974, nan, nan, nan, nan, nan, 0.27737531794326925, nan, 0.0, nan, 0.43829219479653103, nan, nan, nan, nan, nan, nan, 0.23342261710328607, nan, nan, 0.9124139714120385, nan, nan, nan, nan, nan, 0.33922926388679814, 0.3039938946832867, nan, nan, nan, 0.22975206611570248, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 2.0485086856768273e-05, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6817692445720438, 0.0, nan, nan, nan, 0.9702880561138039, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.07907627711686493, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.782914253425625, 0.8358264292888778, 0.9507327829971548, 0.8536028692533421, 0.9756874575935562, 0.8933965057743559, 0.9436632126064023, 0.9811618746788956, 0.9959176365264101, 0.9645021501270152, nan, 0.850210716435882, 0.29728887503895296, nan, nan, 0.6683741648106905, nan, 0.6530190239867659, nan, nan, 0.9373127448720904, 0.41685182551814853, 0.950703257433454, nan, nan, 0.8722512045792125, 0.9484707261260256, nan, nan, nan, nan, nan, 0.27864928680014234, nan, 0.0, nan, 0.44272237196765496, nan, nan, nan, nan, nan, nan, 0.2429125025494595, nan, nan, 0.9306902094740543, nan, nan, nan, nan, nan, 0.8126577713667508, 0.3039938946832867, nan, nan, nan, 0.22981537613667677, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 2.0485086856768273e-05, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6850883519653804, 0.0, nan, nan, nan, 0.9702880561138039, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.07907627711686493, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1711 | 25.0 | 500 | 0.5298 | 0.3593 | 0.5328 | 0.8233 | [0.7717828837162787, 0.7668056952936889, 0.8858525940371548, 0.1333056046584174, 0.9184760882541386, 0.06771366791851599, 0.9373192945911786, 0.8389624688319679, 0.9213019915550088, 0.9430745863495347, nan, 0.7104245138959585, 0.21391925050189606, 0.0, 0.0, 0.6666320058230217, 0.0, 0.4864623623921452, 0.0, nan, 0.8338013023478105, 0.4565378718865954, 0.9413290851160674, nan, nan, 0.6328667467766556, 0.33555309845705383, nan, nan, nan, nan, nan, 0.3767682613168724, nan, 0.0, nan, 0.4220367278797997, nan, nan, nan, nan, nan, nan, 0.2552573861437234, nan, nan, 0.8935682127654354, nan, nan, nan, nan, 0.0, 0.36667207002755714, 0.14678198931569575, nan, nan, nan, 0.21886480529022778, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6621209402476225, 0.0, nan, nan, nan, 0.9663212094941613, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.11686494051784464, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8176207797711541, 0.8297425561030854, 0.9538848678009496, 0.8359960873818063, 0.9768231494774361, 0.8809594314480308, 0.9513825417625618, 0.9859760622609556, 0.992300805729633, 0.9690514536186887, nan, 0.840276941601445, 0.29884699283265814, nan, nan, 0.7139198218262807, nan, 0.6761786600496278, nan, nan, 0.9190399367859612, 0.4565439581926126, 0.9474962202776378, nan, nan, 0.8824493714451328, 0.9315873193056874, nan, nan, nan, nan, nan, 0.3790471261765372, nan, 0.0, nan, 0.42587601078167114, nan, nan, nan, nan, nan, nan, 0.2690189679787885, nan, nan, 0.914766162656352, nan, nan, nan, nan, nan, 0.8157230436350523, 0.14678198931569575, nan, nan, nan, 0.218884908606595, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6653443923548503, 0.0, nan, nan, nan, 0.9663212094941613, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.11686494051784464, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1064 | 26.0 | 520 | 0.5547 | 0.3501 | 0.5263 | 0.8080 | [0.7470273583796584, 0.7596318506288247, 0.8884138203746725, 0.12933705867411735, 0.9229628402170761, 0.06402471055451661, 0.958222732640657, 0.8332906242993049, 0.9030285286750593, 0.9394159640792067, nan, 0.7054126918291166, 0.18507268387936646, 0.0, 0.0, 0.6355110972178806, 0.0, 0.49351491569390404, 0.0, nan, 0.836078431372549, 0.335373006985549, 0.9512856432735325, nan, nan, 0.6171494711121114, 0.29505659495473724, nan, nan, 0.0, nan, nan, 0.39546615787601463, nan, 0.0, nan, 0.4333889816360601, nan, nan, nan, nan, nan, nan, 0.2178094739596964, nan, nan, 0.8946256112844961, nan, nan, nan, nan, 0.0, 0.5360605344052968, 0.16128211650979393, nan, nan, nan, 0.1855423900064297, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6936120581374484, 0.0, nan, nan, nan, 0.9718511513049874, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.11546536039188243, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7854260841926826, 0.8342928213666975, 0.9455465294187275, 0.8301271600912944, 0.9731743752966631, 0.9022801302931596, 0.9739238638490931, 0.9900291134664053, 0.9940913160250672, 0.9656975875408856, nan, 0.8191450933172787, 0.2658148956061078, nan, nan, 0.6791759465478842, nan, 0.62944582299421, nan, nan, 0.9125209890363152, 0.335378968324505, 0.964447702386952, nan, nan, 0.8692533420280404, 0.9336803036222582, nan, nan, nan, nan, nan, 0.3955429051977876, nan, 0.0, nan, 0.43733153638814015, nan, nan, nan, nan, nan, nan, 0.22632401930790672, nan, nan, 0.9143768523634902, nan, nan, nan, nan, nan, 0.8175261449693473, 0.16128211650979393, nan, nan, nan, 0.1855423900064297, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6969888207717274, 0.0, nan, nan, nan, 0.9718511513049874, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.11546536039188243, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1263 | 27.0 | 540 | 0.4958 | 0.3715 | 0.5212 | 0.8227 | [0.7622122950445686, 0.7522242153546104, 0.8918025796823367, 0.13244768800957865, 0.9252054809725452, 0.06880239312671745, 0.9440097666847531, 0.839881597463727, 0.8936026502846482, 0.9376979254184709, nan, 0.710828953530728, 0.14292967758314293, 0.0, 0.0, 0.6028598267404237, 0.0, 0.45043536503683856, 0.0, nan, 0.8413558084342482, 0.4568379049709616, 0.9470273699474352, nan, nan, 0.5979166935078656, 0.33917399941780596, nan, nan, nan, nan, nan, 0.41904088908874826, nan, 0.0, nan, 0.41382765531062127, nan, nan, nan, nan, nan, nan, 0.21008512573916432, nan, nan, 0.9313955191096793, nan, nan, nan, nan, nan, 0.41842984409799555, 0.15161536504706183, nan, nan, nan, 0.23878954607977992, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.002622091117666339, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6507267181051498, 0.0, nan, nan, nan, 0.9673457592833405, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0741777466759972, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.8112065616612516, 0.8423990346696507, 0.9535867568598396, 0.7574176719921748, 0.9709291998031467, 0.8785904649096832, 0.9579291055048397, 0.9880311304778033, 0.9949149507609669, 0.9635889687691975, nan, 0.8223961468994582, 0.17544406357120598, nan, nan, 0.643207126948775, nan, 0.5562448304383788, nan, nan, 0.910413854410167, 0.45686835650040886, 0.9574838502771796, nan, nan, 0.840642321486795, 0.9266897360049116, nan, nan, nan, nan, nan, 0.4219684962965359, nan, 0.0, nan, 0.41745283018867924, nan, nan, nan, nan, nan, nan, 0.21979740295057448, nan, nan, 0.9585321746119455, nan, nan, nan, nan, nan, 0.8130183916336098, 0.15161536504706183, nan, nan, nan, 0.23918434830531826, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.002622091117666339, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6538946988820772, 0.0, nan, nan, nan, 0.9673457592833405, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0741777466759972, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1338 | 28.0 | 560 | 0.5189 | 0.3552 | 0.5213 | 0.8060 | [0.7641336800539343, 0.7669235627004126, 0.8928123290094582, 0.12575472631891518, 0.9216441115446635, 0.06773655919912584, 0.9097365555573613, 0.8380640347747105, 0.9187775239230489, 0.9439952226472345, nan, 0.7035067988958185, 0.2130331240528253, 0.0, 0.0, 0.5849253575529805, 0.0, 0.47883258499037845, 0.0, nan, 0.8306187929717341, 0.2920097583952861, 0.9483015966348546, nan, nan, 0.625896326041042, 0.29212208836732134, nan, nan, nan, nan, nan, 0.3975157283432812, nan, 0.0, nan, 0.38842422214787553, nan, nan, nan, nan, nan, nan, 0.22568771793878342, nan, nan, 0.9575582533777168, nan, nan, nan, nan, nan, 0.2686851829777089, 0.06614093106079878, nan, nan, nan, 0.23198095063650517, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0010857096034087184, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6822178359949758, 0.0, nan, nan, nan, 0.9685673378781311, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.03219034289713086, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8055604605170221, 0.8432214529839629, 0.9513903806613679, 0.8284969025105967, 0.9753292778455633, 0.9086467278649689, 0.9246181629978747, 0.9831979144863281, 0.993661593554163, 0.9711102624981321, nan, 0.8285370258880193, 0.30663758180118417, nan, nan, 0.6239420935412027, nan, 0.6174524400330852, nan, nan, 0.8949395844993909, 0.2920162465782644, 0.9605534429834609, nan, nan, 0.8696065645038583, 0.9416196907964502, nan, nan, nan, nan, nan, 0.3985186143545622, nan, 0.0, nan, 0.3911725067385445, nan, nan, nan, nan, nan, nan, 0.23760962675912706, nan, nan, 0.9826191791832019, nan, nan, nan, nan, nan, 0.8128380815001803, 0.06614093106079878, nan, nan, nan, 0.23266280885459723, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0010857096034087184, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6855391272989542, 0.0, nan, nan, nan, 0.9685673378781311, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.03219034289713086, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.121 | 29.0 | 580 | 0.5205 | 0.3730 | 0.5243 | 0.8166 | [0.7393700064285058, 0.7501469335860191, 0.8925518016900663, 0.12732170707500354, 0.9210242099953948, 0.0680527914528124, 0.9087242371158546, 0.8360235965253469, 0.9161887812273416, 0.9430380769292903, nan, 0.6958363467300341, 0.23164088036609284, 0.0, 0.0, 0.5418580375782881, 0.0, 0.4536802030456853, 0.0, nan, 0.8343651834456678, 0.46940253738307375, 0.9490845133940651, nan, nan, 0.592327031704052, 0.3505386594503726, nan, nan, nan, nan, nan, 0.32457871074166317, nan, 0.0, nan, 0.4567323755429335, nan, nan, nan, nan, nan, nan, 0.20620378247496893, nan, nan, 0.916198971690516, nan, nan, nan, nan, nan, 0.27076610493605835, 0.10429916051895192, nan, nan, nan, 0.21484339120051438, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0015158964274008522, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6853015075376885, 0.0, nan, nan, nan, 0.9670173779406549, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.044086773967809655, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.7818989264020342, 0.8431877473153435, 0.946475934117482, 0.8783827844799479, 0.9784335023281684, 0.9619484749777909, 0.9254055125480394, 0.9815995280954465, 0.9954879140555059, 0.9692174865928374, nan, 0.810957254665864, 0.3312558429417264, nan, nan, 0.5780623608017817, nan, 0.5913978494623656, nan, nan, 0.9007342047212985, 0.4694087951935725, 0.964172813487882, nan, nan, 0.821523022859834, 0.9543310822124239, nan, nan, nan, nan, nan, 0.32457871074166317, nan, 0.0, nan, 0.46057951482479786, nan, nan, nan, nan, nan, nan, 0.21422258481201986, nan, nan, 0.9287059828201135, nan, nan, nan, nan, nan, 0.8169852145690588, 0.10429916051895192, nan, nan, nan, 0.21484339120051438, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0015158964274008522, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6885142445005409, 0.0, nan, nan, nan, 0.9670173779406549, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.044086773967809655, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1153 | 30.0 | 600 | 0.4982 | 0.3552 | 0.5072 | 0.8008 | [0.7667386072252623, 0.7327063027086672, 0.8846346406970453, 0.1478246355847335, 0.9220984554975982, 0.07594007793916696, 0.9382995180579521, 0.8147790885227236, 0.9259321127889883, 0.9418627197518097, nan, 0.6946953781512605, 0.14309327036599764, 0.0, 0.0, 0.5459391658827218, 0.0, 0.47314323607427056, 0.0, nan, 0.8329925849073876, 0.2911839596859127, 0.9473065798987708, nan, nan, 0.5423992673992674, 0.29088924342933914, nan, nan, nan, nan, nan, 0.3970201014706827, nan, 0.0, nan, 0.3996655518394649, nan, nan, nan, nan, nan, nan, 0.19646041075084197, nan, nan, 0.8822862694300518, nan, nan, nan, nan, nan, 0.40010669511869834, 0.07758839989824472, nan, nan, nan, 0.07081840727473133, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.006985414618157981, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.624102656137832, 0.0, nan, nan, nan, 0.969027071757891, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.04758572428271519, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8170866294674389, 0.8460156529125068, 0.960535372178358, 0.8663188783827844, 0.9765377704912305, 0.8886585726976607, 0.955115569699706, 0.9804387951211159, 0.9924798567591764, 0.9678062063125736, nan, 0.7963275135460566, 0.18884387659707075, nan, nan, 0.5816258351893095, nan, 0.5901571546732837, nan, nan, 0.8987587660092846, 0.29118525365281384, 0.9603701837174142, nan, nan, 0.7510234394812159, 0.9667355025953005, nan, nan, nan, nan, nan, 0.39990943493870684, nan, 0.0, nan, 0.4026280323450135, nan, nan, nan, nan, nan, nan, 0.20225712149024408, nan, nan, 0.8895991359823178, nan, nan, nan, nan, nan, 0.8113956004327443, 0.07758839989824472, nan, nan, nan, 0.07081840727473133, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.006985414618157981, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6270284890010819, 0.0, nan, nan, nan, 0.969027071757891, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.04758572428271519, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1334 | 31.0 | 620 | 0.4598 | 0.3664 | 0.5266 | 0.8349 | [0.7600503091919086, 0.7460795921326052, 0.8719402654341993, 0.15596769368830393, 0.9146058394957891, 0.08001224609323004, 0.9351090192335848, 0.8347202249188063, 0.9095788199103315, 0.9425698071108668, nan, 0.7024003361521088, 0.1247807567025808, 0.0, 0.0, 0.5938413361169103, 0.0, 0.4739010989010989, 0.0, nan, 0.8385410334346505, 0.569279035870454, 0.9480098943107713, nan, nan, 0.597444659798021, 0.40289379386884067, nan, nan, nan, nan, nan, 0.48777838131450296, nan, 0.0, nan, 0.47149049683227745, nan, nan, nan, nan, nan, nan, 0.19018323741483098, nan, nan, 0.9425041694978071, nan, nan, nan, nan, nan, 0.3835511797657444, 0.10022894937674892, nan, nan, nan, 0.2044640396803527, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.004527204195345788, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6230258435032304, 0.0, nan, nan, nan, 0.9696969696969697, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0741777466759972, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8003028323209492, 0.8325906851014203, 0.9660328886511795, 0.850016302575807, 0.9753118056627345, 0.8899911163754812, 0.949499509971259, 0.9830076303921755, 0.9953088630259624, 0.9679224293944777, nan, 0.8051173991571342, 0.15518853225303833, nan, nan, 0.6335189309576837, nan, 0.5707196029776674, nan, nan, 0.9083067197840187, 0.569279035870454, 0.9657305172492784, nan, nan, 0.8487120965112488, 0.9522939108109617, nan, nan, nan, nan, nan, 0.49377365203609663, nan, 0.0, nan, 0.47641509433962265, nan, nan, nan, nan, nan, nan, 0.19545856278468965, nan, nan, 0.9580926307329081, nan, nan, nan, nan, nan, 0.8148214929679048, 0.10022894937674892, nan, nan, nan, 0.2044640396803527, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.004527204195345788, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6259466282005048, 0.0, nan, nan, nan, 0.9696969696969697, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0741777466759972, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0582 | 32.0 | 640 | 0.4668 | 0.3797 | 0.5332 | 0.8510 | [0.7672225402489522, 0.756542566524216, 0.8918801669804372, 0.12513134014710098, 0.9212230186158148, 0.07101034301408286, 0.9319699499165276, 0.8339364761381628, 0.9294062173722677, 0.9406139257916795, nan, 0.7105563628814615, 0.17794928335170893, 0.0, 0.0, 0.5558107967004281, 0.0, 0.5188163396590544, nan, nan, 0.8339010888740191, 0.7231027484556868, 0.9488651776833349, nan, nan, 0.6194157056425178, 0.5115702416861331, nan, nan, nan, nan, nan, 0.37192722111103943, nan, 0.0, nan, 0.4285237140948564, nan, nan, nan, nan, nan, nan, 0.20902878170587463, nan, nan, 0.9574062301335029, nan, nan, nan, nan, nan, 0.3562329034779211, 0.04477232256423302, nan, nan, nan, 0.20391292367043262, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.71873317782164, 0.0, nan, nan, nan, 0.9690533422653058, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.06018194541637509, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8105576352592174, 0.8240328158389678, 0.9553666545376431, 0.8542549722856211, 0.9757340500811, 0.8996150429375185, 0.9466969860479457, 0.9822274656061499, 0.9881826320501342, 0.9651330754287801, nan, 0.824262492474413, 0.2514802119040199, nan, nan, 0.5927616926503341, nan, 0.6670802315963607, nan, nan, 0.9026767194547789, 0.7235815350705678, 0.9615155541302057, nan, nan, 0.8984983516284462, 0.9442149913489981, nan, nan, nan, nan, nan, 0.3729016398745027, nan, 0.0, nan, 0.43227762803234504, nan, nan, nan, nan, nan, nan, 0.21626215242368618, nan, nan, 0.983473150148189, nan, nan, nan, nan, nan, 0.8218535881716552, 0.04477232256423302, nan, nan, nan, 0.20391292367043262, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7222322394518572, 0.0, nan, nan, nan, 0.9690533422653058, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.06018194541637509, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0811 | 33.0 | 660 | 0.4840 | 0.3762 | 0.5301 | 0.8580 | [0.7627208167071746, 0.7549441259772555, 0.895652823734063, 0.13903054076772206, 0.9204709940919741, 0.06561774230479867, 0.9537214180030675, 0.8317635125344153, 0.9277675121787334, 0.9368543764774456, nan, 0.7048375300386585, 0.13202185792349727, 0.0, 0.0, 0.5625980545967995, 0.0, 0.5168431183830606, nan, nan, 0.838258117525334, 0.7828351911888796, 0.9454438352440074, nan, nan, 0.6192107092096448, 0.5656764723371361, nan, nan, 0.0, nan, nan, 0.38528964647281433, nan, 0.0, nan, 0.4020066889632107, nan, nan, nan, nan, nan, nan, 0.21716514954486346, nan, nan, 0.955712328437853, nan, nan, nan, nan, nan, 0.5480396902226524, 0.058000508776392776, nan, nan, nan, 0.19041058142739048, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6510229720028715, 0.0, nan, nan, nan, 0.9689613954893539, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.07067879636109167, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8159565263455291, 0.8247574877142838, 0.9457788805934161, 0.8089338115422237, 0.9758971237875033, 0.8910275392360083, 0.9689024457389523, 0.9830076303921755, 0.988898836168308, 0.9606999950190108, nan, 0.8122817579771222, 0.18822062947958865, nan, nan, 0.5989977728285077, nan, 0.6662531017369727, nan, nan, 0.9069239126856089, 0.7833152618294287, 0.9559261465157832, nan, nan, 0.8956997427815817, 0.9291734107272422, nan, nan, nan, nan, nan, 0.38528964647281433, nan, 0.0, nan, 0.4049865229110512, nan, nan, nan, nan, nan, nan, 0.22707186076551772, nan, nan, 0.9986562515697995, nan, nan, nan, nan, nan, 0.8166245943021998, 0.058000508776392776, nan, nan, nan, 0.19041058142739048, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6540750090155066, 0.0, nan, nan, nan, 0.9689613954893539, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.07067879636109167, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1979 | 34.0 | 680 | 0.5273 | 0.3690 | 0.5275 | 0.8497 | [0.7361887307275058, 0.7553466913704463, 0.8934074386522095, 0.11533178813632071, 0.9225648929177093, 0.06224019887521395, 0.9488118586583159, 0.8314142880180616, 0.9208451172849775, 0.9383787452729565, nan, 0.7049095339312561, 0.14388311045071817, 0.0, 0.0, 0.5418930213121604, 0.0, 0.5, 0.0, nan, 0.8429259135910866, 0.7649717614463822, 0.949005424954792, nan, nan, 0.6159888154550076, 0.5454056570456224, nan, nan, nan, nan, nan, 0.18012743797910535, nan, 0.0, nan, 0.4447412353923205, nan, nan, nan, nan, nan, nan, 0.22157168878480354, nan, nan, 0.9460020896072767, nan, nan, nan, nan, nan, 0.4691243287897563, 0.13253625031798524, nan, nan, nan, 0.1894002020758703, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6284996410624551, 0.0, nan, nan, nan, 0.9677266816408558, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.06088173547935619, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7908647054668738, 0.829968384082835, 0.9471247638128389, 0.876100423866971, 0.9726822421469818, 0.9045010364228605, 0.964233407847067, 0.9810286758129888, 0.9910832587287377, 0.9640704643942287, nan, 0.8186032510535822, 0.18105328762854472, nan, nan, 0.5776169265033407, nan, 0.674937965260546, nan, nan, 0.9166364863530109, 0.7656244445234456, 0.961744628212764, nan, nan, 0.8779118211788574, 0.9481358486353743, nan, nan, nan, nan, nan, 0.18012743797910535, nan, 0.0, nan, 0.44878706199460916, nan, nan, nan, nan, nan, nan, 0.23155890951118363, nan, nan, 0.9665193148138845, nan, nan, nan, nan, nan, 0.8191489361702128, 0.13253625031798524, nan, nan, nan, 0.1894002020758703, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6314460872701045, 0.0, nan, nan, nan, 0.9677266816408558, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.06088173547935619, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.112 | 35.0 | 700 | 0.4803 | 0.3779 | 0.5268 | 0.8542 | [0.7485612827037905, 0.7485955783172517, 0.8970574231025616, 0.11843378794544655, 0.9209662185907573, 0.06654696071735443, 0.9500829241325486, 0.8326367934423056, 0.9140421890940205, 0.9409130589414465, nan, 0.7031992067219873, 0.1374733349134866, 0.0, 0.0, 0.5441161115171765, 0.0, 0.5035658914728682, nan, nan, 0.8403203300370078, 0.8157481014344717, 0.9504539090375321, nan, nan, 0.6060380359656377, 0.5850991171081126, nan, nan, nan, nan, nan, 0.27114532457871077, nan, 0.0, nan, 0.4269963247577681, nan, nan, nan, nan, nan, nan, 0.2132534826194506, nan, nan, 0.8645085285307866, nan, nan, nan, nan, nan, 0.48516129032258065, 0.10735181887560417, nan, nan, nan, 0.19270689813539083, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6325139063341109, 0.0, nan, nan, nan, 0.9681470097594935, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.018894331700489854, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7947052902952394, 0.8286841981084379, 0.9498340661893969, 0.8777306814476687, 0.9768056772946072, 0.894432928634883, 0.9651694178018082, 0.9818849542366753, 0.9946284691136974, 0.9663617194374803, nan, 0.811198073449729, 0.1807416640698037, nan, nan, 0.5802895322939866, nan, 0.6716294458229942, nan, nan, 0.9120600533368453, 0.8162483557893988, 0.9641269986713703, nan, nan, 0.8695975075172988, 0.9801724619076855, nan, nan, nan, nan, nan, 0.27114532457871077, nan, 0.0, nan, 0.4305929919137466, nan, nan, nan, nan, nan, nan, 0.22272078319396288, nan, nan, 0.8662781936002412, nan, nan, nan, nan, nan, 0.8135593220338984, 0.10735181887560417, nan, nan, nan, 0.19270689813539083, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.635593220338983, 0.0, nan, nan, nan, 0.9681470097594935, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.018894331700489854, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1137 | 36.0 | 720 | 0.5998 | 0.3441 | 0.5032 | 0.7896 | [0.736777443081168, 0.7505535302591466, 0.8984463748747077, 0.11536311648105947, 0.9227343906956519, 0.06314684580665476, 0.9513026790844925, 0.8316456924719355, 0.9196712619300106, 0.941566635390041, nan, 0.7071084149945559, 0.1096790497851908, 0.0, 0.0, 0.5237945821566782, 0.0, 0.4988647421342848, 0.0, nan, 0.845520801700577, 0.21572391604786853, 0.9476931452161684, nan, nan, 0.605464044331165, 0.27296944109368715, nan, nan, nan, nan, nan, 0.13390691205485655, nan, 0.0, nan, 0.4195056780227121, nan, nan, nan, nan, nan, nan, 0.21503837647977103, nan, nan, 0.8408783614602634, nan, nan, nan, nan, nan, 0.48806194880619486, 0.07453574154159247, nan, nan, nan, 0.19656471020483146, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6148600143575018, 0.0, nan, nan, nan, 0.9650864956456634, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.02099370188943317, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7968507204407402, 0.8306492385889459, 0.9431002661078548, 0.8747962178024128, 0.9763514005410553, 0.8907314184187148, 0.9661990287520235, 0.9783266416760223, 0.9938048343777977, 0.9663451161400655, nan, 0.8210716435881998, 0.13524462449361171, nan, nan, 0.5576837416481069, nan, 0.6360628618693135, nan, nan, 0.9167023343100781, 0.2157248746844893, 0.9570715169285747, nan, nan, 0.8648969314929537, 0.985139811352347, nan, nan, nan, nan, nan, 0.13390691205485655, nan, 0.0, nan, 0.42318059299191374, nan, nan, nan, nan, nan, nan, 0.2247603508056292, nan, nan, 0.8434972622695535, nan, nan, nan, nan, nan, 0.8182473855030653, 0.07453574154159247, nan, nan, nan, 0.19656471020483146, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6177425171294627, 0.0, nan, nan, nan, 0.9650864956456634, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.02099370188943317, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0789 | 37.0 | 740 | 0.5828 | 0.3551 | 0.5097 | 0.8256 | [0.723218907726155, 0.7585395161017704, 0.8937781853354605, 0.11309386929126615, 0.92243925941221, 0.05858434175499498, 0.9553108948657376, 0.8234586128317875, 0.9270341734444556, 0.9392806267806267, nan, 0.7031737773152965, 0.11183851609383524, 0.0, 0.0, 0.5486744210416011, 0.0, 0.527479674796748, 0.0, nan, 0.8464739111948004, 0.5722068423087349, 0.9432926552647098, nan, nan, 0.6260963724959394, 0.39379720501108045, nan, nan, nan, nan, nan, 0.1366562085583983, nan, 0.0, nan, 0.42499164717674576, nan, nan, nan, nan, nan, nan, 0.219196776709124, nan, nan, 0.8514094243968062, nan, nan, nan, nan, nan, 0.5356593731519811, 0.058000508776392776, nan, nan, nan, 0.15321025075778452, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.557619816908993, 0.0, nan, nan, nan, 0.965414876988349, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.04548635409377187, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7894211753072468, 0.8319502773976527, 0.9357746281285209, 0.8017606781871536, 0.9756845455630848, 0.889546935149541, 0.9707854775302551, 0.9764047723250814, 0.99183527305282, 0.9634063324976341, nan, 0.8136664659843468, 0.12776565908382673, nan, nan, 0.5830734966592428, nan, 0.6708023159636063, nan, nan, 0.9176242057090179, 0.572229727327669, 0.9526274797269437, nan, nan, 0.8902474368728037, 0.9719540101579506, nan, nan, nan, nan, nan, 0.1366562085583983, nan, 0.0, nan, 0.42857142857142855, nan, nan, nan, nan, nan, nan, 0.22931538513835067, nan, nan, 0.8583915205706535, nan, nan, nan, nan, nan, 0.8166245943021998, 0.058000508776392776, nan, nan, nan, 0.15321025075778452, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5601334294987378, 0.0, nan, nan, nan, 0.965414876988349, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.04548635409377187, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0993 | 38.0 | 760 | 0.5783 | 0.3647 | 0.5163 | 0.8217 | [0.744229631090945, 0.7515311812492002, 0.893370607028754, 0.11697511732931905, 0.922223077853929, 0.061180108733602676, 0.9493174951637794, 0.8273624007968383, 0.9331375725664911, 0.9382785956964892, nan, 0.7073576855031741, 0.16401715871814282, 0.0, 0.0, 0.5784977601833524, 0.0, 0.4937926753569212, nan, nan, 0.8459956448100653, 0.536201357134287, 0.9458333333333333, nan, nan, 0.6152431678397058, 0.37234376412891423, nan, nan, nan, nan, nan, 0.197399488954297, nan, 0.0, nan, 0.4300500834724541, nan, nan, nan, nan, nan, nan, 0.21321891785667496, nan, nan, 0.819296114985414, nan, nan, nan, nan, nan, 0.47766104213095617, 0.1480539302976342, nan, nan, nan, 0.16459998162946635, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.5617483396158679, 0.0, nan, nan, nan, 0.9657432583310347, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.03778866340097971, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7993713801384377, 0.8313031285601612, 0.931662450734975, 0.8614281056406913, 0.9764737058208577, 0.908054486230382, 0.9618933829602141, 0.9799630848857344, 0.9900447627573858, 0.9629082335751881, nan, 0.8184226369656833, 0.20255531318167652, nan, nan, 0.6183741648106904, nan, 0.6579818031430935, nan, nan, 0.9209495275409081, 0.5362037399125458, 0.9567966280295047, nan, nan, 0.8787994058616817, 0.9767399676285092, nan, nan, nan, nan, nan, 0.197399488954297, nan, 0.0, nan, 0.4339622641509434, nan, nan, nan, nan, nan, nan, 0.22129308586579646, nan, nan, 0.8217963530416437, nan, nan, nan, nan, nan, 0.8115759105661738, 0.1480539302976342, nan, nan, nan, 0.16459998162946635, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5642805625676163, 0.0, nan, nan, nan, 0.9657432583310347, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.03778866340097971, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.3402 | 39.0 | 780 | 0.5565 | 0.3831 | 0.5215 | 0.8438 | [0.7618681616782711, 0.7481570099257434, 0.8938716265253422, 0.11751506885476704, 0.9240094373504694, 0.062395446570647516, 0.9583671833533726, 0.8151943909294761, 0.9299090602896598, 0.9397572839067272, nan, 0.7031965874980252, 0.13659793814432988, 0.0, 0.0, 0.5774045482996036, 0.0, 0.4987088444157521, nan, nan, 0.8421036714449888, 0.7222799584299304, 0.9458114709607381, nan, nan, 0.6021759298304799, 0.49065952601838575, nan, nan, nan, nan, nan, 0.18077433127405634, nan, 0.0, nan, 0.43974700399467376, nan, nan, nan, nan, nan, nan, 0.20208990536277602, nan, nan, 0.8614589445561118, nan, nan, nan, nan, nan, 0.5874071177160736, 0.3108623759857543, nan, nan, nan, 0.06797097455681088, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.5313285457809694, 0.0, nan, nan, nan, 0.9685542026244237, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.03778866340097971, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.8182697061731883, 0.8452505342348476, 0.9287207971837284, 0.8708835996087382, 0.9739577114934931, 0.9056855196920344, 0.9742817500082589, 0.9823035792438111, 0.9886839749328559, 0.9655647611615666, nan, 0.8039133052378086, 0.1816765347460268, nan, nan, 0.6163697104677061, nan, 0.6389578163771712, nan, nan, 0.9167681822671452, 0.7226972163958904, 0.9579878132588079, nan, nan, 0.8406694924464732, 0.9741167606184071, nan, nan, nan, nan, nan, 0.18077433127405634, nan, 0.0, nan, 0.4450808625336927, nan, nan, nan, nan, nan, nan, 0.20905568019579848, nan, nan, 0.8665544783242075, nan, nan, nan, nan, nan, 0.8124774612333213, 0.3108623759857543, nan, nan, nan, 0.06797097455681088, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5336278398846015, 0.0, nan, nan, nan, 0.9685542026244237, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.03778866340097971, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0853 | 40.0 | 800 | 0.5029 | 0.3829 | 0.5218 | 0.8501 | [0.766705401440604, 0.7591361517264135, 0.8970119455070129, 0.1257673090649536, 0.9216664515827885, 0.0652215882277232, 0.956992683177808, 0.8262830457242472, 0.928487454233986, 0.9380904620163064, nan, 0.701473750458908, 0.13925570228091236, 0.0, 0.0, 0.5762888749739095, 0.0, 0.5176544766708702, nan, nan, 0.8428947288837814, 0.7494892520874045, 0.94684595253136, nan, nan, 0.6254142794748606, 0.5120569860454254, nan, nan, nan, nan, nan, 0.26108613384222273, nan, 0.0, nan, 0.41569282136894825, nan, nan, nan, nan, nan, nan, 0.215156056558285, nan, nan, 0.8447320702807478, nan, nan, nan, nan, nan, 0.553144807411019, 0.06334266090053421, nan, nan, nan, 0.1431983099109029, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.578711182911506, 0.0, nan, nan, nan, 0.9696312934284326, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.007697690692792162, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.8213598318971607, 0.8375353066878788, 0.9375676777596086, 0.8617541571568308, 0.9774812683639922, 0.9003553449807521, 0.9729107707216086, 0.9831027724392518, 0.9898657117278424, 0.9628086137906988, nan, 0.8052378085490668, 0.1807416640698037, nan, nan, 0.6149220489977728, nan, 0.6790736145574855, nan, nan, 0.9176571296875514, 0.7499155675637242, 0.9613781096806707, nan, nan, 0.8818968952650075, 0.9789585310040744, nan, nan, nan, nan, nan, 0.26108613384222273, nan, 0.0, nan, 0.4194743935309973, nan, nan, nan, nan, nan, nan, 0.22448840845740703, nan, nan, 0.8483071281458784, nan, nan, nan, nan, nan, 0.8182473855030653, 0.06334266090053421, nan, nan, nan, 0.1431983099109029, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.581319870176704, 0.0, nan, nan, nan, 0.9696312934284326, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.007697690692792162, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.159 | 41.0 | 820 | 0.5570 | 0.3686 | 0.5222 | 0.8319 | [0.7522373552874894, 0.7688653153540742, 0.8954077346952489, 0.12210388639760837, 0.9212171314631614, 0.06570608142754589, 0.9486536373507057, 0.8355657526942528, 0.928545033090335, 0.9356612592796261, nan, 0.7038423022189427, 0.1521637971149372, 0.0, 0.0, 0.5656396866840732, 0.0, 0.5118413262285376, nan, nan, 0.8451802531341851, 0.6051439972626967, 0.9474750202538482, nan, nan, 0.639642178386526, 0.40312683390973886, nan, nan, nan, nan, nan, 0.2328168968528641, nan, 0.0, nan, 0.4279038718291055, nan, nan, nan, nan, nan, nan, 0.2237995551485019, nan, nan, 0.7813139235740729, nan, nan, nan, nan, nan, 0.4615930279691933, 0.06436021368608497, nan, nan, nan, 0.20740332506659317, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6137832017229002, 0.0, nan, nan, nan, 0.9738477098685161, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.05108467459762071, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8063020906907755, 0.8433090877223731, 0.9346347921772182, 0.8522986631887838, 0.976744524654706, 0.9486230381995854, 0.962119126537534, 0.9840351645005994, 0.989794091316025, 0.9605173587474473, nan, 0.8039735099337748, 0.2038018074166407, nan, nan, 0.6031180400890869, nan, 0.7150537634408602, nan, nan, 0.9277977150758898, 0.6051628212876391, 0.964447702386952, nan, nan, 0.8839890591602362, 0.9872467488976949, nan, nan, nan, nan, nan, 0.2328168968528641, nan, 0.0, nan, 0.43194070080862534, nan, nan, nan, nan, nan, nan, 0.2325786933170168, nan, nan, 0.7818732104284924, nan, nan, nan, nan, nan, 0.8213126577713667, 0.06436021368608497, nan, nan, nan, 0.20740332506659317, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6166606563288857, 0.0, nan, nan, nan, 0.9738477098685161, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.05108467459762071, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0892 | 42.0 | 840 | 0.5528 | 0.3809 | 0.5345 | 0.8409 | [0.7550132433091331, 0.7684195080221917, 0.891028117441548, 0.12294202695769788, 0.9174699749313205, 0.06435016780883861, 0.9546742363093658, 0.8348650525737729, 0.931799756789623, 0.9379775935759617, nan, 0.7003735602365881, 0.19900497512437812, 0.0, 0.0, 0.5782881002087683, 0.0, 0.5285456730769231, nan, nan, 0.8407328683052421, 0.6782295428599351, 0.9482487298232993, nan, nan, 0.640155314674001, 0.45160668380462726, nan, nan, nan, nan, nan, 0.24976550118058027, nan, 0.0, nan, 0.41750167000668004, nan, nan, nan, nan, nan, nan, 0.2526933746209922, nan, nan, 0.8163042797167742, nan, nan, nan, nan, nan, 0.5415925002966655, 0.2299669295344696, nan, nan, nan, 0.20271883898227244, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6668760093307016, 0.0, nan, nan, nan, 0.9728757010941667, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.04618614415675298, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8066111032631728, 0.8295773983268506, 0.9302508077491308, 0.859471796543854, 0.9783607015663812, 0.9481788569736452, 0.9703119666119743, 0.983559454265218, 0.9878245299910474, 0.9619452423251258, nan, 0.8127031908488862, 0.28669367404175755, nan, nan, 0.6169265033407573, nan, 0.7274607113316791, nan, nan, 0.9246040891581339, 0.6784857264744569, 0.9662344802309066, nan, nan, 0.8959171104590081, 0.9804933861695596, nan, nan, nan, nan, nan, 0.24976550118058027, nan, 0.0, nan, 0.4211590296495957, nan, nan, nan, nan, nan, nan, 0.26629954449656673, nan, nan, 0.8180162756819209, nan, nan, nan, nan, nan, 0.8229354489722323, 0.2299669295344696, nan, nan, nan, 0.20271883898227244, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6701226108907321, 0.0, nan, nan, nan, 0.9728757010941667, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.04618614415675298, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.2616 | 43.0 | 860 | 0.5823 | 0.3767 | 0.5197 | 0.8231 | [0.7426302692908415, 0.7599637848524602, 0.8876966796040002, 0.11714927938648684, 0.9210419232799578, 0.0629228223729942, 0.9544560801268821, 0.8332577499919018, 0.9320981404610037, 0.9367107052652031, nan, 0.7061555131314173, 0.15169471438729557, 0.0, 0.0, 0.5431944009192521, 0.0, 0.5342242503259452, nan, nan, 0.8439002267573696, 0.5388078139997867, 0.9476397178513294, nan, nan, 0.6305658612227113, 0.3739696661128678, nan, nan, nan, nan, nan, 0.17669890351586506, nan, 0.0, nan, 0.41438127090301, nan, nan, nan, nan, nan, nan, 0.22053927009205457, nan, nan, 0.8266629933880986, nan, nan, nan, nan, nan, 0.5370086176366426, 0.16713304502671075, nan, nan, nan, 0.20547441903187288, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.5899667952974962, 0.0, nan, nan, nan, 0.971220659127031, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.04688593421973408, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] | [0.7997024650374347, 0.8317884901882798, 0.9284928299934678, 0.866644929898924, 0.9755127357652671, 0.95158424637252, 0.9675204545704815, 0.9789545791867258, 0.9890420769919427, 0.9605837719371068, nan, 0.8239614689945816, 0.19943907759426613, nan, nan, 0.579064587973274, nan, 0.6778329197684037, nan, nan, 0.9189740888288941, 0.5388078139997867, 0.9601869244513676, nan, nan, 0.8912799333405789, 0.9736423508399844, nan, nan, nan, nan, nan, 0.17669890351586506, nan, 0.0, nan, 0.41745283018867924, nan, nan, nan, nan, nan, nan, 0.22965531307362838, nan, nan, 0.8290299894509469, nan, nan, nan, nan, nan, 0.8202307969707897, 0.16713304502671075, nan, nan, nan, 0.20547441903187288, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5926794085827624, 0.0, nan, nan, nan, 0.971220659127031, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.04688593421973408, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0803 | 44.0 | 880 | 0.5941 | 0.3614 | 0.5122 | 0.8145 | [0.7365145567549886, 0.7578681545824772, 0.8921524446484752, 0.11852693794044102, 0.9205814314057479, 0.06186874895508591, 0.956313948688984, 0.8290704026220237, 0.927895584485304, 0.9377882048377963, nan, 0.7000672147251952, 0.15913461538461537, 0.0, 0.0, 0.5467084639498433, 0.0, 0.5263157894736842, nan, nan, 0.8438817294953631, 0.47381050779219047, 0.9492463442069742, nan, nan, 0.6261944984540847, 0.34422945706209185, nan, nan, nan, nan, nan, 0.17372319435909048, nan, 0.0, nan, 0.3866220735785953, nan, nan, nan, nan, nan, nan, 0.21884994452059264, nan, nan, 0.7817636578082261, nan, nan, nan, nan, nan, 0.5591331269349845, 0.09997456118036123, nan, nan, nan, 0.16671259300082666, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.5955671213208902, 0.0, nan, nan, nan, 0.9690927480264281, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0013995801259622112, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7951423223619155, 0.8304705985452634, 0.9286594214017352, 0.850016302575807, 0.9768988622696948, 0.931448030796565, 0.9695301230027199, 0.9819230110555058, 0.9903312444046554, 0.9623271181656677, nan, 0.8151715833835039, 0.20629479588656902, nan, nan, 0.5826280623608018, nan, 0.6947890818858561, nan, nan, 0.9227603463602542, 0.4738126133172171, 0.9665551839464883, nan, nan, 0.8896496757598812, 0.9855025953005525, nan, nan, nan, nan, nan, 0.17372319435909048, nan, 0.0, nan, 0.3894878706199461, nan, nan, nan, nan, nan, nan, 0.2279556733972398, nan, nan, 0.7824508966695133, nan, nan, nan, nan, nan, 0.8141002524341868, 0.09997456118036123, nan, nan, nan, 0.16671259300082666, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5983591777857915, 0.0, nan, nan, nan, 0.9690927480264281, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0013995801259622112, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1811 | 45.0 | 900 | 0.5887 | 0.3720 | 0.5271 | 0.8145 | [0.7542444627016928, 0.7627722067287723, 0.8906567550692875, 0.12084715720583557, 0.9185889258162186, 0.06365953058533054, 0.9565465173710805, 0.8364198530125942, 0.9256225973591844, 0.9409223999870586, nan, 0.6993454024751969, 0.1993627674101047, 0.0, 0.0, 0.5572710202378469, 0.0, 0.5315043714199578, nan, nan, 0.8408649681909143, 0.4381376870845035, 0.9484062668827661, nan, nan, 0.6295684020448129, 0.3303631338654354, nan, nan, nan, nan, nan, 0.3071449364427338, nan, 0.0, nan, 0.43922743922743923, nan, nan, nan, nan, nan, nan, 0.24880382775119617, nan, nan, 0.7801949640558545, nan, nan, nan, nan, nan, 0.5653153153153153, 0.17934367845331978, nan, nan, nan, 0.21158435836240133, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6932531849991028, 0.0, nan, nan, nan, 0.9710761713362493, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0006997900629811056, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.7983781254414465, 0.823244103193275, 0.93181150620553, 0.8669709814150636, 0.9771143525245848, 0.9505478235119929, 0.970350508198346, 0.9831788860769128, 0.9915846016114592, 0.9657307941357154, nan, 0.8232992173389524, 0.2729822374571518, nan, nan, 0.5948775055679287, nan, 0.729114971050455, nan, nan, 0.9269087676554835, 0.4381376870845035, 0.9651349246346268, nan, nan, 0.8967865811687136, 0.9823910252832505, nan, nan, nan, nan, nan, 0.3071449364427338, nan, 0.0, nan, 0.4444070080862534, nan, nan, nan, nan, nan, nan, 0.2616085389897342, nan, nan, 0.7809690058773295, nan, nan, nan, nan, nan, 0.8146411828344753, 0.17934367845331978, nan, nan, nan, 0.21172040047763388, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6966282005048684, 0.0, nan, nan, nan, 0.9710761713362493, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0006997900629811056, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1941 | 46.0 | 920 | 0.5889 | 0.3673 | 0.5214 | 0.8191 | [0.7483730306999355, 0.7579889930458026, 0.8906972188764645, 0.12141895342898268, 0.9214732070316911, 0.06487727281879871, 0.952691928839257, 0.8345655444647566, 0.926780273862533, 0.9395429329279141, nan, 0.7101980550888448, 0.1662119145065939, 0.0, 0.0, 0.5557758530731504, 0.0, 0.5298879202988792, nan, nan, 0.8438793077410975, 0.500193304981936, 0.9478852303950273, nan, nan, 0.6233292676729176, 0.3525994406776251, nan, nan, nan, nan, nan, 0.20286573729663293, nan, 0.0, nan, 0.44744744744744747, nan, nan, nan, nan, nan, nan, 0.22586073000258866, nan, nan, 0.7885857635622452, nan, nan, nan, nan, nan, 0.5053161723559038, 0.15136097685067412, nan, nan, nan, 0.2035268185157972, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6412419239052405, 0.0, nan, nan, nan, 0.968501661609594, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.005598320503848845, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8046113504732307, 0.8277134748522006, 0.9369144640798236, 0.8526247147049234, 0.9743246273329004, 0.9541012733195143, 0.963870015746991, 0.9816185565048617, 0.9912981199641898, 0.9645021501270152, nan, 0.8397953040337146, 0.22779682143970084, nan, nan, 0.5930957683741648, nan, 0.7038875103391232, nan, nan, 0.9198959602278339, 0.5001955277471648, 0.9641269986713703, nan, nan, 0.8886896351845814, 0.9781352905062232, nan, nan, nan, nan, nan, 0.20286573729663293, nan, 0.0, nan, 0.45181940700808626, nan, nan, nan, nan, nan, nan, 0.23726969882384935, nan, nan, 0.7895463907168333, nan, nan, nan, nan, nan, 0.8141002524341868, 0.15136097685067412, nan, nan, nan, 0.20354551299715257, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.644248106743599, 0.0, nan, nan, nan, 0.968501661609594, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.005598320503848845, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0397 | 47.0 | 940 | 0.5826 | 0.3722 | 0.5259 | 0.8301 | [0.7543159542047607, 0.7611848175957387, 0.8885425605303451, 0.12246522736851964, 0.921481054207353, 0.06353947926217306, 0.9528460413188534, 0.8337871567960063, 0.932409187493676, 0.9398647117843156, nan, 0.7088026912686681, 0.16915308698645856, 0.0, 0.0, 0.5536681950812838, 0.0, 0.5216331505179769, nan, nan, 0.8455520314151941, 0.5822902364352969, 0.9473091138555846, nan, nan, 0.6281395751669628, 0.39834434441287553, nan, nan, nan, nan, nan, 0.24161464566419769, nan, 0.0, nan, 0.437875751503006, nan, nan, nan, nan, nan, nan, 0.2386283214308692, nan, nan, 0.8440447192555118, nan, nan, nan, nan, nan, 0.4958479020979021, 0.12846603917578225, nan, nan, nan, 0.20857431377949143, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6230258435032304, 0.0, nan, nan, nan, 0.970077892054485, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.034289713086074175, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8085667113999152, 0.8313469459293664, 0.9260509506670233, 0.8526247147049234, 0.9769250705439382, 0.9542493337281611, 0.9647289425289888, 0.9788784655490648, 0.9899731423455684, 0.9642863072606219, nan, 0.8372065021071644, 0.2296665627921471, nan, nan, 0.5916481069042316, nan, 0.7080231596360629, nan, nan, 0.9216080071115793, 0.5823394006185787, 0.9628899986255555, nan, nan, 0.8867695540339818, 0.9742283864486242, nan, nan, nan, nan, nan, 0.24161464566419769, nan, 0.0, nan, 0.441711590296496, nan, nan, nan, nan, nan, nan, 0.25215854238901353, nan, nan, 0.8485708544733008, nan, nan, nan, nan, nan, 0.8182473855030653, 0.12846603917578225, nan, nan, nan, 0.2086892624230734, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6259466282005048, 0.0, nan, nan, nan, 0.970077892054485, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.034289713086074175, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.1314 | 48.0 | 960 | 0.5927 | 0.3630 | 0.5141 | 0.8196 | [0.7502280881913731, 0.7590688050299019, 0.8910077057631726, 0.11483274455929172, 0.9221333362696896, 0.06396252720689274, 0.9568111724078016, 0.8306528367021019, 0.9325250751409949, 0.9385486611115605, nan, 0.7058311575282855, 0.14699219733199093, 0.0, 0.0, 0.5370622059592264, 0.0, 0.5270981728089191, nan, nan, 0.8434542586750788, 0.503899428083881, 0.9486080404277399, nan, nan, 0.623828844972353, 0.35694982365245537, nan, nan, nan, nan, nan, 0.1631141443218941, nan, 0.0, nan, 0.39732441471571905, nan, nan, nan, nan, nan, nan, 0.22156659317857605, nan, nan, 0.8027879456450885, nan, nan, nan, nan, nan, 0.5537351754493214, 0.09310607987789367, nan, nan, nan, 0.1848994213281896, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.592964824120603, 0.0, nan, nan, nan, 0.967175000985144, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.00489853044086774, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8094937491171069, 0.8333827683139751, 0.9312021323700258, 0.8584936419954353, 0.9754603192167803, 0.9441812259401836, 0.9702238715574104, 0.9798298860198276, 0.9888272157564906, 0.9631074731441664, nan, 0.8300421432871764, 0.18198815830476783, nan, nan, 0.5720489977728285, nan, 0.7038875103391232, nan, nan, 0.9155170710828696, 0.5039016673184258, 0.9632107023411371, nan, nan, 0.8828478788537478, 0.978623653513423, nan, nan, nan, nan, nan, 0.1631141443218941, nan, 0.0, nan, 0.4002695417789757, nan, nan, nan, nan, nan, nan, 0.2323067509687946, nan, nan, 0.8042397146732305, nan, nan, nan, nan, nan, 0.8166245943021998, 0.09310607987789367, nan, nan, nan, 0.1848994213281896, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.5957446808510638, 0.0, nan, nan, nan, 0.967175000985144, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.00489853044086774, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.0773 | 49.0 | 980 | 0.5931 | 0.3656 | 0.5183 | 0.8231 | [0.7494730094550116, 0.7586128250805959, 0.8910898955811202, 0.11396943485860261, 0.9215884404968075, 0.06435698280799414, 0.9525378930022352, 0.8325239977413891, 0.9279983887751334, 0.9375919786528665, nan, 0.7062682589308595, 0.15715622076707203, 0.0, 0.0, 0.5481613038027581, 0.0, 0.5298669142680285, nan, nan, 0.8440275812230887, 0.5403785222611794, 0.9478307879443169, nan, nan, 0.6218709833416053, 0.37128555706061633, nan, nan, nan, nan, nan, 0.20014878545783873, nan, 0.0, nan, 0.4174465240641711, nan, nan, nan, nan, nan, nan, 0.22923931292780575, nan, nan, 0.8003159438823485, nan, nan, nan, nan, nan, 0.5039475147336817, 0.08038667005850929, nan, nan, nan, 0.19132910811059062, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6223418573351278, 0.0, nan, nan, nan, 0.9677004111334411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0244926522043387, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8051719875688657, 0.8296245862629177, 0.9341788577966971, 0.8291490055428757, 0.9747148394160796, 0.948326917382292, 0.9643435266652718, 0.9819420394649211, 0.9900089525514771, 0.9625927709243056, nan, 0.8296207104154124, 0.20941103147397944, nan, nan, 0.5842984409799554, nan, 0.7080231596360629, nan, nan, 0.9228920422743885, 0.5403809236019766, 0.9638979245888121, nan, nan, 0.885157410426403, 0.9790980632918458, nan, nan, nan, nan, nan, 0.20014878545783873, nan, 0.0, nan, 0.42082210242587603, nan, nan, nan, nan, nan, nan, 0.241348834047182, nan, nan, 0.801652684985181, nan, nan, nan, nan, nan, 0.8171655247024883, 0.08038667005850929, nan, nan, nan, 0.19132910811059062, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6253155427335017, 0.0, nan, nan, nan, 0.9677004111334411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0244926522043387, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
| 0.3419 | 50.0 | 1000 | 0.5845 | 0.3672 | 0.5180 | 0.8290 | [0.7471836875245966, 0.7577637085524198, 0.8895546109310157, 0.11085509472606246, 0.9216125389161993, 0.06317712705545406, 0.9563222390153204, 0.830028328611898, 0.9289316210582935, 0.9387814548102598, nan, 0.7060518731988472, 0.1376830208065759, 0.0, 0.0, 0.5381879054195439, 0.0, 0.527306967984934, nan, nan, 0.8465404640804165, 0.5976121816945779, 0.9477989695381, nan, nan, 0.6212450409224044, 0.4029468326443188, nan, nan, nan, nan, nan, 0.16078532846007051, nan, 0.0, nan, 0.434913217623498, nan, nan, nan, nan, nan, nan, 0.2213077571123064, nan, nan, 0.8266935514170852, nan, nan, nan, nan, nan, 0.5321733037486862, 0.1086237598575426, nan, nan, nan, 0.17764306053090842, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.6124371859296482, 0.0, nan, nan, nan, 0.9673457592833405, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.013296011196641007, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] | [0.8046025215425907, 0.8330558233283674, 0.9275152014660044, 0.847081838930551, 0.9766833720148048, 0.9471424341131182, 0.9685170298752354, 0.9812570167259719, 0.9895076096687556, 0.9631904896312408, nan, 0.8260084286574353, 0.16703022748519789, nan, nan, 0.5728285077951002, nan, 0.6947890818858561, nan, nan, 0.9260856681921443, 0.5976838849585837, 0.9607825170660191, nan, nan, 0.877902764192298, 0.9764888095105207, nan, nan, nan, nan, nan, 0.16078532846007051, nan, 0.0, nan, 0.4390161725067385, nan, nan, nan, nan, nan, nan, 0.23217077979468353, nan, nan, 0.8289671974682272, nan, nan, nan, nan, nan, 0.8216732780382258, 0.1086237598575426, nan, nan, nan, 0.17764306053090842, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6153083303281645, 0.0, nan, nan, nan, 0.9673457592833405, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.013296011196641007, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan] |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
hasibul1ah/bloom3b-finetuned-LoRA-for-Bengali
|
hasibul1ah
| 2023-10-09T08:42:14Z | 0 | 0 |
peft
|
[
"peft",
"bloom",
"region:us"
] | null | 2023-10-06T05:12:49Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
checkiejan/multi-qa-mpnet-base-dot-v1-covidqa-search-65-25-v2-2epoch
|
checkiejan
| 2023-10-09T08:33:56Z | 15 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-10-09T08:33:31Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 803 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 160,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
RogerB/afro-xlmr-large-kinre-finetuned
|
RogerB
| 2023-10-09T08:32:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"base_model:Davlan/afro-xlmr-large",
"base_model:finetune:Davlan/afro-xlmr-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-09T07:41:50Z |
---
license: mit
base_model: Davlan/afro-xlmr-large
tags:
- generated_from_trainer
model-index:
- name: afro-xlmr-large-kinre-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# afro-xlmr-large-kinre-finetuned
This model is a fine-tuned version of [Davlan/afro-xlmr-large](https://huggingface.co/Davlan/afro-xlmr-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2280
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4816 | 1.0 | 1875 | 1.2862 |
| 1.3458 | 2.0 | 3750 | 1.2337 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
suoluo/ddpm-celebahq-finetuned-butterflies-4epochs
|
suoluo
| 2023-10-09T08:28:20Z | 46 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-10-09T08:27:17Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('suoluo/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
ishtikar/my-pet-dog
|
ishtikar
| 2023-10-09T08:24:47Z | 4 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-10-09T08:18:34Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by ishtikar following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: VCE-58
Sample pictures of this concept:
.jpg)
|
sage-operator/llama2-qlora-7B-200it-v1
|
sage-operator
| 2023-10-09T08:22:05Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-10-09T08:21:57Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
mostafaashahin/wav2vec2-large-robust-libri-clean-100-voiced
|
mostafaashahin
| 2023-10-09T08:17:27Z | 4 | 0 |
generic
|
[
"generic",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"region:us"
] |
automatic-speech-recognition
| 2023-10-04T20:03:53Z |
---
tags:
- automatic-speech-recognition
library_name: generic
---
# Automatic Speech Recognition repository template
This is a template repository for Automatic Speech Recognition to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/pyctcdecode_asr
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/automatic-speech-recognition
cd automatic-speech-recognition
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
```
|
mdana3474/RoBERTa-large-PM-M3-Voc-hf-finetuned-echosquad
|
mdana3474
| 2023-10-09T08:17:08Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-09T08:15:08Z |
---
tags:
- generated_from_trainer
model-index:
- name: RoBERTa-large-PM-M3-Voc-hf-finetuned-echosquad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# RoBERTa-large-PM-M3-Voc-hf-finetuned-echosquad
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Daruni/DoraemonRVC2
|
Daruni
| 2023-10-09T07:59:22Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-09-02T12:59:30Z |
---
license: cc-by-nc-4.0
---
[UPDATED] Doraemon (RVC v2, rmvpe, 425 epochs, 24000 steps).
-
Trained till overtraining was detected with approximately 25 minutes of Japanese decent-quality dataset from Youtube.
Haven't tried other methods but rmvpe and mangio seems good.
Bear in mind that when the vocal/acapella's pitch is too high or low, the voice will break terribly, otherwise it should work fine.
You can combine both rmvpe and mangio audio outputs to get the best result, because in some places, mangio works better than rvmpe and vice versa.
- My recommended settings: (This is just my own experiments, you can go wild as you like)
+ Search feature ratio/Feature retrieval rate: 0.6-0.8.
+ Protect voiceless consonants and breath sounds: 0.2-0.4.
+ Hop Length (For Mangio): 128-512.
|
seohyun03/flan-t5-large-financial-phrasebank-lora
|
seohyun03
| 2023-10-09T07:51:12Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-10-09T07:51:06Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
Tiabet/Tiabet-tensorflow-finetuned-koGPT-complete_story-epoch-3
|
Tiabet
| 2023-10-09T07:50:41Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:skt/kogpt2-base-v2",
"base_model:finetune:skt/kogpt2-base-v2",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-05T10:54:25Z |
---
license: cc-by-nc-sa-4.0
base_model: skt/kogpt2-base-v2
tags:
- generated_from_keras_callback
model-index:
- name: Tiabet-tensorflow-finetuned-koGPT-complete_story-epoch-3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Tiabet-tensorflow-finetuned-koGPT-complete_story-epoch-3
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Tokenizers 0.14.1
|
Sageem/peshy
|
Sageem
| 2023-10-09T07:44:55Z | 0 | 0 | null |
[
"peshy",
"music",
"finance",
"art",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"dataset:lmsys/lmsys-chat-1m",
"dataset:heliosbrahma/mental_health_chatbot_dataset",
"dataset:knowrohit07/know_sql",
"dataset:DavidMOBrien/8000-java",
"region:us"
] | null | 2023-10-09T07:41:51Z |
---
datasets:
- fka/awesome-chatgpt-prompts
- lmsys/lmsys-chat-1m
- heliosbrahma/mental_health_chatbot_dataset
- knowrohit07/know_sql
- DavidMOBrien/8000-java
language:
- en
tags:
- peshy
- music
- finance
- art
---
|
Shiou0601/distilbert-base-uncased-flinetuned-emotion
|
Shiou0601
| 2023-10-09T07:42:25Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-09T07:33:41Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-flinetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9235
- name: F1
type: f1
value: 0.9235438455228105
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-flinetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2186
- Accuracy: 0.9235
- F1: 0.9235
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3119 | 0.908 | 0.9066 |
| No log | 2.0 | 500 | 0.2186 | 0.9235 | 0.9235 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
juliensimon/stable-diffusion-v1-5-pokemon-lora
|
juliensimon
| 2023-10-09T07:41:10Z | 14 | 4 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"dataset:lambdalabs/pokemon-blip-captions",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-10-06T13:34:59Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
datasets:
- lambdalabs/pokemon-blip-captions
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
This model was fine-tuned using 4-bit QLoRa, following the instructions in https://huggingface.co/blog/lora. The training script and training log are included.
I used a Amazon EC2 g4dn.xlarge instance (1xT4 GPU), with the Deep Learning AMI for PyTorch. Training time was about 6 hours. On-demand price is about $3, which can easily be reduced to about $1 with EC2 Spot Instances.
# LoRA text2image fine-tuning - juliensimon/stable-diffusion-v1-5-pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
fancyerii/bloomz-560m_PROMPT_TUNING_CAUSAL_LM
|
fancyerii
| 2023-10-09T07:35:26Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T07:35:23Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
GAIR/autoj-scenario-classifier
|
GAIR
| 2023-10-09T07:34:14Z | 17 | 5 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-04T16:50:34Z |
---
language:
- en
---
Please refer to our [github repo](https://github.com/GAIR-NLP/auto-j) for more details.
|
liwii/fluency-score-classification-ja
|
liwii
| 2023-10-09T07:28:38Z | 192 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"generated_from_trainer",
"base_model:line-corporation/line-distilbert-base-japanese",
"base_model:finetune:line-corporation/line-distilbert-base-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-10-06T08:58:35Z |
---
license: apache-2.0
base_model: line-corporation/line-distilbert-base-japanese
tags:
- generated_from_trainer
model-index:
- name: fluency-score-classification-ja
results: []
---
# fluency-score-classification-ja
This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the ["日本語文法誤りデータセット"](https://github.com/liwii/ja_perturbed/tree/main).
It achieves the following results on the evaluation set:
- Loss: 0.1912
- ROC AUC: 0.9811
## Model description
This model wraps [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) with [DistilBertForSequenceClassification](https://huggingface.co/docs/transformers/v4.34.0/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification) to make a binary classifier.
## Intended uses & limitations
This model can be used to classify whether the given Japanese texts are fluent (i.e., not having grammactical errors).
Example usage:
```python
# Load the tokenizer & the model
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("line-corporation/line-distilbert-base-japanese", trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained("liwii/fluency-score-classification-ja")
# Make predictions
input_tokens = tokenizer([
'黒い猫が',
'黒い猫がいます',
'あっちの方で黒い猫があくびをしています',
'あっちの方でで黒い猫ががあくびをしています',
'ある日の暮方の事である。一人の下人が、羅生門の下で雨やみを待っていた。'
],
return_tensors='pt',
padding=True)
output = model(**input_tokens)
with torch.no_grad():
# Probabilities of [not_fluent, fluent]
probs = torch.nn.functional.softmax(
output.logits, dim=1)
probs[:, 1] # => tensor([0.1007, 0.2416, 0.5635, 0.0453, 0.7701])
```
The scores could be low for short sentences even if they do not contain any grammatical erros because the training dataset consist of long sentences.
## Training and evaluation data
From ["日本語文法誤りデータセット"](https://github.com/liwii/ja_perturbed/tree/main), used 512 rows as the evaluation dataset and the rest of the dataset as the training dataset.
For each dataset split, Used the "original" rows as the data with "fluent" label, and "perturbed" as the data with "not fluent" data.
## Training procedure
Fine-tuned the model for 5 epochs. Freezed the params in the original DistilBERT during the fine-duning.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4582 | 1.0 | 647 | 0.2887 | 0.9679 |
| 0.2664 | 2.0 | 1294 | 0.2224 | 0.9761 |
| 0.2177 | 3.0 | 1941 | 0.2047 | 0.9793 |
| 0.1899 | 4.0 | 2588 | 0.1944 | 0.9807 |
| 0.1865 | 5.0 | 3235 | 0.1912 | 0.9811 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
SakataHalmi/rl_course_vizdoom_health_gathering_supreme
|
SakataHalmi
| 2023-10-09T07:24:33Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-09T07:24:25Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.94 +/- 5.68
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r SakataHalmi/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Gyummo/Gyummo
|
Gyummo
| 2023-10-09T07:22:33Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-10-09T05:47:52Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
calum/tinystories-gpt2-3M
|
calum
| 2023-10-09T07:21:52Z | 665 | 6 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"en",
"dataset:roneneldan/TinyStories",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-09T06:48:55Z |
---
tags:
- generated_from_trainer
model-index:
- name: out
results: []
datasets:
- roneneldan/TinyStories
pipeline_tag: text-generation
language:
- en
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# TinyStories-GPT2-3M
This model is a tiny (3M trainable parameters) GPT-2 model pre-trained for 3 epochs on the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) V2 dataset.
## Model description
TinyStories-GPT2-3M is a replication of the TinyStories model, using a GPT-2 architecture in place of GPT-Neo. This was a
deliberate choice made to accelerate research, as the GPT-2 architecture is more widely supported across tooling. We do not
contribute any performance improvements of note, though similarly to the original model, we find a surprising degree of coherency
within the model, given its size.
## Intended uses & limitations
Research use only - NOT suitable for commercial use per OpenAI TOS on using their APIs to source training data.
Note that the vocabulary this model was trained on is quite minimal. Out of distribution inputs will not work as well as
a larger, more general purpose model. To observe this behaviour, try generating a few tokens after a non-trivial word like
"Biology". The model typically treats words that did not frequently appear in training as character names in a story.
All training data is English. As such, input with other languages is out of distribution, and will result in the model treating
previous input as character names, ignoring it entirely, or generating meaningless tokens.
## Training and evaluation data
Trained for 3 epochs on the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) V2 dataset, produced by GPT-4.
## Training procedure
Trained for 400k steps (~7 hours) on 2xH100 80GB PCIe with 32vCPU and 500GB RAM on Runpod.
To replicate, download GPT-4 V2 version of the TinyStories dataset alongside HuggingFace's `train_clm.py` script. Then run the following:
```bash
#! /bin/bash
python train_clm.py \
--model_type=gpt2 \
--config_overrides=n_embd=64,n_layer=8,n_head=16 \
--tokenizer_name=gpt2 \
--train_file="data/TinyStoriesV2-GPT4-train.txt" \
--validation_file="data/TinyStoriesV2-GPT4-valid.txt" \
--block_size=256 \
--preprocessing_num_workers=8 \
--output_dir="out" \
--logging_dir="./log" \
--logging_steps=100 \
--logging_strategy=steps \
--save_steps=5000 \
--save_total_limit=10 \
--do_train
```
### Training hyperparameters
The following hyperparameters were used during training:
- n_embd: 64
- n_layer: 8
- n_head: 16
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Roy029/phi-1_5-finetuned-gsm8k
|
Roy029
| 2023-10-09T07:21:18Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"region:us"
] | null | 2023-10-09T07:01:22Z |
---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-gsm8k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-1_5-finetuned-gsm8k
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
kerwin7/ppo-LunarLander-v2
|
kerwin7
| 2023-10-09T07:17:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-09T07:17:09Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 236.97 +/- 12.97
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mdana3474/RoBERTa-large-PM-M3-Voc-hf-finetuned-squad
|
mdana3474
| 2023-10-09T07:16:19Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-09T07:05:56Z |
---
tags:
- generated_from_trainer
model-index:
- name: RoBERTa-large-PM-M3-Voc-hf-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# RoBERTa-large-PM-M3-Voc-hf-finetuned-squad
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
KangSoYeon/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
|
KangSoYeon
| 2023-10-09T07:14:49Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-10-08T18:14:36Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
generativex/Finance-Llama-2-70b-chat_LoRA
|
generativex
| 2023-10-09T07:10:17Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-21T15:38:39Z |
---
library_name: peft
---
GenerativeX Finance Llama2 model LoRA
お問合せ先
https://gen-x.jp/contact
|
AlvianKhairi/Scicite_classification_model
|
AlvianKhairi
| 2023-10-09T07:04:49Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:scicite",
"base_model:allenai/scibert_scivocab_uncased",
"base_model:finetune:allenai/scibert_scivocab_uncased",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-26T08:51:56Z |
---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
datasets:
- scicite
metrics:
- accuracy
model-index:
- name: Scicite_classification_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: scicite
type: scicite
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9224890829694323
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Scicite_classification_model
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the scicite dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4704
- Accuracy: 0.9225
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2493 | 1.0 | 513 | 0.2034 | 0.9214 |
| 0.1777 | 2.0 | 1026 | 0.1942 | 0.9247 |
| 0.1385 | 3.0 | 1539 | 0.2552 | 0.9247 |
| 0.1019 | 4.0 | 2052 | 0.2995 | 0.9258 |
| 0.0705 | 5.0 | 2565 | 0.3964 | 0.9181 |
| 0.0444 | 6.0 | 3078 | 0.4243 | 0.9203 |
| 0.0331 | 7.0 | 3591 | 0.4904 | 0.9192 |
| 0.0223 | 8.0 | 4104 | 0.4704 | 0.9225 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
tahreema-r-z/my_awesome_billsum_model
|
tahreema-r-z
| 2023-10-09T06:56:54Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-12T12:27:13Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.2001
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1970
- Rouge1: 0.2001
- Rouge2: 0.1053
- Rougel: 0.1716
- Rougelsum: 0.1717
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 124 | 2.5355 | 0.1414 | 0.0544 | 0.1183 | 0.1182 | 19.0 |
| No log | 2.0 | 248 | 2.3807 | 0.1674 | 0.0738 | 0.1416 | 0.1412 | 19.0 |
| No log | 3.0 | 372 | 2.3128 | 0.1977 | 0.1007 | 0.1695 | 0.1697 | 19.0 |
| No log | 4.0 | 496 | 2.2729 | 0.1987 | 0.1008 | 0.1695 | 0.1694 | 19.0 |
| 2.8078 | 5.0 | 620 | 2.2460 | 0.1997 | 0.1025 | 0.1707 | 0.1707 | 19.0 |
| 2.8078 | 6.0 | 744 | 2.2251 | 0.2011 | 0.1034 | 0.1715 | 0.1714 | 19.0 |
| 2.8078 | 7.0 | 868 | 2.2133 | 0.2016 | 0.1049 | 0.172 | 0.172 | 19.0 |
| 2.8078 | 8.0 | 992 | 2.2035 | 0.2018 | 0.1062 | 0.1723 | 0.1725 | 19.0 |
| 2.4762 | 9.0 | 1116 | 2.1985 | 0.2008 | 0.1059 | 0.172 | 0.1723 | 19.0 |
| 2.4762 | 10.0 | 1240 | 2.1970 | 0.2001 | 0.1053 | 0.1716 | 0.1717 | 19.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
krthk/kaps_model
|
krthk
| 2023-10-09T06:51:18Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-10-09T06:51:12Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
krthk/llama2-qlora-finetunined-french
|
krthk
| 2023-10-09T06:51:04Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-09-20T07:00:38Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
waiwai256/distilbert-base-uncased-finetuned-emotion
|
waiwai256
| 2023-10-09T06:43:59Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-09T05:41:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9249648984209448
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2208
- Accuracy: 0.925
- F1: 0.9250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8234 | 1.0 | 250 | 0.3223 | 0.905 | 0.9021 |
| 0.252 | 2.0 | 500 | 0.2208 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
omarfaheem/chatbot
|
omarfaheem
| 2023-10-09T06:21:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-10-09T06:18:54Z |
---
title: Demo
app_file: app.py
sdk: gradio
sdk_version: 3.41.2
---
|
mesolitica/llama2-embedding-600m-8k
|
mesolitica
| 2023-10-09T06:11:30Z | 17 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"feature-extraction",
"custom_code",
"ms",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-10-05T23:50:19Z |
---
language:
- ms
---
# 600M 32768 context length Llama2 on Malaysian text embedding task
Trained on truncated 8k context length, but infer able to scale up to 32k context length.
README at https://github.com/mesolitica/llama2-embedding#finetune
WandB, https://wandb.ai/mesolitica/llama2-embedding-600m?workspace=user-husein-mesolitica
## how-to
```python
from transformers import AutoModel, AutoTokenizer
from sklearn.metrics.pairwise import cosine_similarity
model = AutoModel.from_pretrained('mesolitica/llama2-embedding-600m-8k', trust_remote_code = True)
tokenizer = AutoTokenizer.from_pretrained('mesolitica/llama2-embedding-600m-8k')
input_ids = tokenizer(
[
'tak suka ayam',
'Isu perkauman: Kerajaan didakwa terdesak kaitkan pemimpin PN',
'nasi ayam tu sedap',
'suka ikan goreng?',
'Kerajaan tidak akan berkompromi dengan isu perkauman dan agama yang dimanipulasi pihak tertentu untuk mengganggu-gugat kestabilan negara serta ketenteraman rakyat.',
'rasis bodo mamat tu',
'kerajaan sekarang xde otak',
'aku nak sukan olimpik ni',
'malaysia dapat x pingat kt sukan asia?',
'pingat gangsa menerusi terjun dan olahraga pada hari ke-10',
'Kerajaan negeri kini dibenarkan melaksanakan penerokaan awal unsur nadir bumi (REE) berdasarkan prosedur operasi standard (SOP) sedia ada untuk perlombongan nadir bumi dan mineral.',
'KONTINJEN Malaysia mendekati sasaran 27 pingat di Sukan Asia kali ini esok, selepas menuai dua lagi pingat gangsa menerusi terjun dan olahraga pada hari ke-10 pertandingan, pada Selasa.'
],
return_tensors = 'pt',
padding = True
)
v = model.encode(input_ids).detach().numpy()
v.shape
```
```
(12, 1536)
```
|
St4n/wav2vec2-fine-tuning-960h-demo-google-colab
|
St4n
| 2023-10-09T05:42:10Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:St4n/wav2vec2-base-960h-demo-google-colab",
"base_model:finetune:St4n/wav2vec2-base-960h-demo-google-colab",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-09T03:44:07Z |
---
license: apache-2.0
base_model: St4n/wav2vec2-base-960h-demo-google-colab
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-fine-tuning-960h-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-fine-tuning-960h-demo-google-colab
This model is a fine-tuned version of [St4n/wav2vec2-base-960h-demo-google-colab](https://huggingface.co/St4n/wav2vec2-base-960h-demo-google-colab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6643
- Wer: 0.9985
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8048 | 5.81 | 500 | 0.5176 | 1.0 |
| 0.353 | 11.63 | 1000 | 0.5259 | 1.0 |
| 0.2843 | 17.44 | 1500 | 0.5725 | 0.9985 |
| 0.3374 | 23.26 | 2000 | 0.6190 | 0.9985 |
| 0.1625 | 29.07 | 2500 | 0.6643 | 0.9985 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
distill-io/detr-v9
|
distill-io
| 2023-10-09T05:29:06Z | 189 | 0 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-10-09T05:28:47Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr-9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-9
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9548
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| No log | 0.68 | 100 | 3.2412 |
| 3.9658 | 1.36 | 200 | 3.0423 |
| 3.063 | 2.04 | 300 | 2.8348 |
| 3.063 | 2.72 | 400 | 2.8693 |
| 2.8954 | 3.4 | 500 | 2.6293 |
| 2.7743 | 4.08 | 600 | 2.6178 |
| 2.7743 | 4.76 | 700 | 2.5513 |
| 2.6323 | 5.44 | 800 | 2.5795 |
| 2.6396 | 6.12 | 900 | 2.3751 |
| 2.6396 | 6.8 | 1000 | 2.3357 |
| 2.4932 | 7.48 | 1100 | 2.3184 |
| 2.4299 | 8.16 | 1200 | 2.2754 |
| 2.4299 | 8.84 | 1300 | 2.2419 |
| 2.3508 | 9.52 | 1400 | 2.1568 |
| 2.2593 | 10.2 | 1500 | 2.1253 |
| 2.2593 | 10.88 | 1600 | 2.1364 |
| 2.2376 | 11.56 | 1700 | 2.1320 |
| 2.1749 | 12.24 | 1800 | 2.0464 |
| 2.1749 | 12.93 | 1900 | 2.0201 |
| 2.0878 | 13.61 | 2000 | 2.1603 |
| 2.0701 | 14.29 | 2100 | 1.9910 |
| 2.0701 | 14.97 | 2200 | 1.9665 |
| 2.007 | 15.65 | 2300 | 1.8944 |
| 1.9432 | 16.33 | 2400 | 1.8717 |
| 1.9552 | 17.01 | 2500 | 1.9168 |
| 1.9552 | 17.69 | 2600 | 1.8580 |
| 1.905 | 18.37 | 2700 | 1.8306 |
| 1.8821 | 19.05 | 2800 | 1.8386 |
| 1.8821 | 19.73 | 2900 | 1.8215 |
| 1.8569 | 20.41 | 3000 | 1.7825 |
| 1.8083 | 21.09 | 3100 | 1.7335 |
| 1.8083 | 21.77 | 3200 | 1.7117 |
| 1.7617 | 22.45 | 3300 | 1.7170 |
| 1.7304 | 23.13 | 3400 | 1.7235 |
| 1.7304 | 23.81 | 3500 | 1.6907 |
| 1.7165 | 24.49 | 3600 | 1.6281 |
| 1.6793 | 25.17 | 3700 | 1.5950 |
| 1.6793 | 25.85 | 3800 | 1.5856 |
| 1.656 | 26.53 | 3900 | 1.6097 |
| 1.6229 | 27.21 | 4000 | 1.5895 |
| 1.6229 | 27.89 | 4100 | 1.6217 |
| 1.6292 | 28.57 | 4200 | 1.5542 |
| 1.5697 | 29.25 | 4300 | 1.6426 |
| 1.5697 | 29.93 | 4400 | 1.6292 |
| 1.6189 | 30.61 | 4500 | 1.5757 |
| 1.573 | 31.29 | 4600 | 1.5476 |
| 1.573 | 31.97 | 4700 | 1.5437 |
| 1.5273 | 32.65 | 4800 | 1.5518 |
| 1.5054 | 33.33 | 4900 | 1.4695 |
| 1.4757 | 34.01 | 5000 | 1.5011 |
| 1.4757 | 34.69 | 5100 | 1.4744 |
| 1.4538 | 35.37 | 5200 | 1.4481 |
| 1.4319 | 36.05 | 5300 | 1.4195 |
| 1.4319 | 36.73 | 5400 | 1.5335 |
| 1.3979 | 37.41 | 5500 | 1.3619 |
| 1.3775 | 38.1 | 5600 | 1.4305 |
| 1.3775 | 38.78 | 5700 | 1.3961 |
| 1.3914 | 39.46 | 5800 | 1.3965 |
| 1.3373 | 40.14 | 5900 | 1.3740 |
| 1.3373 | 40.82 | 6000 | 1.4195 |
| 1.3315 | 41.5 | 6100 | 1.4008 |
| 1.3288 | 42.18 | 6200 | 1.3568 |
| 1.3288 | 42.86 | 6300 | 1.3631 |
| 1.2927 | 43.54 | 6400 | 1.3045 |
| 1.2694 | 44.22 | 6500 | 1.3606 |
| 1.2694 | 44.9 | 6600 | 1.3045 |
| 1.2487 | 45.58 | 6700 | 1.3688 |
| 1.2544 | 46.26 | 6800 | 1.2831 |
| 1.2544 | 46.94 | 6900 | 1.2673 |
| 1.2359 | 47.62 | 7000 | 1.2622 |
| 1.2207 | 48.3 | 7100 | 1.2208 |
| 1.2207 | 48.98 | 7200 | 1.2399 |
| 1.2011 | 49.66 | 7300 | 1.2899 |
| 1.1915 | 50.34 | 7400 | 1.2599 |
| 1.1742 | 51.02 | 7500 | 1.1938 |
| 1.1742 | 51.7 | 7600 | 1.2566 |
| 1.1587 | 52.38 | 7700 | 1.1858 |
| 1.1431 | 53.06 | 7800 | 1.2510 |
| 1.1431 | 53.74 | 7900 | 1.1766 |
| 1.1473 | 54.42 | 8000 | 1.1920 |
| 1.1406 | 55.1 | 8100 | 1.3181 |
| 1.1406 | 55.78 | 8200 | 1.2287 |
| 1.1288 | 56.46 | 8300 | 1.1828 |
| 1.1058 | 57.14 | 8400 | 1.3380 |
| 1.1058 | 57.82 | 8500 | 1.3039 |
| 1.1011 | 58.5 | 8600 | 1.1949 |
| 1.0685 | 59.18 | 8700 | 1.1771 |
| 1.0685 | 59.86 | 8800 | 1.1391 |
| 1.077 | 60.54 | 8900 | 1.1271 |
| 1.0787 | 61.22 | 9000 | 1.1005 |
| 1.0787 | 61.9 | 9100 | 1.1096 |
| 1.0493 | 62.59 | 9200 | 1.1689 |
| 1.0428 | 63.27 | 9300 | 1.1353 |
| 1.0428 | 63.95 | 9400 | 1.1348 |
| 1.1068 | 64.63 | 9500 | 1.1882 |
| 1.0131 | 65.31 | 9600 | 1.2055 |
| 1.0131 | 65.99 | 9700 | 1.0887 |
| 1.0127 | 66.67 | 9800 | 1.1398 |
| 1.0163 | 67.35 | 9900 | 1.0899 |
| 1.0039 | 68.03 | 10000 | 1.0990 |
| 1.0039 | 68.71 | 10100 | 1.1135 |
| 1.0104 | 69.39 | 10200 | 1.1319 |
| 1.0014 | 70.07 | 10300 | 1.1386 |
| 1.0014 | 70.75 | 10400 | 1.1442 |
| 0.9976 | 71.43 | 10500 | 1.2050 |
| 0.9616 | 72.11 | 10600 | 1.0659 |
| 0.9616 | 72.79 | 10700 | 1.1428 |
| 0.9801 | 73.47 | 10800 | 1.1244 |
| 0.9548 | 74.15 | 10900 | 1.1127 |
| 0.9548 | 74.83 | 11000 | 1.1491 |
| 0.9669 | 75.51 | 11100 | 1.0919 |
| 0.9556 | 76.19 | 11200 | 1.1382 |
| 0.9556 | 76.87 | 11300 | 1.1156 |
| 0.919 | 77.55 | 11400 | 1.0326 |
| 0.9121 | 78.23 | 11500 | 1.1168 |
| 0.9121 | 78.91 | 11600 | 1.1301 |
| 0.9038 | 79.59 | 11700 | 1.1149 |
| 0.8933 | 80.27 | 11800 | 1.0959 |
| 0.8933 | 80.95 | 11900 | 1.1232 |
| 0.8999 | 81.63 | 12000 | 1.0805 |
| 0.8931 | 82.31 | 12100 | 1.1335 |
| 0.8931 | 82.99 | 12200 | 1.1315 |
| 0.8815 | 83.67 | 12300 | 1.0665 |
| 0.8694 | 84.35 | 12400 | 1.0750 |
| 0.8793 | 85.03 | 12500 | 1.0751 |
| 0.8793 | 85.71 | 12600 | 1.0839 |
| 0.9073 | 86.39 | 12700 | 1.1007 |
| 0.8811 | 87.07 | 12800 | 1.0817 |
| 0.8811 | 87.76 | 12900 | 1.0797 |
| 0.8407 | 88.44 | 13000 | 1.1029 |
| 0.8772 | 89.12 | 13100 | 1.0542 |
| 0.8772 | 89.8 | 13200 | 1.0271 |
| 0.8447 | 90.48 | 13300 | 1.0275 |
| 0.8392 | 91.16 | 13400 | 0.9989 |
| 0.8392 | 91.84 | 13500 | 1.0119 |
| 0.8329 | 92.52 | 13600 | 1.0015 |
| 0.8392 | 93.2 | 13700 | 1.0249 |
| 0.8392 | 93.88 | 13800 | 1.0294 |
| 0.8175 | 94.56 | 13900 | 1.0980 |
| 0.8401 | 95.24 | 14000 | 1.0724 |
| 0.8401 | 95.92 | 14100 | 1.0085 |
| 0.8262 | 96.6 | 14200 | 1.0564 |
| 0.8007 | 97.28 | 14300 | 1.0666 |
| 0.8007 | 97.96 | 14400 | 1.0119 |
| 0.8013 | 98.64 | 14500 | 1.1449 |
| 0.7966 | 99.32 | 14600 | 1.0698 |
| 0.7966 | 100.0 | 14700 | 1.0514 |
| 0.7963 | 100.68 | 14800 | 0.9480 |
| 0.7939 | 101.36 | 14900 | 0.9131 |
| 0.7782 | 102.04 | 15000 | 0.9641 |
| 0.7782 | 102.72 | 15100 | 0.9714 |
| 0.7767 | 103.4 | 15200 | 1.0656 |
| 0.7762 | 104.08 | 15300 | 1.0194 |
| 0.7762 | 104.76 | 15400 | 1.0062 |
| 0.7929 | 105.44 | 15500 | 1.0862 |
| 0.7757 | 106.12 | 15600 | 1.0567 |
| 0.7757 | 106.8 | 15700 | 0.9659 |
| 0.7799 | 107.48 | 15800 | 0.9637 |
| 0.7736 | 108.16 | 15900 | 0.9711 |
| 0.7736 | 108.84 | 16000 | 1.0166 |
| 0.7483 | 109.52 | 16100 | 1.0213 |
| 0.7381 | 110.2 | 16200 | 0.9550 |
| 0.7381 | 110.88 | 16300 | 0.9763 |
| 0.7287 | 111.56 | 16400 | 0.9390 |
| 0.7327 | 112.24 | 16500 | 1.0193 |
| 0.7327 | 112.93 | 16600 | 0.9088 |
| 0.7377 | 113.61 | 16700 | 0.9728 |
| 0.7109 | 114.29 | 16800 | 1.0400 |
| 0.7109 | 114.97 | 16900 | 1.0058 |
| 0.717 | 115.65 | 17000 | 0.9745 |
| 0.7187 | 116.33 | 17100 | 1.0387 |
| 0.7097 | 117.01 | 17200 | 0.9599 |
| 0.7097 | 117.69 | 17300 | 1.0639 |
| 0.7072 | 118.37 | 17400 | 1.0272 |
| 0.7124 | 119.05 | 17500 | 0.9891 |
| 0.7124 | 119.73 | 17600 | 0.9851 |
| 0.6856 | 120.41 | 17700 | 0.9980 |
| 0.6781 | 121.09 | 17800 | 1.0234 |
| 0.6781 | 121.77 | 17900 | 1.0307 |
| 0.6827 | 122.45 | 18000 | 0.9978 |
| 0.6793 | 123.13 | 18100 | 0.9692 |
| 0.6793 | 123.81 | 18200 | 0.9417 |
| 0.6867 | 124.49 | 18300 | 0.9869 |
| 0.6744 | 125.17 | 18400 | 0.9923 |
| 0.6744 | 125.85 | 18500 | 0.9756 |
| 0.6593 | 126.53 | 18600 | 0.9938 |
| 0.6488 | 127.21 | 18700 | 0.9382 |
| 0.6488 | 127.89 | 18800 | 0.9534 |
| 0.644 | 128.57 | 18900 | 0.9072 |
| 0.6725 | 129.25 | 19000 | 1.0747 |
| 0.6725 | 129.93 | 19100 | 0.9569 |
| 0.656 | 130.61 | 19200 | 0.9673 |
| 0.6653 | 131.29 | 19300 | 0.9582 |
| 0.6653 | 131.97 | 19400 | 0.9470 |
| 0.6719 | 132.65 | 19500 | 0.9331 |
| 0.6665 | 133.33 | 19600 | 0.9860 |
| 0.6533 | 134.01 | 19700 | 1.0467 |
| 0.6533 | 134.69 | 19800 | 1.0140 |
| 0.6489 | 135.37 | 19900 | 0.9366 |
| 0.6546 | 136.05 | 20000 | 0.9923 |
| 0.6546 | 136.73 | 20100 | 1.1226 |
| 0.6501 | 137.41 | 20200 | 0.9184 |
| 0.6487 | 138.1 | 20300 | 1.0354 |
| 0.6487 | 138.78 | 20400 | 1.0149 |
| 0.6462 | 139.46 | 20500 | 0.9540 |
| 0.6413 | 140.14 | 20600 | 1.0019 |
| 0.6413 | 140.82 | 20700 | 0.9481 |
| 0.6563 | 141.5 | 20800 | 0.9663 |
| 0.6485 | 142.18 | 20900 | 0.9496 |
| 0.6485 | 142.86 | 21000 | 0.9743 |
| 0.6489 | 143.54 | 21100 | 1.0144 |
| 0.6493 | 144.22 | 21200 | 0.9667 |
| 0.6493 | 144.9 | 21300 | 0.9665 |
| 0.6385 | 145.58 | 21400 | 1.0027 |
| 0.6337 | 146.26 | 21500 | 0.9546 |
| 0.6337 | 146.94 | 21600 | 1.0924 |
| 0.6199 | 147.62 | 21700 | 0.9781 |
| 0.6389 | 148.3 | 21800 | 1.0117 |
| 0.6389 | 148.98 | 21900 | 0.9892 |
| 0.638 | 149.66 | 22000 | 0.9263 |
| 0.615 | 150.34 | 22100 | 0.9498 |
| 0.6052 | 151.02 | 22200 | 0.9727 |
| 0.6052 | 151.7 | 22300 | 0.9810 |
| 0.6144 | 152.38 | 22400 | 0.9167 |
| 0.6024 | 153.06 | 22500 | 0.9862 |
| 0.6024 | 153.74 | 22600 | 1.0106 |
| 0.6015 | 154.42 | 22700 | 1.0130 |
| 0.5847 | 155.1 | 22800 | 1.0303 |
| 0.5847 | 155.78 | 22900 | 0.9814 |
| 0.6149 | 156.46 | 23000 | 0.8867 |
| 0.5985 | 157.14 | 23100 | 0.9578 |
| 0.5985 | 157.82 | 23200 | 1.0177 |
| 0.6023 | 158.5 | 23300 | 0.9790 |
| 0.5924 | 159.18 | 23400 | 0.9915 |
| 0.5924 | 159.86 | 23500 | 0.9732 |
| 0.5974 | 160.54 | 23600 | 0.9765 |
| 0.6002 | 161.22 | 23700 | 0.9913 |
| 0.6002 | 161.9 | 23800 | 1.0328 |
| 0.5858 | 162.59 | 23900 | 0.9185 |
| 0.5894 | 163.27 | 24000 | 0.9617 |
| 0.5894 | 163.95 | 24100 | 0.9610 |
| 0.5677 | 164.63 | 24200 | 0.9228 |
| 0.5782 | 165.31 | 24300 | 0.9632 |
| 0.5782 | 165.99 | 24400 | 0.9346 |
| 0.5772 | 166.67 | 24500 | 1.0165 |
| 0.5823 | 167.35 | 24600 | 1.0094 |
| 0.5719 | 168.03 | 24700 | 0.9632 |
| 0.5719 | 168.71 | 24800 | 0.9426 |
| 0.5629 | 169.39 | 24900 | 0.9430 |
| 0.5665 | 170.07 | 25000 | 0.9907 |
| 0.5665 | 170.75 | 25100 | 0.9612 |
| 0.5634 | 171.43 | 25200 | 1.0117 |
| 0.5662 | 172.11 | 25300 | 1.0252 |
| 0.5662 | 172.79 | 25400 | 0.9665 |
| 0.5645 | 173.47 | 25500 | 0.9646 |
| 0.5567 | 174.15 | 25600 | 0.9745 |
| 0.5567 | 174.83 | 25700 | 0.9662 |
| 0.5676 | 175.51 | 25800 | 0.9624 |
| 0.5614 | 176.19 | 25900 | 0.9740 |
| 0.5614 | 176.87 | 26000 | 0.9564 |
| 0.5498 | 177.55 | 26100 | 0.9050 |
| 0.5664 | 178.23 | 26200 | 0.9700 |
| 0.5664 | 178.91 | 26300 | 1.0037 |
| 0.5471 | 179.59 | 26400 | 0.9914 |
| 0.5366 | 180.27 | 26500 | 1.0204 |
| 0.5366 | 180.95 | 26600 | 0.9942 |
| 0.5436 | 181.63 | 26700 | 0.9809 |
| 0.5703 | 182.31 | 26800 | 1.0165 |
| 0.5703 | 182.99 | 26900 | 0.9786 |
| 0.549 | 183.67 | 27000 | 1.0115 |
| 0.5397 | 184.35 | 27100 | 1.0087 |
| 0.5344 | 185.03 | 27200 | 0.9985 |
| 0.5344 | 185.71 | 27300 | 0.9601 |
| 0.5346 | 186.39 | 27400 | 0.9388 |
| 0.5548 | 187.07 | 27500 | 0.9791 |
| 0.5548 | 187.76 | 27600 | 0.9298 |
| 0.5437 | 188.44 | 27700 | 1.0127 |
| 0.5551 | 189.12 | 27800 | 0.9693 |
| 0.5551 | 189.8 | 27900 | 0.9636 |
| 0.5438 | 190.48 | 28000 | 0.9502 |
| 0.5263 | 191.16 | 28100 | 0.9204 |
| 0.5263 | 191.84 | 28200 | 0.9547 |
| 0.5232 | 192.52 | 28300 | 0.9199 |
| 0.525 | 193.2 | 28400 | 1.0316 |
| 0.525 | 193.88 | 28500 | 0.9328 |
| 0.5372 | 194.56 | 28600 | 0.9614 |
| 0.5478 | 195.24 | 28700 | 0.9657 |
| 0.5478 | 195.92 | 28800 | 0.9648 |
| 0.5401 | 196.6 | 28900 | 0.9427 |
| 0.5338 | 197.28 | 29000 | 0.9627 |
| 0.5338 | 197.96 | 29100 | 0.9876 |
| 0.5131 | 198.64 | 29200 | 0.9777 |
| 0.522 | 199.32 | 29300 | 1.0747 |
| 0.522 | 200.0 | 29400 | 1.0181 |
| 0.5275 | 200.68 | 29500 | 0.9527 |
| 0.5342 | 201.36 | 29600 | 1.0019 |
| 0.5297 | 202.04 | 29700 | 0.9576 |
| 0.5297 | 202.72 | 29800 | 0.9968 |
| 0.5367 | 203.4 | 29900 | 0.9542 |
| 0.5148 | 204.08 | 30000 | 0.9250 |
| 0.5148 | 204.76 | 30100 | 1.0072 |
| 0.5176 | 205.44 | 30200 | 0.9485 |
| 0.5125 | 206.12 | 30300 | 0.9220 |
| 0.5125 | 206.8 | 30400 | 0.9326 |
| 0.5075 | 207.48 | 30500 | 0.9153 |
| 0.5084 | 208.16 | 30600 | 0.9837 |
| 0.5084 | 208.84 | 30700 | 0.9482 |
| 0.503 | 209.52 | 30800 | 0.9677 |
| 0.5001 | 210.2 | 30900 | 0.9626 |
| 0.5001 | 210.88 | 31000 | 0.9106 |
| 0.5115 | 211.56 | 31100 | 1.0392 |
| 0.5012 | 212.24 | 31200 | 0.9873 |
| 0.5012 | 212.93 | 31300 | 0.9727 |
| 0.5122 | 213.61 | 31400 | 1.0177 |
| 0.4997 | 214.29 | 31500 | 0.9833 |
| 0.4997 | 214.97 | 31600 | 0.9190 |
| 0.5147 | 215.65 | 31700 | 0.9619 |
| 0.5122 | 216.33 | 31800 | 0.8989 |
| 0.4964 | 217.01 | 31900 | 0.8954 |
| 0.4964 | 217.69 | 32000 | 0.9823 |
| 0.4953 | 218.37 | 32100 | 1.0035 |
| 0.4951 | 219.05 | 32200 | 0.9277 |
| 0.4951 | 219.73 | 32300 | 0.9064 |
| 0.5088 | 220.41 | 32400 | 0.9687 |
| 0.5003 | 221.09 | 32500 | 1.0024 |
| 0.5003 | 221.77 | 32600 | 0.9359 |
| 0.5013 | 222.45 | 32700 | 0.8833 |
| 0.5002 | 223.13 | 32800 | 0.8583 |
| 0.5002 | 223.81 | 32900 | 0.8660 |
| 0.4936 | 224.49 | 33000 | 0.8381 |
| 0.4919 | 225.17 | 33100 | 0.8624 |
| 0.4919 | 225.85 | 33200 | 0.8423 |
| 0.5002 | 226.53 | 33300 | 0.8991 |
| 0.4781 | 227.21 | 33400 | 0.9186 |
| 0.4781 | 227.89 | 33500 | 0.8910 |
| 0.4823 | 228.57 | 33600 | 0.9290 |
| 0.4899 | 229.25 | 33700 | 0.9599 |
| 0.4899 | 229.93 | 33800 | 0.8219 |
| 0.4986 | 230.61 | 33900 | 0.8769 |
| 0.4837 | 231.29 | 34000 | 0.9619 |
| 0.4837 | 231.97 | 34100 | 0.9140 |
| 0.4838 | 232.65 | 34200 | 0.9978 |
| 0.491 | 233.33 | 34300 | 0.9176 |
| 0.4786 | 234.01 | 34400 | 0.9227 |
| 0.4786 | 234.69 | 34500 | 0.9498 |
| 0.4754 | 235.37 | 34600 | 0.9387 |
| 0.476 | 236.05 | 34700 | 0.9002 |
| 0.476 | 236.73 | 34800 | 0.9502 |
| 0.4869 | 237.41 | 34900 | 0.9350 |
| 0.4638 | 238.1 | 35000 | 0.9066 |
| 0.4638 | 238.78 | 35100 | 0.8994 |
| 0.4748 | 239.46 | 35200 | 0.9009 |
| 0.4617 | 240.14 | 35300 | 0.9449 |
| 0.4617 | 240.82 | 35400 | 0.9188 |
| 0.47 | 241.5 | 35500 | 0.9288 |
| 0.4572 | 242.18 | 35600 | 0.9002 |
| 0.4572 | 242.86 | 35700 | 0.9040 |
| 0.4687 | 243.54 | 35800 | 0.9652 |
| 0.4808 | 244.22 | 35900 | 0.9639 |
| 0.4808 | 244.9 | 36000 | 0.8987 |
| 0.4647 | 245.58 | 36100 | 0.8977 |
| 0.4728 | 246.26 | 36200 | 0.9150 |
| 0.4728 | 246.94 | 36300 | 0.8753 |
| 0.464 | 247.62 | 36400 | 0.9486 |
| 0.4628 | 248.3 | 36500 | 0.8833 |
| 0.4628 | 248.98 | 36600 | 0.9540 |
| 0.4692 | 249.66 | 36700 | 0.8930 |
| 0.4732 | 250.34 | 36800 | 0.9098 |
| 0.4552 | 251.02 | 36900 | 0.9363 |
| 0.4552 | 251.7 | 37000 | 0.9720 |
| 0.458 | 252.38 | 37100 | 0.8646 |
| 0.4576 | 253.06 | 37200 | 0.9070 |
| 0.4576 | 253.74 | 37300 | 0.9384 |
| 0.4575 | 254.42 | 37400 | 0.8082 |
| 0.4673 | 255.1 | 37500 | 0.9216 |
| 0.4673 | 255.78 | 37600 | 0.8547 |
| 0.4685 | 256.46 | 37700 | 0.9245 |
| 0.4593 | 257.14 | 37800 | 0.9047 |
| 0.4593 | 257.82 | 37900 | 0.8846 |
| 0.4549 | 258.5 | 38000 | 0.9293 |
| 0.4573 | 259.18 | 38100 | 0.8907 |
| 0.4573 | 259.86 | 38200 | 0.9024 |
| 0.463 | 260.54 | 38300 | 0.9144 |
| 0.4549 | 261.22 | 38400 | 0.9190 |
| 0.4549 | 261.9 | 38500 | 0.8713 |
| 0.4459 | 262.59 | 38600 | 0.8938 |
| 0.4625 | 263.27 | 38700 | 0.8699 |
| 0.4625 | 263.95 | 38800 | 0.8854 |
| 0.4379 | 264.63 | 38900 | 0.8578 |
| 0.4458 | 265.31 | 39000 | 0.9256 |
| 0.4458 | 265.99 | 39100 | 0.9711 |
| 0.4438 | 266.67 | 39200 | 0.9254 |
| 0.4515 | 267.35 | 39300 | 0.9599 |
| 0.4565 | 268.03 | 39400 | 0.9208 |
| 0.4565 | 268.71 | 39500 | 0.9153 |
| 0.4586 | 269.39 | 39600 | 0.8639 |
| 0.4368 | 270.07 | 39700 | 0.8932 |
| 0.4368 | 270.75 | 39800 | 0.9732 |
| 0.4458 | 271.43 | 39900 | 1.0161 |
| 0.4452 | 272.11 | 40000 | 0.9847 |
| 0.4452 | 272.79 | 40100 | 0.9129 |
| 0.4499 | 273.47 | 40200 | 0.9575 |
| 0.4308 | 274.15 | 40300 | 0.9167 |
| 0.4308 | 274.83 | 40400 | 0.9678 |
| 0.4399 | 275.51 | 40500 | 0.9841 |
| 0.4355 | 276.19 | 40600 | 0.9262 |
| 0.4355 | 276.87 | 40700 | 0.9440 |
| 0.4312 | 277.55 | 40800 | 0.9780 |
| 0.4259 | 278.23 | 40900 | 0.9153 |
| 0.4259 | 278.91 | 41000 | 0.9735 |
| 0.4354 | 279.59 | 41100 | 0.9483 |
| 0.4318 | 280.27 | 41200 | 0.9608 |
| 0.4318 | 280.95 | 41300 | 0.9413 |
| 0.438 | 281.63 | 41400 | 0.9569 |
| 0.4388 | 282.31 | 41500 | 0.9049 |
| 0.4388 | 282.99 | 41600 | 0.8986 |
| 0.4438 | 283.67 | 41700 | 0.9700 |
| 0.4368 | 284.35 | 41800 | 0.9049 |
| 0.4371 | 285.03 | 41900 | 0.8275 |
| 0.4371 | 285.71 | 42000 | 1.0013 |
| 0.4497 | 286.39 | 42100 | 0.9242 |
| 0.4601 | 287.07 | 42200 | 0.9197 |
| 0.4601 | 287.76 | 42300 | 0.8905 |
| 0.4428 | 288.44 | 42400 | 0.8584 |
| 0.4369 | 289.12 | 42500 | 0.8881 |
| 0.4369 | 289.8 | 42600 | 0.9121 |
| 0.4325 | 290.48 | 42700 | 0.8598 |
| 0.4266 | 291.16 | 42800 | 0.9031 |
| 0.4266 | 291.84 | 42900 | 0.8444 |
| 0.4218 | 292.52 | 43000 | 0.8966 |
| 0.4252 | 293.2 | 43100 | 0.9224 |
| 0.4252 | 293.88 | 43200 | 1.0000 |
| 0.4231 | 294.56 | 43300 | 0.9438 |
| 0.4204 | 295.24 | 43400 | 0.8706 |
| 0.4204 | 295.92 | 43500 | 0.8563 |
| 0.4207 | 296.6 | 43600 | 0.9680 |
| 0.4247 | 297.28 | 43700 | 0.8682 |
| 0.4247 | 297.96 | 43800 | 0.9071 |
| 0.4247 | 298.64 | 43900 | 0.8642 |
| 0.4183 | 299.32 | 44000 | 0.8874 |
| 0.4183 | 300.0 | 44100 | 0.9027 |
| 0.4352 | 300.68 | 44200 | 0.8447 |
| 0.4241 | 301.36 | 44300 | 0.9028 |
| 0.4266 | 302.04 | 44400 | 0.9055 |
| 0.4266 | 302.72 | 44500 | 0.9251 |
| 0.4183 | 303.4 | 44600 | 0.9440 |
| 0.4148 | 304.08 | 44700 | 0.9566 |
| 0.4148 | 304.76 | 44800 | 0.8994 |
| 0.4217 | 305.44 | 44900 | 1.0046 |
| 0.4165 | 306.12 | 45000 | 0.8346 |
| 0.4165 | 306.8 | 45100 | 0.8727 |
| 0.4129 | 307.48 | 45200 | 0.9284 |
| 0.408 | 308.16 | 45300 | 0.9695 |
| 0.408 | 308.84 | 45400 | 0.9798 |
| 0.3986 | 309.52 | 45500 | 0.9456 |
| 0.4219 | 310.2 | 45600 | 0.9017 |
| 0.4219 | 310.88 | 45700 | 0.9370 |
| 0.422 | 311.56 | 45800 | 0.8430 |
| 0.415 | 312.24 | 45900 | 0.9242 |
| 0.415 | 312.93 | 46000 | 0.9381 |
| 0.4173 | 313.61 | 46100 | 0.8775 |
| 0.4204 | 314.29 | 46200 | 0.9259 |
| 0.4204 | 314.97 | 46300 | 0.9272 |
| 0.4073 | 315.65 | 46400 | 0.8997 |
| 0.4137 | 316.33 | 46500 | 0.9177 |
| 0.4043 | 317.01 | 46600 | 0.9592 |
| 0.4043 | 317.69 | 46700 | 0.9665 |
| 0.4224 | 318.37 | 46800 | 0.8610 |
| 0.415 | 319.05 | 46900 | 0.8602 |
| 0.415 | 319.73 | 47000 | 0.9231 |
| 0.4103 | 320.41 | 47100 | 0.9351 |
| 0.4162 | 321.09 | 47200 | 0.9975 |
| 0.4162 | 321.77 | 47300 | 0.9037 |
| 0.4083 | 322.45 | 47400 | 0.8951 |
| 0.4173 | 323.13 | 47500 | 0.9530 |
| 0.4173 | 323.81 | 47600 | 0.8620 |
| 0.4124 | 324.49 | 47700 | 0.9234 |
| 0.413 | 325.17 | 47800 | 0.9347 |
| 0.413 | 325.85 | 47900 | 0.9841 |
| 0.4117 | 326.53 | 48000 | 0.9996 |
| 0.4127 | 327.21 | 48100 | 0.9128 |
| 0.4127 | 327.89 | 48200 | 0.8949 |
| 0.3967 | 328.57 | 48300 | 0.9390 |
| 0.4068 | 329.25 | 48400 | 0.9034 |
| 0.4068 | 329.93 | 48500 | 0.9314 |
| 0.4086 | 330.61 | 48600 | 0.9609 |
| 0.4143 | 331.29 | 48700 | 0.9333 |
| 0.4143 | 331.97 | 48800 | 0.9294 |
| 0.4144 | 332.65 | 48900 | 0.8984 |
| 0.4077 | 333.33 | 49000 | 1.0073 |
| 0.3953 | 334.01 | 49100 | 0.9610 |
| 0.3953 | 334.69 | 49200 | 0.9907 |
| 0.3961 | 335.37 | 49300 | 0.9689 |
| 0.4122 | 336.05 | 49400 | 0.9386 |
| 0.4122 | 336.73 | 49500 | 0.9186 |
| 0.3946 | 337.41 | 49600 | 0.9927 |
| 0.4021 | 338.1 | 49700 | 1.0131 |
| 0.4021 | 338.78 | 49800 | 1.0783 |
| 0.4039 | 339.46 | 49900 | 0.9340 |
| 0.3989 | 340.14 | 50000 | 0.9544 |
| 0.3989 | 340.82 | 50100 | 0.9124 |
| 0.4054 | 341.5 | 50200 | 0.9842 |
| 0.4059 | 342.18 | 50300 | 0.9947 |
| 0.4059 | 342.86 | 50400 | 0.9687 |
| 0.403 | 343.54 | 50500 | 0.9740 |
| 0.3969 | 344.22 | 50600 | 0.9313 |
| 0.3969 | 344.9 | 50700 | 0.9186 |
| 0.4034 | 345.58 | 50800 | 0.9666 |
| 0.4101 | 346.26 | 50900 | 0.8962 |
| 0.4101 | 346.94 | 51000 | 0.9590 |
| 0.4141 | 347.62 | 51100 | 0.9583 |
| 0.4029 | 348.3 | 51200 | 0.9203 |
| 0.4029 | 348.98 | 51300 | 0.9875 |
| 0.4141 | 349.66 | 51400 | 0.9645 |
| 0.3974 | 350.34 | 51500 | 0.9310 |
| 0.3881 | 351.02 | 51600 | 0.9739 |
| 0.3881 | 351.7 | 51700 | 0.9420 |
| 0.384 | 352.38 | 51800 | 0.9549 |
| 0.3902 | 353.06 | 51900 | 0.9647 |
| 0.3902 | 353.74 | 52000 | 0.9604 |
| 0.3846 | 354.42 | 52100 | 0.9756 |
| 0.3906 | 355.1 | 52200 | 0.9419 |
| 0.3906 | 355.78 | 52300 | 0.9461 |
| 0.3712 | 356.46 | 52400 | 0.9420 |
| 0.3769 | 357.14 | 52500 | 0.9315 |
| 0.3769 | 357.82 | 52600 | 0.9119 |
| 0.3896 | 358.5 | 52700 | 0.9798 |
| 0.3787 | 359.18 | 52800 | 0.9941 |
| 0.3787 | 359.86 | 52900 | 0.9364 |
| 0.3903 | 360.54 | 53000 | 0.9152 |
| 0.3823 | 361.22 | 53100 | 0.9817 |
| 0.3823 | 361.9 | 53200 | 0.9087 |
| 0.378 | 362.59 | 53300 | 0.9299 |
| 0.3882 | 363.27 | 53400 | 0.9989 |
| 0.3882 | 363.95 | 53500 | 0.9168 |
| 0.3787 | 364.63 | 53600 | 0.9464 |
| 0.3835 | 365.31 | 53700 | 0.9010 |
| 0.3835 | 365.99 | 53800 | 0.8880 |
| 0.3751 | 366.67 | 53900 | 0.9004 |
| 0.3745 | 367.35 | 54000 | 0.9491 |
| 0.3776 | 368.03 | 54100 | 1.0176 |
| 0.3776 | 368.71 | 54200 | 0.9734 |
| 0.3744 | 369.39 | 54300 | 0.9464 |
| 0.3659 | 370.07 | 54400 | 0.9967 |
| 0.3659 | 370.75 | 54500 | 0.9905 |
| 0.3671 | 371.43 | 54600 | 0.9456 |
| 0.3747 | 372.11 | 54700 | 0.9371 |
| 0.3747 | 372.79 | 54800 | 0.8921 |
| 0.3728 | 373.47 | 54900 | 0.8826 |
| 0.3776 | 374.15 | 55000 | 0.9630 |
| 0.3776 | 374.83 | 55100 | 0.9353 |
| 0.3694 | 375.51 | 55200 | 0.9479 |
| 0.3768 | 376.19 | 55300 | 0.9303 |
| 0.3768 | 376.87 | 55400 | 0.9540 |
| 0.3747 | 377.55 | 55500 | 0.9383 |
| 0.3737 | 378.23 | 55600 | 0.9170 |
| 0.3737 | 378.91 | 55700 | 0.8026 |
| 0.3757 | 379.59 | 55800 | 0.8989 |
| 0.3678 | 380.27 | 55900 | 0.9248 |
| 0.3678 | 380.95 | 56000 | 0.8190 |
| 0.3738 | 381.63 | 56100 | 0.9005 |
| 0.376 | 382.31 | 56200 | 0.8561 |
| 0.376 | 382.99 | 56300 | 0.9408 |
| 0.3714 | 383.67 | 56400 | 0.9226 |
| 0.364 | 384.35 | 56500 | 0.9577 |
| 0.3465 | 385.03 | 56600 | 0.9440 |
| 0.3465 | 385.71 | 56700 | 0.9178 |
| 0.374 | 386.39 | 56800 | 0.9044 |
| 0.3727 | 387.07 | 56900 | 0.8633 |
| 0.3727 | 387.76 | 57000 | 0.9078 |
| 0.3735 | 388.44 | 57100 | 0.9021 |
| 0.3655 | 389.12 | 57200 | 0.9499 |
| 0.3655 | 389.8 | 57300 | 0.9290 |
| 0.3615 | 390.48 | 57400 | 0.8906 |
| 0.3588 | 391.16 | 57500 | 0.8692 |
| 0.3588 | 391.84 | 57600 | 0.8857 |
| 0.3639 | 392.52 | 57700 | 0.9569 |
| 0.3557 | 393.2 | 57800 | 0.9146 |
| 0.3557 | 393.88 | 57900 | 0.9878 |
| 0.3532 | 394.56 | 58000 | 0.8703 |
| 0.3745 | 395.24 | 58100 | 0.8679 |
| 0.3745 | 395.92 | 58200 | 0.8823 |
| 0.3566 | 396.6 | 58300 | 0.9611 |
| 0.3642 | 397.28 | 58400 | 0.9327 |
| 0.3642 | 397.96 | 58500 | 0.8587 |
| 0.3623 | 398.64 | 58600 | 0.8746 |
| 0.3629 | 399.32 | 58700 | 0.9093 |
| 0.3629 | 400.0 | 58800 | 0.8858 |
| 0.354 | 400.68 | 58900 | 0.8902 |
| 0.3487 | 401.36 | 59000 | 0.8693 |
| 0.3467 | 402.04 | 59100 | 1.0825 |
| 0.3467 | 402.72 | 59200 | 0.9697 |
| 0.3517 | 403.4 | 59300 | 0.9169 |
| 0.3696 | 404.08 | 59400 | 0.9237 |
| 0.3696 | 404.76 | 59500 | 0.9033 |
| 0.3629 | 405.44 | 59600 | 0.9062 |
| 0.3559 | 406.12 | 59700 | 0.9159 |
| 0.3559 | 406.8 | 59800 | 0.8730 |
| 0.3603 | 407.48 | 59900 | 0.8732 |
| 0.3676 | 408.16 | 60000 | 0.8897 |
| 0.3676 | 408.84 | 60100 | 0.7334 |
| 0.3584 | 409.52 | 60200 | 0.8494 |
| 0.3449 | 410.2 | 60300 | 0.8944 |
| 0.3449 | 410.88 | 60400 | 0.8014 |
| 0.3513 | 411.56 | 60500 | 0.8673 |
| 0.3497 | 412.24 | 60600 | 0.9071 |
| 0.3497 | 412.93 | 60700 | 0.8574 |
| 0.3582 | 413.61 | 60800 | 0.9135 |
| 0.3555 | 414.29 | 60900 | 0.8723 |
| 0.3555 | 414.97 | 61000 | 0.8772 |
| 0.3453 | 415.65 | 61100 | 0.9041 |
| 0.3451 | 416.33 | 61200 | 0.8647 |
| 0.3499 | 417.01 | 61300 | 0.9790 |
| 0.3499 | 417.69 | 61400 | 0.9859 |
| 0.3502 | 418.37 | 61500 | 0.8996 |
| 0.3534 | 419.05 | 61600 | 0.9446 |
| 0.3534 | 419.73 | 61700 | 0.8965 |
| 0.3388 | 420.41 | 61800 | 0.9314 |
| 0.3441 | 421.09 | 61900 | 0.8792 |
| 0.3441 | 421.77 | 62000 | 0.9422 |
| 0.3443 | 422.45 | 62100 | 0.9591 |
| 0.3681 | 423.13 | 62200 | 0.9265 |
| 0.3681 | 423.81 | 62300 | 0.8872 |
| 0.3557 | 424.49 | 62400 | 0.8528 |
| 0.3553 | 425.17 | 62500 | 0.9701 |
| 0.3553 | 425.85 | 62600 | 0.9512 |
| 0.3523 | 426.53 | 62700 | 0.9026 |
| 0.3467 | 427.21 | 62800 | 0.9087 |
| 0.3467 | 427.89 | 62900 | 1.0169 |
| 0.3521 | 428.57 | 63000 | 0.9314 |
| 0.3411 | 429.25 | 63100 | 0.9291 |
| 0.3411 | 429.93 | 63200 | 0.9567 |
| 0.3469 | 430.61 | 63300 | 0.9458 |
| 0.3449 | 431.29 | 63400 | 0.9337 |
| 0.3449 | 431.97 | 63500 | 0.9503 |
| 0.3369 | 432.65 | 63600 | 0.8987 |
| 0.3384 | 433.33 | 63700 | 0.8578 |
| 0.3265 | 434.01 | 63800 | 0.9543 |
| 0.3265 | 434.69 | 63900 | 0.9231 |
| 0.3356 | 435.37 | 64000 | 0.9121 |
| 0.3388 | 436.05 | 64100 | 0.9279 |
| 0.3388 | 436.73 | 64200 | 0.8939 |
| 0.3351 | 437.41 | 64300 | 0.8934 |
| 0.3386 | 438.1 | 64400 | 0.9469 |
| 0.3386 | 438.78 | 64500 | 0.9149 |
| 0.3439 | 439.46 | 64600 | 0.8963 |
| 0.3381 | 440.14 | 64700 | 0.8653 |
| 0.3381 | 440.82 | 64800 | 0.8633 |
| 0.3339 | 441.5 | 64900 | 0.8783 |
| 0.3242 | 442.18 | 65000 | 0.9143 |
| 0.3242 | 442.86 | 65100 | 0.9553 |
| 0.3271 | 443.54 | 65200 | 0.8563 |
| 0.3281 | 444.22 | 65300 | 0.9003 |
| 0.3281 | 444.9 | 65400 | 0.8555 |
| 0.3367 | 445.58 | 65500 | 0.9146 |
| 0.3228 | 446.26 | 65600 | 0.9052 |
| 0.3228 | 446.94 | 65700 | 0.9237 |
| 0.3328 | 447.62 | 65800 | 0.9128 |
| 0.324 | 448.3 | 65900 | 0.9159 |
| 0.324 | 448.98 | 66000 | 0.8867 |
| 0.3305 | 449.66 | 66100 | 0.9694 |
| 0.3329 | 450.34 | 66200 | 0.9833 |
| 0.3348 | 451.02 | 66300 | 0.9344 |
| 0.3348 | 451.7 | 66400 | 0.9303 |
| 0.321 | 452.38 | 66500 | 0.9275 |
| 0.3335 | 453.06 | 66600 | 0.9419 |
| 0.3335 | 453.74 | 66700 | 0.9502 |
| 0.3189 | 454.42 | 66800 | 0.9341 |
| 0.3386 | 455.1 | 66900 | 0.9404 |
| 0.3386 | 455.78 | 67000 | 0.9660 |
| 0.3273 | 456.46 | 67100 | 0.9323 |
| 0.339 | 457.14 | 67200 | 0.9266 |
| 0.339 | 457.82 | 67300 | 0.9289 |
| 0.3326 | 458.5 | 67400 | 0.9248 |
| 0.3207 | 459.18 | 67500 | 0.9374 |
| 0.3207 | 459.86 | 67600 | 0.8996 |
| 0.3339 | 460.54 | 67700 | 0.9271 |
| 0.3198 | 461.22 | 67800 | 0.9627 |
| 0.3198 | 461.9 | 67900 | 0.9429 |
| 0.3208 | 462.59 | 68000 | 0.9561 |
| 0.3147 | 463.27 | 68100 | 0.8795 |
| 0.3147 | 463.95 | 68200 | 0.8876 |
| 0.3222 | 464.63 | 68300 | 0.9007 |
| 0.3241 | 465.31 | 68400 | 0.9475 |
| 0.3241 | 465.99 | 68500 | 0.9403 |
| 0.3312 | 466.67 | 68600 | 0.9368 |
| 0.3302 | 467.35 | 68700 | 0.8937 |
| 0.3201 | 468.03 | 68800 | 0.9319 |
| 0.3201 | 468.71 | 68900 | 0.9094 |
| 0.3217 | 469.39 | 69000 | 0.9517 |
| 0.3193 | 470.07 | 69100 | 0.8895 |
| 0.3193 | 470.75 | 69200 | 0.9202 |
| 0.3352 | 471.43 | 69300 | 0.9320 |
| 0.3249 | 472.11 | 69400 | 0.9640 |
| 0.3249 | 472.79 | 69500 | 0.9452 |
| 0.3097 | 473.47 | 69600 | 0.9311 |
| 0.327 | 474.15 | 69700 | 0.9392 |
| 0.327 | 474.83 | 69800 | 0.9525 |
| 0.3271 | 475.51 | 69900 | 0.9064 |
| 0.3165 | 476.19 | 70000 | 0.9455 |
| 0.3165 | 476.87 | 70100 | 0.9435 |
| 0.3103 | 477.55 | 70200 | 0.8891 |
| 0.3189 | 478.23 | 70300 | 0.9199 |
| 0.3189 | 478.91 | 70400 | 0.9362 |
| 0.3264 | 479.59 | 70500 | 0.9289 |
| 0.313 | 480.27 | 70600 | 0.9246 |
| 0.313 | 480.95 | 70700 | 0.9549 |
| 0.3289 | 481.63 | 70800 | 0.9513 |
| 0.3189 | 482.31 | 70900 | 0.9798 |
| 0.3189 | 482.99 | 71000 | 0.9027 |
| 0.3177 | 483.67 | 71100 | 0.8823 |
| 0.3219 | 484.35 | 71200 | 0.9269 |
| 0.3175 | 485.03 | 71300 | 0.8984 |
| 0.3175 | 485.71 | 71400 | 0.8696 |
| 0.3167 | 486.39 | 71500 | 0.8722 |
| 0.318 | 487.07 | 71600 | 0.8909 |
| 0.318 | 487.76 | 71700 | 0.8783 |
| 0.3128 | 488.44 | 71800 | 0.8144 |
| 0.315 | 489.12 | 71900 | 0.8250 |
| 0.315 | 489.8 | 72000 | 0.8791 |
| 0.3085 | 490.48 | 72100 | 0.9192 |
| 0.3081 | 491.16 | 72200 | 0.8403 |
| 0.3081 | 491.84 | 72300 | 0.9223 |
| 0.31 | 492.52 | 72400 | 0.8974 |
| 0.3054 | 493.2 | 72500 | 0.9169 |
| 0.3054 | 493.88 | 72600 | 0.8845 |
| 0.3134 | 494.56 | 72700 | 0.9554 |
| 0.3083 | 495.24 | 72800 | 0.9337 |
| 0.3083 | 495.92 | 72900 | 0.9209 |
| 0.3028 | 496.6 | 73000 | 0.9142 |
| 0.3016 | 497.28 | 73100 | 0.9345 |
| 0.3016 | 497.96 | 73200 | 0.9100 |
| 0.3075 | 498.64 | 73300 | 0.8989 |
| 0.3105 | 499.32 | 73400 | 0.8598 |
| 0.3105 | 500.0 | 73500 | 0.9177 |
| 0.3059 | 500.68 | 73600 | 0.9242 |
| 0.3018 | 501.36 | 73700 | 0.9403 |
| 0.3159 | 502.04 | 73800 | 0.9011 |
| 0.3159 | 502.72 | 73900 | 0.9442 |
| 0.2996 | 503.4 | 74000 | 0.9575 |
| 0.3016 | 504.08 | 74100 | 0.9119 |
| 0.3016 | 504.76 | 74200 | 0.9072 |
| 0.3072 | 505.44 | 74300 | 0.9389 |
| 0.3042 | 506.12 | 74400 | 0.9038 |
| 0.3042 | 506.8 | 74500 | 0.8814 |
| 0.3142 | 507.48 | 74600 | 0.9452 |
| 0.3099 | 508.16 | 74700 | 0.9395 |
| 0.3099 | 508.84 | 74800 | 0.9604 |
| 0.3081 | 509.52 | 74900 | 0.9176 |
| 0.3175 | 510.2 | 75000 | 0.8799 |
| 0.3175 | 510.88 | 75100 | 0.8732 |
| 0.3052 | 511.56 | 75200 | 0.8323 |
| 0.2961 | 512.24 | 75300 | 0.8956 |
| 0.2961 | 512.93 | 75400 | 0.8629 |
| 0.3012 | 513.61 | 75500 | 0.8523 |
| 0.2999 | 514.29 | 75600 | 0.8276 |
| 0.2999 | 514.97 | 75700 | 0.9008 |
| 0.298 | 515.65 | 75800 | 0.8051 |
| 0.2968 | 516.33 | 75900 | 0.8240 |
| 0.2907 | 517.01 | 76000 | 0.9271 |
| 0.2907 | 517.69 | 76100 | 0.8934 |
| 0.2859 | 518.37 | 76200 | 0.9044 |
| 0.306 | 519.05 | 76300 | 0.8994 |
| 0.306 | 519.73 | 76400 | 0.8539 |
| 0.2947 | 520.41 | 76500 | 0.9063 |
| 0.2977 | 521.09 | 76600 | 0.9074 |
| 0.2977 | 521.77 | 76700 | 0.9297 |
| 0.2991 | 522.45 | 76800 | 0.9109 |
| 0.3013 | 523.13 | 76900 | 0.9491 |
| 0.3013 | 523.81 | 77000 | 0.8518 |
| 0.3 | 524.49 | 77100 | 0.9199 |
| 0.3009 | 525.17 | 77200 | 0.9277 |
| 0.3009 | 525.85 | 77300 | 0.9617 |
| 0.3054 | 526.53 | 77400 | 0.9254 |
| 0.2994 | 527.21 | 77500 | 0.8886 |
| 0.2994 | 527.89 | 77600 | 0.8579 |
| 0.2957 | 528.57 | 77700 | 0.9694 |
| 0.3082 | 529.25 | 77800 | 0.9411 |
| 0.3082 | 529.93 | 77900 | 0.8823 |
| 0.2928 | 530.61 | 78000 | 0.8684 |
| 0.2936 | 531.29 | 78100 | 0.9942 |
| 0.2936 | 531.97 | 78200 | 0.8861 |
| 0.2964 | 532.65 | 78300 | 0.8939 |
| 0.2914 | 533.33 | 78400 | 0.9633 |
| 0.2928 | 534.01 | 78500 | 0.8713 |
| 0.2928 | 534.69 | 78600 | 0.8938 |
| 0.2909 | 535.37 | 78700 | 0.8905 |
| 0.2966 | 536.05 | 78800 | 0.9006 |
| 0.2966 | 536.73 | 78900 | 0.9431 |
| 0.2886 | 537.41 | 79000 | 0.9343 |
| 0.2922 | 538.1 | 79100 | 0.9032 |
| 0.2922 | 538.78 | 79200 | 0.9507 |
| 0.2817 | 539.46 | 79300 | 0.9199 |
| 0.2917 | 540.14 | 79400 | 0.9156 |
| 0.2917 | 540.82 | 79500 | 0.9175 |
| 0.29 | 541.5 | 79600 | 0.9104 |
| 0.291 | 542.18 | 79700 | 0.9223 |
| 0.291 | 542.86 | 79800 | 0.9622 |
| 0.3055 | 543.54 | 79900 | 0.8998 |
| 0.2842 | 544.22 | 80000 | 0.9216 |
| 0.2842 | 544.9 | 80100 | 0.9475 |
| 0.2952 | 545.58 | 80200 | 0.9345 |
| 0.278 | 546.26 | 80300 | 0.9923 |
| 0.278 | 546.94 | 80400 | 0.9217 |
| 0.2882 | 547.62 | 80500 | 0.9385 |
| 0.286 | 548.3 | 80600 | 0.9422 |
| 0.286 | 548.98 | 80700 | 0.9100 |
| 0.2828 | 549.66 | 80800 | 0.9751 |
| 0.2903 | 550.34 | 80900 | 0.9360 |
| 0.2803 | 551.02 | 81000 | 0.9827 |
| 0.2803 | 551.7 | 81100 | 0.9771 |
| 0.282 | 552.38 | 81200 | 1.0085 |
| 0.2901 | 553.06 | 81300 | 0.9342 |
| 0.2901 | 553.74 | 81400 | 1.0034 |
| 0.2822 | 554.42 | 81500 | 0.9586 |
| 0.281 | 555.1 | 81600 | 0.9590 |
| 0.281 | 555.78 | 81700 | 0.9488 |
| 0.2824 | 556.46 | 81800 | 0.9709 |
| 0.287 | 557.14 | 81900 | 0.9507 |
| 0.287 | 557.82 | 82000 | 0.9429 |
| 0.2873 | 558.5 | 82100 | 0.9334 |
| 0.2806 | 559.18 | 82200 | 0.9271 |
| 0.2806 | 559.86 | 82300 | 0.9470 |
| 0.2892 | 560.54 | 82400 | 0.9602 |
| 0.2772 | 561.22 | 82500 | 0.9843 |
| 0.2772 | 561.9 | 82600 | 0.9335 |
| 0.2881 | 562.59 | 82700 | 0.9451 |
| 0.2816 | 563.27 | 82800 | 0.9621 |
| 0.2816 | 563.95 | 82900 | 0.9989 |
| 0.2813 | 564.63 | 83000 | 0.9163 |
| 0.2804 | 565.31 | 83100 | 0.9638 |
| 0.2804 | 565.99 | 83200 | 0.9520 |
| 0.2748 | 566.67 | 83300 | 0.9263 |
| 0.2795 | 567.35 | 83400 | 0.9293 |
| 0.2804 | 568.03 | 83500 | 0.9620 |
| 0.2804 | 568.71 | 83600 | 0.9169 |
| 0.2741 | 569.39 | 83700 | 0.9286 |
| 0.2718 | 570.07 | 83800 | 0.9334 |
| 0.2718 | 570.75 | 83900 | 0.9654 |
| 0.2782 | 571.43 | 84000 | 0.9761 |
| 0.2843 | 572.11 | 84100 | 0.9883 |
| 0.2843 | 572.79 | 84200 | 0.9993 |
| 0.2805 | 573.47 | 84300 | 0.9312 |
| 0.2793 | 574.15 | 84400 | 0.9932 |
| 0.2793 | 574.83 | 84500 | 0.9828 |
| 0.2722 | 575.51 | 84600 | 0.9558 |
| 0.273 | 576.19 | 84700 | 0.9739 |
| 0.273 | 576.87 | 84800 | 0.9193 |
| 0.2706 | 577.55 | 84900 | 0.9511 |
| 0.2745 | 578.23 | 85000 | 0.9054 |
| 0.2745 | 578.91 | 85100 | 0.9574 |
| 0.2715 | 579.59 | 85200 | 0.9881 |
| 0.2715 | 580.27 | 85300 | 0.9603 |
| 0.2715 | 580.95 | 85400 | 1.0218 |
| 0.2789 | 581.63 | 85500 | 0.9076 |
| 0.274 | 582.31 | 85600 | 0.9393 |
| 0.274 | 582.99 | 85700 | 0.8968 |
| 0.2762 | 583.67 | 85800 | 0.9474 |
| 0.2767 | 584.35 | 85900 | 0.9883 |
| 0.2688 | 585.03 | 86000 | 0.9717 |
| 0.2688 | 585.71 | 86100 | 1.0013 |
| 0.2706 | 586.39 | 86200 | 0.9569 |
| 0.2739 | 587.07 | 86300 | 0.9369 |
| 0.2739 | 587.76 | 86400 | 0.8882 |
| 0.2716 | 588.44 | 86500 | 0.9189 |
| 0.2693 | 589.12 | 86600 | 0.9402 |
| 0.2693 | 589.8 | 86700 | 0.9262 |
| 0.2667 | 590.48 | 86800 | 0.9782 |
| 0.268 | 591.16 | 86900 | 0.9457 |
| 0.268 | 591.84 | 87000 | 0.9509 |
| 0.2726 | 592.52 | 87100 | 0.9320 |
| 0.275 | 593.2 | 87200 | 0.9357 |
| 0.275 | 593.88 | 87300 | 0.9786 |
| 0.2673 | 594.56 | 87400 | 0.9770 |
| 0.2684 | 595.24 | 87500 | 0.9389 |
| 0.2684 | 595.92 | 87600 | 0.9558 |
| 0.2664 | 596.6 | 87700 | 0.9698 |
| 0.2691 | 597.28 | 87800 | 1.0059 |
| 0.2691 | 597.96 | 87900 | 0.9660 |
| 0.2753 | 598.64 | 88000 | 0.9761 |
| 0.2547 | 599.32 | 88100 | 0.9627 |
| 0.2547 | 600.0 | 88200 | 0.9621 |
| 0.2691 | 600.68 | 88300 | 0.9752 |
| 0.266 | 601.36 | 88400 | 0.9677 |
| 0.2675 | 602.04 | 88500 | 0.9663 |
| 0.2675 | 602.72 | 88600 | 0.9749 |
| 0.2747 | 603.4 | 88700 | 0.9452 |
| 0.2674 | 604.08 | 88800 | 0.9587 |
| 0.2674 | 604.76 | 88900 | 0.9693 |
| 0.2801 | 605.44 | 89000 | 0.9513 |
| 0.2722 | 606.12 | 89100 | 0.9783 |
| 0.2722 | 606.8 | 89200 | 0.9452 |
| 0.2731 | 607.48 | 89300 | 0.9678 |
| 0.2723 | 608.16 | 89400 | 0.9786 |
| 0.2723 | 608.84 | 89500 | 0.9852 |
| 0.2651 | 609.52 | 89600 | 0.9570 |
| 0.2811 | 610.2 | 89700 | 0.9567 |
| 0.2811 | 610.88 | 89800 | 0.9049 |
| 0.2688 | 611.56 | 89900 | 0.9634 |
| 0.2624 | 612.24 | 90000 | 0.8975 |
| 0.2624 | 612.93 | 90100 | 0.9899 |
| 0.2616 | 613.61 | 90200 | 0.9626 |
| 0.2603 | 614.29 | 90300 | 0.9310 |
| 0.2603 | 614.97 | 90400 | 0.9788 |
| 0.2721 | 615.65 | 90500 | 0.9413 |
| 0.2622 | 616.33 | 90600 | 0.9807 |
| 0.2683 | 617.01 | 90700 | 0.9218 |
| 0.2683 | 617.69 | 90800 | 0.9893 |
| 0.2573 | 618.37 | 90900 | 0.9086 |
| 0.2654 | 619.05 | 91000 | 0.9373 |
| 0.2654 | 619.73 | 91100 | 0.9583 |
| 0.2647 | 620.41 | 91200 | 0.9232 |
| 0.2616 | 621.09 | 91300 | 0.9738 |
| 0.2616 | 621.77 | 91400 | 0.9405 |
| 0.258 | 622.45 | 91500 | 0.9601 |
| 0.2632 | 623.13 | 91600 | 0.9567 |
| 0.2632 | 623.81 | 91700 | 0.9362 |
| 0.2636 | 624.49 | 91800 | 0.9496 |
| 0.2636 | 625.17 | 91900 | 1.0030 |
| 0.2636 | 625.85 | 92000 | 0.9785 |
| 0.2454 | 626.53 | 92100 | 0.9485 |
| 0.2533 | 627.21 | 92200 | 0.9630 |
| 0.2533 | 627.89 | 92300 | 0.9709 |
| 0.2596 | 628.57 | 92400 | 0.9479 |
| 0.256 | 629.25 | 92500 | 0.9214 |
| 0.256 | 629.93 | 92600 | 0.9570 |
| 0.255 | 630.61 | 92700 | 0.9472 |
| 0.2613 | 631.29 | 92800 | 0.9457 |
| 0.2613 | 631.97 | 92900 | 0.9615 |
| 0.2703 | 632.65 | 93000 | 0.9583 |
| 0.2582 | 633.33 | 93100 | 0.9601 |
| 0.2634 | 634.01 | 93200 | 0.9444 |
| 0.2634 | 634.69 | 93300 | 0.9499 |
| 0.259 | 635.37 | 93400 | 0.9512 |
| 0.2617 | 636.05 | 93500 | 0.9543 |
| 0.2617 | 636.73 | 93600 | 0.9303 |
| 0.2611 | 637.41 | 93700 | 0.9388 |
| 0.2513 | 638.1 | 93800 | 0.9443 |
| 0.2513 | 638.78 | 93900 | 0.9276 |
| 0.2571 | 639.46 | 94000 | 0.9073 |
| 0.2636 | 640.14 | 94100 | 0.9122 |
| 0.2636 | 640.82 | 94200 | 0.9132 |
| 0.2673 | 641.5 | 94300 | 0.9055 |
| 0.2594 | 642.18 | 94400 | 0.9299 |
| 0.2594 | 642.86 | 94500 | 0.9161 |
| 0.2552 | 643.54 | 94600 | 0.9347 |
| 0.254 | 644.22 | 94700 | 0.9239 |
| 0.254 | 644.9 | 94800 | 0.9454 |
| 0.2522 | 645.58 | 94900 | 0.9481 |
| 0.2556 | 646.26 | 95000 | 0.9153 |
| 0.2556 | 646.94 | 95100 | 0.9141 |
| 0.2583 | 647.62 | 95200 | 0.9280 |
| 0.2645 | 648.3 | 95300 | 0.9218 |
| 0.2645 | 648.98 | 95400 | 0.9603 |
| 0.2512 | 649.66 | 95500 | 0.9017 |
| 0.2602 | 650.34 | 95600 | 0.9101 |
| 0.255 | 651.02 | 95700 | 0.9184 |
| 0.255 | 651.7 | 95800 | 0.9234 |
| 0.2547 | 652.38 | 95900 | 0.9194 |
| 0.2546 | 653.06 | 96000 | 0.9825 |
| 0.2546 | 653.74 | 96100 | 0.9515 |
| 0.2526 | 654.42 | 96200 | 0.9067 |
| 0.261 | 655.1 | 96300 | 0.9282 |
| 0.261 | 655.78 | 96400 | 0.9561 |
| 0.2545 | 656.46 | 96500 | 0.9466 |
| 0.2509 | 657.14 | 96600 | 0.9294 |
| 0.2509 | 657.82 | 96700 | 0.9114 |
| 0.2503 | 658.5 | 96800 | 1.0040 |
| 0.2482 | 659.18 | 96900 | 0.9106 |
| 0.2482 | 659.86 | 97000 | 0.9159 |
| 0.2523 | 660.54 | 97100 | 0.9490 |
| 0.2528 | 661.22 | 97200 | 0.9538 |
| 0.2528 | 661.9 | 97300 | 0.9570 |
| 0.2455 | 662.59 | 97400 | 0.8882 |
| 0.2502 | 663.27 | 97500 | 0.9164 |
| 0.2502 | 663.95 | 97600 | 0.9269 |
| 0.2465 | 664.63 | 97700 | 0.9628 |
| 0.2524 | 665.31 | 97800 | 0.8976 |
| 0.2524 | 665.99 | 97900 | 0.9017 |
| 0.2479 | 666.67 | 98000 | 0.9197 |
| 0.249 | 667.35 | 98100 | 0.9282 |
| 0.2533 | 668.03 | 98200 | 0.9342 |
| 0.2533 | 668.71 | 98300 | 0.9494 |
| 0.2501 | 669.39 | 98400 | 0.9430 |
| 0.2444 | 670.07 | 98500 | 0.9252 |
| 0.2444 | 670.75 | 98600 | 0.9799 |
| 0.243 | 671.43 | 98700 | 0.9195 |
| 0.249 | 672.11 | 98800 | 0.9142 |
| 0.249 | 672.79 | 98900 | 0.9553 |
| 0.2528 | 673.47 | 99000 | 0.9196 |
| 0.244 | 674.15 | 99100 | 0.9640 |
| 0.244 | 674.83 | 99200 | 0.9809 |
| 0.2462 | 675.51 | 99300 | 0.9868 |
| 0.247 | 676.19 | 99400 | 0.9640 |
| 0.247 | 676.87 | 99500 | 0.9228 |
| 0.2615 | 677.55 | 99600 | 0.9172 |
| 0.2487 | 678.23 | 99700 | 0.9166 |
| 0.2487 | 678.91 | 99800 | 0.8928 |
| 0.242 | 679.59 | 99900 | 0.8830 |
| 0.2448 | 680.27 | 100000 | 0.9209 |
| 0.2448 | 680.95 | 100100 | 0.9139 |
| 0.2488 | 681.63 | 100200 | 0.8970 |
| 0.2504 | 682.31 | 100300 | 0.9254 |
| 0.2504 | 682.99 | 100400 | 0.9437 |
| 0.2381 | 683.67 | 100500 | 0.9419 |
| 0.245 | 684.35 | 100600 | 0.9379 |
| 0.2452 | 685.03 | 100700 | 0.9465 |
| 0.2452 | 685.71 | 100800 | 0.9626 |
| 0.2482 | 686.39 | 100900 | 0.9472 |
| 0.2456 | 687.07 | 101000 | 0.9434 |
| 0.2456 | 687.76 | 101100 | 0.9426 |
| 0.2388 | 688.44 | 101200 | 0.9440 |
| 0.2496 | 689.12 | 101300 | 0.9311 |
| 0.2496 | 689.8 | 101400 | 0.9338 |
| 0.2399 | 690.48 | 101500 | 0.9290 |
| 0.2427 | 691.16 | 101600 | 0.9347 |
| 0.2427 | 691.84 | 101700 | 0.9197 |
| 0.2709 | 692.52 | 101800 | 0.9046 |
| 0.2474 | 693.2 | 101900 | 0.9455 |
| 0.2474 | 693.88 | 102000 | 0.9212 |
| 0.2411 | 694.56 | 102100 | 0.9508 |
| 0.242 | 695.24 | 102200 | 0.9558 |
| 0.242 | 695.92 | 102300 | 0.9846 |
| 0.2443 | 696.6 | 102400 | 0.9656 |
| 0.2356 | 697.28 | 102500 | 0.9428 |
| 0.2356 | 697.96 | 102600 | 0.9238 |
| 0.2422 | 698.64 | 102700 | 0.9156 |
| 0.2341 | 699.32 | 102800 | 0.9324 |
| 0.2341 | 700.0 | 102900 | 0.9372 |
| 0.2382 | 700.68 | 103000 | 0.9374 |
| 0.2407 | 701.36 | 103100 | 0.9342 |
| 0.2427 | 702.04 | 103200 | 0.9400 |
| 0.2427 | 702.72 | 103300 | 0.9451 |
| 0.2373 | 703.4 | 103400 | 0.9355 |
| 0.2439 | 704.08 | 103500 | 0.9281 |
| 0.2439 | 704.76 | 103600 | 0.9282 |
| 0.247 | 705.44 | 103700 | 0.9186 |
| 0.2391 | 706.12 | 103800 | 0.8933 |
| 0.2391 | 706.8 | 103900 | 0.9392 |
| 0.2467 | 707.48 | 104000 | 0.9764 |
| 0.238 | 708.16 | 104100 | 0.9495 |
| 0.238 | 708.84 | 104200 | 0.9409 |
| 0.2436 | 709.52 | 104300 | 0.9296 |
| 0.2396 | 710.2 | 104400 | 0.9472 |
| 0.2396 | 710.88 | 104500 | 0.9574 |
| 0.2476 | 711.56 | 104600 | 0.9231 |
| 0.2397 | 712.24 | 104700 | 0.8930 |
| 0.2397 | 712.93 | 104800 | 0.9173 |
| 0.2448 | 713.61 | 104900 | 0.9187 |
| 0.2448 | 714.29 | 105000 | 0.9194 |
| 0.2448 | 714.97 | 105100 | 0.9242 |
| 0.2365 | 715.65 | 105200 | 0.9254 |
| 0.2374 | 716.33 | 105300 | 0.8915 |
| 0.2417 | 717.01 | 105400 | 0.9117 |
| 0.2417 | 717.69 | 105500 | 0.9284 |
| 0.2355 | 718.37 | 105600 | 0.9527 |
| 0.2344 | 719.05 | 105700 | 0.9486 |
| 0.2344 | 719.73 | 105800 | 0.9683 |
| 0.2387 | 720.41 | 105900 | 0.9552 |
| 0.2395 | 721.09 | 106000 | 0.9223 |
| 0.2395 | 721.77 | 106100 | 0.9092 |
| 0.2433 | 722.45 | 106200 | 0.9380 |
| 0.2353 | 723.13 | 106300 | 0.9535 |
| 0.2353 | 723.81 | 106400 | 0.9584 |
| 0.2375 | 724.49 | 106500 | 0.9346 |
| 0.2325 | 725.17 | 106600 | 0.9275 |
| 0.2325 | 725.85 | 106700 | 0.9382 |
| 0.2335 | 726.53 | 106800 | 0.9199 |
| 0.234 | 727.21 | 106900 | 0.9580 |
| 0.234 | 727.89 | 107000 | 0.9703 |
| 0.2324 | 728.57 | 107100 | 0.9901 |
| 0.2303 | 729.25 | 107200 | 0.9820 |
| 0.2303 | 729.93 | 107300 | 0.9646 |
| 0.2291 | 730.61 | 107400 | 0.9548 |
| 0.2415 | 731.29 | 107500 | 0.9346 |
| 0.2415 | 731.97 | 107600 | 0.9242 |
| 0.2385 | 732.65 | 107700 | 0.9535 |
| 0.2324 | 733.33 | 107800 | 0.9393 |
| 0.233 | 734.01 | 107900 | 0.9851 |
| 0.233 | 734.69 | 108000 | 0.9890 |
| 0.2324 | 735.37 | 108100 | 0.9940 |
| 0.2332 | 736.05 | 108200 | 0.9818 |
| 0.2332 | 736.73 | 108300 | 0.9922 |
| 0.2317 | 737.41 | 108400 | 0.9977 |
| 0.2343 | 738.1 | 108500 | 0.9593 |
| 0.2343 | 738.78 | 108600 | 0.9995 |
| 0.2288 | 739.46 | 108700 | 1.0020 |
| 0.2291 | 740.14 | 108800 | 0.9864 |
| 0.2291 | 740.82 | 108900 | 0.9740 |
| 0.2308 | 741.5 | 109000 | 0.9993 |
| 0.2347 | 742.18 | 109100 | 1.0078 |
| 0.2347 | 742.86 | 109200 | 0.9762 |
| 0.2309 | 743.54 | 109300 | 0.9776 |
| 0.2264 | 744.22 | 109400 | 0.9629 |
| 0.2264 | 744.9 | 109500 | 0.9830 |
| 0.2312 | 745.58 | 109600 | 0.9737 |
| 0.2253 | 746.26 | 109700 | 1.0124 |
| 0.2253 | 746.94 | 109800 | 1.0121 |
| 0.2275 | 747.62 | 109900 | 0.9924 |
| 0.2331 | 748.3 | 110000 | 0.9456 |
| 0.2331 | 748.98 | 110100 | 0.9486 |
| 0.2275 | 749.66 | 110200 | 0.9388 |
| 0.2303 | 750.34 | 110300 | 0.9762 |
| 0.2322 | 751.02 | 110400 | 0.9643 |
| 0.2322 | 751.7 | 110500 | 0.9878 |
| 0.232 | 752.38 | 110600 | 0.9780 |
| 0.2348 | 753.06 | 110700 | 0.9774 |
| 0.2348 | 753.74 | 110800 | 1.0018 |
| 0.2312 | 754.42 | 110900 | 0.9684 |
| 0.2304 | 755.1 | 111000 | 0.9828 |
| 0.2304 | 755.78 | 111100 | 0.9591 |
| 0.2412 | 756.46 | 111200 | 0.9862 |
| 0.2313 | 757.14 | 111300 | 0.9796 |
| 0.2313 | 757.82 | 111400 | 0.9653 |
| 0.2309 | 758.5 | 111500 | 0.9666 |
| 0.2293 | 759.18 | 111600 | 1.0382 |
| 0.2293 | 759.86 | 111700 | 1.0208 |
| 0.235 | 760.54 | 111800 | 1.0372 |
| 0.2337 | 761.22 | 111900 | 1.0057 |
| 0.2337 | 761.9 | 112000 | 1.0245 |
| 0.2309 | 762.59 | 112100 | 0.9766 |
| 0.2275 | 763.27 | 112200 | 0.9449 |
| 0.2275 | 763.95 | 112300 | 0.9659 |
| 0.2263 | 764.63 | 112400 | 0.9614 |
| 0.2325 | 765.31 | 112500 | 0.9605 |
| 0.2325 | 765.99 | 112600 | 0.9494 |
| 0.2292 | 766.67 | 112700 | 0.9632 |
| 0.2246 | 767.35 | 112800 | 0.9762 |
| 0.2268 | 768.03 | 112900 | 0.9754 |
| 0.2268 | 768.71 | 113000 | 0.9704 |
| 0.2274 | 769.39 | 113100 | 0.9722 |
| 0.2234 | 770.07 | 113200 | 0.9678 |
| 0.2234 | 770.75 | 113300 | 0.9736 |
| 0.2238 | 771.43 | 113400 | 1.0298 |
| 0.2242 | 772.11 | 113500 | 0.9642 |
| 0.2242 | 772.79 | 113600 | 0.9844 |
| 0.2257 | 773.47 | 113700 | 0.9649 |
| 0.225 | 774.15 | 113800 | 0.9992 |
| 0.225 | 774.83 | 113900 | 0.9868 |
| 0.2262 | 775.51 | 114000 | 1.0092 |
| 0.2266 | 776.19 | 114100 | 0.9961 |
| 0.2266 | 776.87 | 114200 | 0.9714 |
| 0.2314 | 777.55 | 114300 | 0.9864 |
| 0.217 | 778.23 | 114400 | 0.9824 |
| 0.217 | 778.91 | 114500 | 0.9910 |
| 0.2248 | 779.59 | 114600 | 0.9945 |
| 0.223 | 780.27 | 114700 | 0.9858 |
| 0.223 | 780.95 | 114800 | 0.9657 |
| 0.2312 | 781.63 | 114900 | 1.0191 |
| 0.2223 | 782.31 | 115000 | 1.0089 |
| 0.2223 | 782.99 | 115100 | 1.0103 |
| 0.2222 | 783.67 | 115200 | 1.0265 |
| 0.2231 | 784.35 | 115300 | 1.0014 |
| 0.2276 | 785.03 | 115400 | 0.9888 |
| 0.2276 | 785.71 | 115500 | 0.9721 |
| 0.222 | 786.39 | 115600 | 0.9885 |
| 0.2142 | 787.07 | 115700 | 0.9856 |
| 0.2142 | 787.76 | 115800 | 0.9973 |
| 0.2208 | 788.44 | 115900 | 0.9472 |
| 0.223 | 789.12 | 116000 | 0.9729 |
| 0.223 | 789.8 | 116100 | 0.9979 |
| 0.2207 | 790.48 | 116200 | 0.9717 |
| 0.2329 | 791.16 | 116300 | 0.9832 |
| 0.2329 | 791.84 | 116400 | 0.9535 |
| 0.2174 | 792.52 | 116500 | 0.9792 |
| 0.219 | 793.2 | 116600 | 0.9819 |
| 0.219 | 793.88 | 116700 | 1.0191 |
| 0.2262 | 794.56 | 116800 | 1.0070 |
| 0.2202 | 795.24 | 116900 | 0.9743 |
| 0.2202 | 795.92 | 117000 | 0.9888 |
| 0.2205 | 796.6 | 117100 | 0.9719 |
| 0.2217 | 797.28 | 117200 | 0.9671 |
| 0.2217 | 797.96 | 117300 | 0.9480 |
| 0.226 | 798.64 | 117400 | 0.9839 |
| 0.2181 | 799.32 | 117500 | 0.9551 |
| 0.2181 | 800.0 | 117600 | 0.9727 |
| 0.2178 | 800.68 | 117700 | 0.9849 |
| 0.2226 | 801.36 | 117800 | 0.9799 |
| 0.2151 | 802.04 | 117900 | 0.9489 |
| 0.2151 | 802.72 | 118000 | 0.9519 |
| 0.2284 | 803.4 | 118100 | 0.9786 |
| 0.2168 | 804.08 | 118200 | 0.9589 |
| 0.2168 | 804.76 | 118300 | 0.9683 |
| 0.2161 | 805.44 | 118400 | 0.9861 |
| 0.2113 | 806.12 | 118500 | 0.9648 |
| 0.2113 | 806.8 | 118600 | 0.9970 |
| 0.2201 | 807.48 | 118700 | 0.9777 |
| 0.2105 | 808.16 | 118800 | 0.9693 |
| 0.2105 | 808.84 | 118900 | 0.9831 |
| 0.2139 | 809.52 | 119000 | 0.9316 |
| 0.2263 | 810.2 | 119100 | 0.9245 |
| 0.2263 | 810.88 | 119200 | 0.9254 |
| 0.2275 | 811.56 | 119300 | 0.9750 |
| 0.2133 | 812.24 | 119400 | 0.9973 |
| 0.2133 | 812.93 | 119500 | 0.9579 |
| 0.2132 | 813.61 | 119600 | 0.9847 |
| 0.2167 | 814.29 | 119700 | 0.9638 |
| 0.2167 | 814.97 | 119800 | 0.9713 |
| 0.2161 | 815.65 | 119900 | 0.9488 |
| 0.2224 | 816.33 | 120000 | 1.0207 |
| 0.215 | 817.01 | 120100 | 0.9745 |
| 0.215 | 817.69 | 120200 | 0.9800 |
| 0.2142 | 818.37 | 120300 | 0.9843 |
| 0.2146 | 819.05 | 120400 | 0.9693 |
| 0.2146 | 819.73 | 120500 | 0.9966 |
| 0.2169 | 820.41 | 120600 | 0.9695 |
| 0.2137 | 821.09 | 120700 | 0.9613 |
| 0.2137 | 821.77 | 120800 | 0.9962 |
| 0.2141 | 822.45 | 120900 | 0.9930 |
| 0.2185 | 823.13 | 121000 | 0.9766 |
| 0.2185 | 823.81 | 121100 | 0.9663 |
| 0.2104 | 824.49 | 121200 | 0.9545 |
| 0.2167 | 825.17 | 121300 | 0.9401 |
| 0.2167 | 825.85 | 121400 | 0.9651 |
| 0.2123 | 826.53 | 121500 | 0.9568 |
| 0.2174 | 827.21 | 121600 | 0.9756 |
| 0.2174 | 827.89 | 121700 | 0.9679 |
| 0.2195 | 828.57 | 121800 | 0.9835 |
| 0.2204 | 829.25 | 121900 | 0.9675 |
| 0.2204 | 829.93 | 122000 | 0.9839 |
| 0.2139 | 830.61 | 122100 | 0.9765 |
| 0.2218 | 831.29 | 122200 | 0.9590 |
| 0.2218 | 831.97 | 122300 | 0.9659 |
| 0.2178 | 832.65 | 122400 | 0.9701 |
| 0.2113 | 833.33 | 122500 | 0.9306 |
| 0.2159 | 834.01 | 122600 | 0.9616 |
| 0.2159 | 834.69 | 122700 | 0.9466 |
| 0.2158 | 835.37 | 122800 | 0.9510 |
| 0.2145 | 836.05 | 122900 | 0.9692 |
| 0.2145 | 836.73 | 123000 | 0.9628 |
| 0.2117 | 837.41 | 123100 | 0.9403 |
| 0.2118 | 838.1 | 123200 | 0.9518 |
| 0.2118 | 838.78 | 123300 | 0.9710 |
| 0.2114 | 839.46 | 123400 | 0.9493 |
| 0.2141 | 840.14 | 123500 | 0.9499 |
| 0.2141 | 840.82 | 123600 | 0.9426 |
| 0.2091 | 841.5 | 123700 | 0.9513 |
| 0.2104 | 842.18 | 123800 | 0.9460 |
| 0.2104 | 842.86 | 123900 | 0.9268 |
| 0.2076 | 843.54 | 124000 | 0.9714 |
| 0.2069 | 844.22 | 124100 | 0.9622 |
| 0.2069 | 844.9 | 124200 | 0.9883 |
| 0.2093 | 845.58 | 124300 | 0.9668 |
| 0.2098 | 846.26 | 124400 | 0.9509 |
| 0.2098 | 846.94 | 124500 | 0.9675 |
| 0.2106 | 847.62 | 124600 | 0.9406 |
| 0.2176 | 848.3 | 124700 | 0.9220 |
| 0.2176 | 848.98 | 124800 | 0.9003 |
| 0.2068 | 849.66 | 124900 | 0.9253 |
| 0.2101 | 850.34 | 125000 | 0.8712 |
| 0.2164 | 851.02 | 125100 | 0.9273 |
| 0.2164 | 851.7 | 125200 | 0.9093 |
| 0.214 | 852.38 | 125300 | 0.9479 |
| 0.2191 | 853.06 | 125400 | 0.9132 |
| 0.2191 | 853.74 | 125500 | 0.9244 |
| 0.2205 | 854.42 | 125600 | 0.9187 |
| 0.2082 | 855.1 | 125700 | 0.9112 |
| 0.2082 | 855.78 | 125800 | 0.9785 |
| 0.206 | 856.46 | 125900 | 1.0037 |
| 0.203 | 857.14 | 126000 | 1.0003 |
| 0.203 | 857.82 | 126100 | 0.9682 |
| 0.2121 | 858.5 | 126200 | 0.9759 |
| 0.2079 | 859.18 | 126300 | 0.9583 |
| 0.2079 | 859.86 | 126400 | 0.9627 |
| 0.2064 | 860.54 | 126500 | 0.9796 |
| 0.2132 | 861.22 | 126600 | 0.9863 |
| 0.2132 | 861.9 | 126700 | 0.9890 |
| 0.2132 | 862.59 | 126800 | 1.0000 |
| 0.2108 | 863.27 | 126900 | 0.9936 |
| 0.2108 | 863.95 | 127000 | 0.9510 |
| 0.2075 | 864.63 | 127100 | 0.9674 |
| 0.2081 | 865.31 | 127200 | 0.9562 |
| 0.2081 | 865.99 | 127300 | 0.9576 |
| 0.2165 | 866.67 | 127400 | 0.9516 |
| 0.2103 | 867.35 | 127500 | 0.9649 |
| 0.2078 | 868.03 | 127600 | 0.9543 |
| 0.2078 | 868.71 | 127700 | 0.9340 |
| 0.2001 | 869.39 | 127800 | 0.9447 |
| 0.2086 | 870.07 | 127900 | 0.9299 |
| 0.2086 | 870.75 | 128000 | 0.9294 |
| 0.2034 | 871.43 | 128100 | 0.9396 |
| 0.205 | 872.11 | 128200 | 0.9387 |
| 0.205 | 872.79 | 128300 | 0.9331 |
| 0.2083 | 873.47 | 128400 | 0.9292 |
| 0.2118 | 874.15 | 128500 | 0.9468 |
| 0.2118 | 874.83 | 128600 | 0.9398 |
| 0.2061 | 875.51 | 128700 | 0.9466 |
| 0.2117 | 876.19 | 128800 | 0.9093 |
| 0.2117 | 876.87 | 128900 | 0.9129 |
| 0.207 | 877.55 | 129000 | 0.9233 |
| 0.2038 | 878.23 | 129100 | 0.9220 |
| 0.2038 | 878.91 | 129200 | 0.9356 |
| 0.207 | 879.59 | 129300 | 0.9280 |
| 0.2088 | 880.27 | 129400 | 0.9434 |
| 0.2088 | 880.95 | 129500 | 0.9478 |
| 0.2077 | 881.63 | 129600 | 0.9528 |
| 0.2027 | 882.31 | 129700 | 0.9433 |
| 0.2027 | 882.99 | 129800 | 0.9510 |
| 0.2054 | 883.67 | 129900 | 0.9538 |
| 0.2049 | 884.35 | 130000 | 0.9634 |
| 0.2022 | 885.03 | 130100 | 0.9260 |
| 0.2022 | 885.71 | 130200 | 0.9655 |
| 0.206 | 886.39 | 130300 | 0.9469 |
| 0.2027 | 887.07 | 130400 | 0.9635 |
| 0.2027 | 887.76 | 130500 | 0.9606 |
| 0.2003 | 888.44 | 130600 | 0.9452 |
| 0.2049 | 889.12 | 130700 | 0.9407 |
| 0.2049 | 889.8 | 130800 | 0.9174 |
| 0.2086 | 890.48 | 130900 | 0.9513 |
| 0.2018 | 891.16 | 131000 | 0.9203 |
| 0.2018 | 891.84 | 131100 | 0.9370 |
| 0.2109 | 892.52 | 131200 | 0.9344 |
| 0.2041 | 893.2 | 131300 | 0.9300 |
| 0.2041 | 893.88 | 131400 | 0.9149 |
| 0.2009 | 894.56 | 131500 | 0.9109 |
| 0.2037 | 895.24 | 131600 | 0.9259 |
| 0.2037 | 895.92 | 131700 | 0.9581 |
| 0.2082 | 896.6 | 131800 | 0.9198 |
| 0.2067 | 897.28 | 131900 | 0.9171 |
| 0.2067 | 897.96 | 132000 | 0.8966 |
| 0.2119 | 898.64 | 132100 | 0.9311 |
| 0.2023 | 899.32 | 132200 | 0.9210 |
| 0.2023 | 900.0 | 132300 | 0.9106 |
| 0.2087 | 900.68 | 132400 | 0.9157 |
| 0.2152 | 901.36 | 132500 | 0.9347 |
| 0.2087 | 902.04 | 132600 | 0.9516 |
| 0.2087 | 902.72 | 132700 | 0.9711 |
| 0.2057 | 903.4 | 132800 | 0.9298 |
| 0.2071 | 904.08 | 132900 | 0.9421 |
| 0.2071 | 904.76 | 133000 | 0.9209 |
| 0.2097 | 905.44 | 133100 | 0.9325 |
| 0.2081 | 906.12 | 133200 | 0.9231 |
| 0.2081 | 906.8 | 133300 | 0.9227 |
| 0.2012 | 907.48 | 133400 | 0.9220 |
| 0.1995 | 908.16 | 133500 | 0.9500 |
| 0.1995 | 908.84 | 133600 | 0.9587 |
| 0.2058 | 909.52 | 133700 | 0.9579 |
| 0.2011 | 910.2 | 133800 | 0.9512 |
| 0.2011 | 910.88 | 133900 | 0.9445 |
| 0.2083 | 911.56 | 134000 | 0.9482 |
| 0.2022 | 912.24 | 134100 | 0.9282 |
| 0.2022 | 912.93 | 134200 | 0.9387 |
| 0.2003 | 913.61 | 134300 | 0.9509 |
| 0.212 | 914.29 | 134400 | 0.9609 |
| 0.212 | 914.97 | 134500 | 0.9430 |
| 0.2045 | 915.65 | 134600 | 0.9330 |
| 0.2045 | 916.33 | 134700 | 0.9764 |
| 0.2049 | 917.01 | 134800 | 0.9311 |
| 0.2049 | 917.69 | 134900 | 0.9344 |
| 0.2028 | 918.37 | 135000 | 0.9538 |
| 0.1993 | 919.05 | 135100 | 0.9359 |
| 0.1993 | 919.73 | 135200 | 0.9695 |
| 0.2068 | 920.41 | 135300 | 0.9354 |
| 0.2036 | 921.09 | 135400 | 0.9817 |
| 0.2036 | 921.77 | 135500 | 0.9404 |
| 0.2054 | 922.45 | 135600 | 0.9537 |
| 0.2017 | 923.13 | 135700 | 0.9613 |
| 0.2017 | 923.81 | 135800 | 0.9340 |
| 0.1973 | 924.49 | 135900 | 0.9313 |
| 0.216 | 925.17 | 136000 | 0.9541 |
| 0.216 | 925.85 | 136100 | 0.9556 |
| 0.2032 | 926.53 | 136200 | 0.9236 |
| 0.1984 | 927.21 | 136300 | 0.9243 |
| 0.1984 | 927.89 | 136400 | 0.9497 |
| 0.195 | 928.57 | 136500 | 0.9485 |
| 0.196 | 929.25 | 136600 | 0.9370 |
| 0.196 | 929.93 | 136700 | 0.9294 |
| 0.1991 | 930.61 | 136800 | 0.9510 |
| 0.2008 | 931.29 | 136900 | 0.9445 |
| 0.2008 | 931.97 | 137000 | 0.9428 |
| 0.1997 | 932.65 | 137100 | 0.9718 |
| 0.1998 | 933.33 | 137200 | 0.9620 |
| 0.1962 | 934.01 | 137300 | 0.9388 |
| 0.1962 | 934.69 | 137400 | 0.9578 |
| 0.1932 | 935.37 | 137500 | 0.9383 |
| 0.1989 | 936.05 | 137600 | 0.9285 |
| 0.1989 | 936.73 | 137700 | 0.9671 |
| 0.1965 | 937.41 | 137800 | 0.9572 |
| 0.1988 | 938.1 | 137900 | 0.9487 |
| 0.1988 | 938.78 | 138000 | 0.9369 |
| 0.2006 | 939.46 | 138100 | 0.9343 |
| 0.1995 | 940.14 | 138200 | 0.9488 |
| 0.1995 | 940.82 | 138300 | 0.9242 |
| 0.2047 | 941.5 | 138400 | 0.9214 |
| 0.2118 | 942.18 | 138500 | 0.9054 |
| 0.2118 | 942.86 | 138600 | 0.9391 |
| 0.1934 | 943.54 | 138700 | 0.9256 |
| 0.2012 | 944.22 | 138800 | 0.9372 |
| 0.2012 | 944.9 | 138900 | 0.9355 |
| 0.1984 | 945.58 | 139000 | 0.9284 |
| 0.1953 | 946.26 | 139100 | 0.9206 |
| 0.1953 | 946.94 | 139200 | 0.9281 |
| 0.1974 | 947.62 | 139300 | 0.9300 |
| 0.1919 | 948.3 | 139400 | 0.9566 |
| 0.1919 | 948.98 | 139500 | 0.9674 |
| 0.1951 | 949.66 | 139600 | 0.9739 |
| 0.1986 | 950.34 | 139700 | 0.9548 |
| 0.2041 | 951.02 | 139800 | 0.9510 |
| 0.2041 | 951.7 | 139900 | 0.9621 |
| 0.198 | 952.38 | 140000 | 0.9119 |
| 0.1954 | 953.06 | 140100 | 0.9355 |
| 0.1954 | 953.74 | 140200 | 0.9858 |
| 0.1986 | 954.42 | 140300 | 0.9534 |
| 0.2021 | 955.1 | 140400 | 0.9391 |
| 0.2021 | 955.78 | 140500 | 0.9440 |
| 0.2 | 956.46 | 140600 | 0.9461 |
| 0.1928 | 957.14 | 140700 | 0.9493 |
| 0.1928 | 957.82 | 140800 | 0.9452 |
| 0.1953 | 958.5 | 140900 | 0.9946 |
| 0.1982 | 959.18 | 141000 | 0.9450 |
| 0.1982 | 959.86 | 141100 | 0.9513 |
| 0.2022 | 960.54 | 141200 | 0.9530 |
| 0.1939 | 961.22 | 141300 | 0.9312 |
| 0.1939 | 961.9 | 141400 | 0.9523 |
| 0.2007 | 962.59 | 141500 | 0.9353 |
| 0.1884 | 963.27 | 141600 | 0.9613 |
| 0.1884 | 963.95 | 141700 | 0.9531 |
| 0.1993 | 964.63 | 141800 | 0.9392 |
| 0.1971 | 965.31 | 141900 | 0.9484 |
| 0.1971 | 965.99 | 142000 | 0.9328 |
| 0.1961 | 966.67 | 142100 | 0.9410 |
| 0.1977 | 967.35 | 142200 | 0.9437 |
| 0.1998 | 968.03 | 142300 | 0.9449 |
| 0.1998 | 968.71 | 142400 | 0.9371 |
| 0.1982 | 969.39 | 142500 | 0.9450 |
| 0.1996 | 970.07 | 142600 | 0.9448 |
| 0.1996 | 970.75 | 142700 | 0.9493 |
| 0.1964 | 971.43 | 142800 | 0.9377 |
| 0.1938 | 972.11 | 142900 | 0.9306 |
| 0.1938 | 972.79 | 143000 | 0.9513 |
| 0.1897 | 973.47 | 143100 | 0.9496 |
| 0.2045 | 974.15 | 143200 | 0.9461 |
| 0.2045 | 974.83 | 143300 | 0.9329 |
| 0.1946 | 975.51 | 143400 | 0.9688 |
| 0.197 | 976.19 | 143500 | 0.9371 |
| 0.197 | 976.87 | 143600 | 0.9512 |
| 0.2004 | 977.55 | 143700 | 0.9373 |
| 0.2002 | 978.23 | 143800 | 0.9569 |
| 0.2002 | 978.91 | 143900 | 0.9513 |
| 0.1916 | 979.59 | 144000 | 0.9457 |
| 0.1959 | 980.27 | 144100 | 0.9251 |
| 0.1959 | 980.95 | 144200 | 0.9330 |
| 0.1934 | 981.63 | 144300 | 0.9382 |
| 0.1954 | 982.31 | 144400 | 0.9553 |
| 0.1954 | 982.99 | 144500 | 0.9498 |
| 0.1919 | 983.67 | 144600 | 0.9558 |
| 0.1883 | 984.35 | 144700 | 0.9484 |
| 0.1928 | 985.03 | 144800 | 0.9310 |
| 0.1928 | 985.71 | 144900 | 0.9282 |
| 0.1872 | 986.39 | 145000 | 0.9351 |
| 0.1868 | 987.07 | 145100 | 0.9457 |
| 0.1868 | 987.76 | 145200 | 0.9444 |
| 0.1906 | 988.44 | 145300 | 0.9478 |
| 0.1957 | 989.12 | 145400 | 0.9691 |
| 0.1957 | 989.8 | 145500 | 0.9437 |
| 0.1959 | 990.48 | 145600 | 0.9576 |
| 0.1912 | 991.16 | 145700 | 0.9539 |
| 0.1912 | 991.84 | 145800 | 0.9463 |
| 0.1977 | 992.52 | 145900 | 0.9703 |
| 0.1955 | 993.2 | 146000 | 0.9462 |
| 0.1955 | 993.88 | 146100 | 0.9621 |
| 0.1923 | 994.56 | 146200 | 0.9568 |
| 0.1959 | 995.24 | 146300 | 0.9650 |
| 0.1959 | 995.92 | 146400 | 0.9668 |
| 0.1921 | 996.6 | 146500 | 0.9588 |
| 0.1968 | 997.28 | 146600 | 0.9510 |
| 0.1968 | 997.96 | 146700 | 0.9430 |
| 0.1927 | 998.64 | 146800 | 0.9672 |
| 0.1995 | 999.32 | 146900 | 0.9508 |
| 0.1995 | 1000.0 | 147000 | 0.9548 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.2.0.dev20230910+cu121
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hasibul1ah/bloom1b7-finetuned-LoRA-for-Bengali
|
hasibul1ah
| 2023-10-09T05:25:37Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T05:07:14Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
pinot/wav2vec2-xls-r-300m-ja-phoneme-cv_13_test
|
pinot
| 2023-10-09T05:04:17Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_4",
"base_model:finetune:pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_4",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-09T02:36:46Z |
---
license: apache-2.0
base_model: pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_4
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-ja-phoneme-cv_13_test
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: ja
split: test
args: ja
metrics:
- name: Wer
type: wer
value: 1.0452119589468987
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-ja-phoneme-cv_13_test
This model is a fine-tuned version of [pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_4](https://huggingface.co/pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_4) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3902
- Wer: 1.0452
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4481 | 3.22 | 500 | 3.3902 | 1.0452 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
|
AmineAllo/table-transformer-azure-dust-65
|
AmineAllo
| 2023-10-09T04:53:50Z | 189 | 0 |
transformers
|
[
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"base_model:AmineAllo/MT-celestial-grass-58",
"base_model:finetune:AmineAllo/MT-celestial-grass-58",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-10-09T04:39:39Z |
---
base_model: toobiza/MT-celestial-grass-58
tags:
- generated_from_trainer
model-index:
- name: table-transformer-azure-dust-65
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# table-transformer-azure-dust-65
This model is a fine-tuned version of [toobiza/MT-celestial-grass-58](https://huggingface.co/toobiza/MT-celestial-grass-58) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1439
- eval_loss_ce: 0.0014
- eval_loss_bbox: 0.0818
- eval_cardinality_error: 1.0
- eval_giou: 92.6646
- eval_runtime: 26.1327
- eval_samples_per_second: 3.061
- eval_steps_per_second: 1.531
- epoch: 4.17
- step: 350
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Jackellie/QR_Bocca
|
Jackellie
| 2023-10-09T04:52:11Z | 0 | 4 | null |
[
"stable diffusion",
"control net",
"qr code",
"en",
"zh",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-09-07T06:52:24Z |
---
license: cc-by-nc-4.0
language:
- en
- zh
tags:
- stable diffusion
- control net
- qr code
---
** Controlnet QR Bocca for SD-1.5 **控制網QR Bocca適用於SD-1.5
## Model Description
The development of this model primarily focuses on beautification and scanning stability.
While this model has good stability, it can still be influenced by the choice of models used in combination.
## How to Use
1、For QR codes, it is recommended to use version 5 or 6,and process the area outside the QR code into gray,This will extend the image more efficiently..
2、You can set the parameters according to the original A1111 settings: Step 20 / CFG 7 / Use Euler A for sampling and DPM++ 2M SDE Karras for relative stability.
3、A smaller size might lead to a reduction in scanning rate or image quality. It is recommended to use a minimum size of 768x768.
4、Please try with low weights first, then gradually increase the control net weights using the same seed to achieve an excellent final product.
5、This is very important. Please input a QR code that has not been deformed to ensure stability in the output
## Example Outputs
Here are some examples of creative, yet scannable QR codes produced by our model:
If you have any other requests, feel free to let me know.
YT: https://www.youtube.com/@JackEllie
DC: https://discord.gg/TM5d89YNwA
## 模型說明
此模型的開發主要著重於多風格的美化及加強掃描穩定性
雖然這個模型的穩定性遠優於從前，但是依然會受到選擇使用搭配的模型影響
## 如何使用
1、QRcode版本建議使用5或6，並將QR code以外的區域改成灰色，這將能更有效的將畫面延伸。
2、參數可依照A1111原始參數設定就好(步數 20 / CFG 7 / 取樣方式使用Euler a 和DPM++ 2M SDE Karras相對穩定)
3、較小的尺寸可能會導致掃描率或是畫面品質降低，建議最少使用768*768尺寸
4、請以低權重嘗試後再使用相同種子將控制網權重慢慢往上調整，以獲得優秀的成品
5、這非常的重要，請輸入沒有變形過的qrcode，以保證輸出的穩定度
## 此模型製作範例圖
以下是我們的模型生成的一些美化且可掃描的二維碼示例：
%2C(enticing%20breakfast%20scene)%2Ca%20modwatering%20tableau%20showcases%20a%20(scrumptious%20breakfast%20Sandwich)%20RESTIN.png)
%2Cbest%20quality%2Chighres%2Cextremely%20detailed%208k%20wallpaper%2Cvery%20clear%2Chigh%20quality%2Cextremely%20detailed%20face%2Cextremely.png)
開心地玩耍吧!!!有任何有趣的想法記得到杰克艾粒的yt或dc找我們喔~~~
YT: https://www.youtube.com/@JackEllie
DC: https://discord.gg/TM5d89YNwA
|
dvs/swin-tiny-patch4-window7-224-uploads-classifier-v2
|
dvs
| 2023-10-09T04:41:23Z | 220 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-10-09T03:55:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-uploads-classifier-v2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.984313725490196
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-uploads-classifier-v2
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0745
- Accuracy: 0.9843
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2482 | 1.0 | 18 | 0.4781 | 0.8824 |
| 0.3036 | 2.0 | 36 | 0.0936 | 0.9804 |
| 0.1687 | 3.0 | 54 | 0.0745 | 0.9843 |
| 0.1392 | 4.0 | 72 | 0.0980 | 0.9725 |
| 0.14 | 5.0 | 90 | 0.0778 | 0.9765 |
| 0.1186 | 6.0 | 108 | 0.0837 | 0.9725 |
| 0.1088 | 7.0 | 126 | 0.0645 | 0.9804 |
| 0.0789 | 8.0 | 144 | 0.0675 | 0.9765 |
| 0.0644 | 9.0 | 162 | 0.0940 | 0.9686 |
| 0.0582 | 10.0 | 180 | 0.0879 | 0.9725 |
| 0.0591 | 11.0 | 198 | 0.0935 | 0.9686 |
| 0.0538 | 12.0 | 216 | 0.0540 | 0.9804 |
| 0.0588 | 13.0 | 234 | 0.0725 | 0.9686 |
| 0.0538 | 14.0 | 252 | 0.0637 | 0.9765 |
| 0.0462 | 15.0 | 270 | 0.0694 | 0.9725 |
| 0.0352 | 16.0 | 288 | 0.0771 | 0.9686 |
| 0.0536 | 17.0 | 306 | 0.0629 | 0.9804 |
| 0.0403 | 18.0 | 324 | 0.0933 | 0.9686 |
| 0.0412 | 19.0 | 342 | 0.0848 | 0.9725 |
| 0.0305 | 20.0 | 360 | 0.0820 | 0.9725 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
saurastha/whisper-small-ne
|
saurastha
| 2023-10-09T04:40:59Z | 84 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-09T04:39:09Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6003
- Wer Ortho: 72.9876
- Wer: 49.5585
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 5
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Wer Ortho |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 1.5813 | 0.04 | 5 | 1.2523 | 73.4580 | 94.3667 |
| 0.9924 | 0.08 | 10 | 1.0420 | 76.0991 | 94.0559 |
| 0.8522 | 0.12 | 15 | 0.9581 | 71.8012 | 91.8415 |
| 0.7602 | 0.17 | 20 | 0.9154 | 64.7802 | 88.3450 |
| 0.6813 | 0.21 | 25 | 0.8780 | 65.9285 | 87.9953 |
| 0.7084 | 0.25 | 30 | 0.8520 | 63.5171 | 86.4413 |
| 0.5974 | 0.29 | 35 | 0.8120 | 62.9593 | 85.5866 |
| 0.5631 | 0.33 | 40 | 0.7904 | 59.9409 | 84.1492 |
| 0.6962 | 0.38 | 45 | 0.7631 | 58.0709 | 82.6340 |
| 0.4946 | 0.42 | 50 | 0.7486 | 60.7448 | 83.1779 |
| 0.6118 | 0.46 | 55 | 0.7216 | 59.8097 | 84.4600 |
| 0.5004 | 0.5 | 60 | 0.7018 | 58.8255 | 82.4786 |
| 0.4357 | 0.54 | 65 | 0.6932 | 57.4311 | 81.2743 |
| 0.4478 | 0.58 | 70 | 0.6839 | 55.8399 | 80.8469 |
| 0.4561 | 0.62 | 75 | 0.6856 | 59.3012 | 80.8081 |
| 0.429 | 0.67 | 80 | 0.6646 | 55.0853 | 79.9145 |
| 0.4072 | 0.71 | 85 | 0.6601 | 52.2638 | 76.0684 |
| 0.4096 | 0.75 | 90 | 0.6477 | 52.3950 | 76.8842 |
| 0.4471 | 0.79 | 95 | 0.6393 | 51.2795 | 76.1072 |
| 0.3282 | 0.83 | 100 | 0.6003 | 72.9876 | 49.5585 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
rlmjy/biogpt_test_2
|
rlmjy
| 2023-10-09T04:38:26Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T04:38:25Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
ssalbab/nlp-homework
|
ssalbab
| 2023-10-09T04:37:17Z | 3 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-10-08T15:22:22Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
wangzhang/Llama2-sequoiaDB
|
wangzhang
| 2023-10-09T04:32:48Z | 5 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"tensorboard",
"autotrain",
"text-generation",
"dataset:wangzhang/sdb",
"region:us"
] |
text-generation
| 2023-10-01T08:11:46Z |
---
tags:
- autotrain
- text-generation
widget:
- text: This is a private NLP model trained with data from SequioaDB
datasets:
- wangzhang/sdb
library_name: adapter-transformers
---
# This is a private NLP model trained with data from SequioaDB
```
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "TinyPixel/Llama-2-7B-bf16-sharded"
adapters_name = 'wangzhang/Llama2-sequoiaDB'
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
),
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
|
kaveshan/mistral-7b-kn
|
kaveshan
| 2023-10-09T04:24:05Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T04:23:23Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
anzorq/openai-whisper-large-v2-LORA-colab
|
anzorq
| 2023-10-09T04:19:22Z | 1 | 0 |
peft
|
[
"peft",
"base_model:openai/whisper-large-v2",
"base_model:adapter:openai/whisper-large-v2",
"region:us"
] | null | 2023-10-08T23:17:34Z |
---
library_name: peft
base_model: openai/whisper-large-v2
---
Use language="Georgian" for inference.
# Inference
```Python
import torch
import gradio as gr
from transformers import (
AutomaticSpeechRecognitionPipeline,
WhisperForConditionalGeneration,
WhisperTokenizer,
WhisperProcessor,
)
from peft import PeftModel, PeftConfig
from pytube import YouTube
peft_model_id = "anzorq/openai-whisper-large-v2-LORA-colab"
# peft_model_id = "/content/whisper_large_kbd_lora/checkpoint-64"
language = "Georgian"
task = "transcribe"
peft_config = PeftConfig.from_pretrained(peft_model_id)
model = WhisperForConditionalGeneration.from_pretrained(
peft_config.base_model_name_or_path, load_in_8bit=True, device_map="auto"
)
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
feature_extractor = processor.feature_extractor
forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)
pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
def transcribe(path_to_audio):
with torch.cuda.amp.autocast():
text = pipe(audio_path, generate_kwargs={"forced_decoder_ids": forced_decoder_ids}, max_new_tokens=255)["text"]
return text
transcribe(path_to_audio)
```
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
BAAI/AquilaSQL-7B
|
BAAI
| 2023-10-09T04:16:41Z | 15 | 12 |
transformers
|
[
"transformers",
"pytorch",
"aquila",
"custom_code",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-10-08T06:40:13Z |
---
license: other
---
<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/BAAI/AquilaSQL-7B/blob/main/README_zh.md">简体中文</a>
</p>
</h4>
Aquila Language Model is the first open source language model that supports both Chinese and English knowledge, commercial license agreements, and compliance with domestic data regulations.
- 🌟 **Supports open source commercial licenses**. The source code of the Aquila series models is based on the [Apache 2.0 agreement](https://www.apache.org/licenses/LICENSE-2.0), while the model weight is based on the [BAAI Aquila Model License Agreement](https://huggingface.co/BAAI/AquilaChat-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf). Users can use it for commercial purposes as long as they meet the licensing restrictions.
- ✍️ **Possesses Chinese and English knowledge**. The Aquila series model is trained from scratch on a high-quality corpus of Chinese and English languages, with Chinese corpora accounting for about 40%, ensuring that the model accumulates native Chinese world knowledge during the pre-training phase, rather than translated knowledge.
- 👮♀️ **Complies with domestic data regulations**. The Chinese corpora of the Aquila series models come from Intelligence Source's accumulated Chinese datasets over the years, including Chinese internet data from over 10,000 sources (more than 99% of which are domestic sources), as well as high-quality Chinese literature and book data supported by authoritative domestic organizations. We will continue to accumulate high-quality and diverse datasets and incorporate them into the subsequent training of the Aquila base models.
- 🎯 **Continuous improvements and open sourcing**. We will continue to improve training data, optimize training methods, and enhance model performance, cultivate a flourishing "model tree" on a better base model foundation, and continuously update open-source versions.
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels, including the [FlagAI GitHub repository](https://github.com/FlagAI-Open/FlagAI/), [FlagAI's Zhihu account](https://www.zhihu.com/people/95-22-20-18) and [FlagAI's official technical communication group](https://github.com/FlagAI-Open/FlagAI/blob/master/wechat-qrcode.jpg).
| Model | Model Type | Description | Status | GPUs Used |
| ------------ | ---------- | ------------------------------------------------------------ | --------- | ----------- |
| AquilaSQL-7B | chat model | text2sql model, cotinue traind from the AquilaCode-base model, AquilaSQL achieved sota on the cspider leadboard | published | Nvidia-A100 |
We will continue to release improved versions of Aquila model as open source.
(https://huggingface.co/BAAI/AquilaSQL-7B/blob/main/change_log.log).
<!-- </table> -->
## Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = torch.device("cuda")
model_info = "BAAI/AquilaSQL-7B"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_info, trust_remote_code=True, torch_dtype=torch.float16, device_map='auto')
model.eval()
model.to(device)
torch.manual_seed(123)
text = "有多个数据库表，信息如下：\n表名为cars_data，包含的属性为cars_data.horsepower,cars_data.accelerate,cars_data.mpg,cars_data.id,cars_data.year;表名为continents，包含的属性为continents.contid,continents.continent;表名为countries，包含的属性为countries.continent,countries.countryname,countries.countryid;表名为model_list，包含的属性为model_list.model,model_list.maker,model_list.modelid，它们之间的关系为 countries.continent = continents.contid\n请为下面的问题编写sql查询语句：\n加速度比马力最大的汽车更大的汽车有多少辆？ "
def generate_prompt(input: str):
prompt = f"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: {input}###Assistant:"
return prompt
stop_tokens = ["###", "[UNK]", "</s>","<|endoftext|>"]
with torch.no_grad():
_input = generate_prompt(text)
tokens = tokenizer.encode_plus(_input, None, max_length=None)['input_ids']
tokens = torch.tensor(tokens)[None,].to(device)
out = model.generate(tokens, do_sample=False, max_length=1024, eos_token_id=100007,max_new_tokens=512,
bad_words_ids=[[tokenizer.encode(token)[0] for token in stop_tokens]])[0]
out = tokenizer.decode(out.cpu().numpy().tolist())
print(out)
```
## License
AquilaSQL-7B open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaChat-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf)
|
saumyasinha0510/MT5
|
saumyasinha0510
| 2023-10-09T04:12:59Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-01T23:54:21Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_keras_callback
model-index:
- name: saumyasinha0510/MT5
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# saumyasinha0510/MT5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 206848, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| nan | nan | 0 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Mkmworld/original-regression
|
Mkmworld
| 2023-10-09T04:06:02Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-10-09T04:04:28Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | 9.999999747378752e-05 |
| decay | 1e-05 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
sanskarGupta551/bloomz-560m_Prompt_to_Dialog
|
sanskarGupta551
| 2023-10-09T04:04:25Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T04:03:01Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
AmineAllo/MT-radiant-spaceship-60
|
AmineAllo
| 2023-10-09T03:43:27Z | 189 | 0 |
transformers
|
[
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"base_model:AmineAllo/MT-celestial-grass-58",
"base_model:finetune:AmineAllo/MT-celestial-grass-58",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-10-09T03:01:51Z |
---
base_model: toobiza/MT-celestial-grass-58
tags:
- generated_from_trainer
model-index:
- name: MT-radiant-spaceship-60
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MT-radiant-spaceship-60
This model is a fine-tuned version of [toobiza/MT-celestial-grass-58](https://huggingface.co/toobiza/MT-celestial-grass-58) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6476
- Loss Ce: 0.0030
- Loss Bbox: 0.0949
- Cardinality Error: 1.0
- Giou: 91.4979
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Loss Ce | Loss Bbox | Cardinality Error | Giou |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:-----------------:|:-------:|
| 0.4929 | 0.6 | 50 | 0.7752 | 0.0038 | 0.1130 | 1.0 | 89.6877 |
| 0.4067 | 1.19 | 100 | 0.6803 | 0.0034 | 0.0994 | 1.0 | 91.0025 |
| 0.3416 | 1.79 | 150 | 0.6697 | 0.0032 | 0.0980 | 1.0 | 91.1694 |
| 0.3604 | 2.38 | 200 | 0.6478 | 0.0031 | 0.0949 | 1.0 | 91.4886 |
| 0.3774 | 2.98 | 250 | 0.6476 | 0.0030 | 0.0949 | 1.0 | 91.4979 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ThuyNT03/PhoBERT-cls-detail-in-Non_OCR
|
ThuyNT03
| 2023-10-09T03:38:09Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:vinai/phobert-base",
"base_model:finetune:vinai/phobert-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-16T17:40:00Z |
---
base_model: vinai/phobert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBERT-cls-detail-in-Non_OCR
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PhoBERT-cls-detail-in-Non_OCR
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2965
- Accuracy: 0.95
- F1: 0.9359
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5312 | 1.0 | 25 | 1.2681 | 0.55 | 0.4060 |
| 1.1478 | 2.0 | 50 | 0.8709 | 0.82 | 0.7465 |
| 0.7779 | 3.0 | 75 | 0.5259 | 0.92 | 0.8928 |
| 0.528 | 4.0 | 100 | 0.3918 | 0.92 | 0.8928 |
| 0.4236 | 5.0 | 125 | 0.3363 | 0.94 | 0.9254 |
| 0.3641 | 6.0 | 150 | 0.3035 | 0.95 | 0.9359 |
| 0.3356 | 7.0 | 175 | 0.2965 | 0.95 | 0.9359 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Rewcifer/teamyellow-llama7B-lora
|
Rewcifer
| 2023-10-09T03:21:12Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"region:us"
] | null | 2023-10-07T00:11:12Z |
---
library_name: peft
base_model: decapoda-research/llama-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
casque/majicmixRealistic_v7
|
casque
| 2023-10-09T03:17:22Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-10-08T23:59:01Z |
---
license: creativeml-openrail-m
---
|
qkrtnwls0/hw1
|
qkrtnwls0
| 2023-10-09T03:15:07Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-10-09T02:53:33Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
rmuema/kaggle-x-elo-finetune-v1.2
|
rmuema
| 2023-10-09T03:14:03Z | 2 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-10-09T03:13:55Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
ttttdiva/rvc_okiba
|
ttttdiva
| 2023-10-09T03:01:17Z | 0 | 4 | null |
[
"rvc",
"audio-to-audio",
"ja",
"license:creativeml-openrail-m",
"region:us"
] |
audio-to-audio
| 2023-10-09T03:01:16Z |
---
tags:
- rvc
pipeline_tag: audio-to-audio
license: creativeml-openrail-m
language:
- ja
---
<style>
audio {
width: 200px;
height: 30px;
}
</style>
# RVC okiba (随時モデルを追加しています: More models are uploaded regularly)
しばらく忙しいので更新停止中です(I'm busy now, so currently update is stopped.)
[人気投票・アンケートへご協力ください (Please participate in the popularity poll and survey.)](https://docs.google.com/forms/d/e/1FAIpQLSdUTaFcpNErkSs58e7zO9MtlC9F1LElI-ODjOX5LZEtYoMAIg/viewform?usp=sf_link)
<details>
<summary>English</summary>
[**Text-to-Speech Demo here!**](https://huggingface.co/spaces/litagin/rvc_okiba_TTS)
[Update History](https://huggingface.co/litagin/rvc_okiba/commits/main)
A collection of [RVC v2](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) models (with index files) trained using approximately 1 hour (per model) of high-quality Japanese voice data of *moe* characters.
**Samples can be found in the table below.** ([*Japanese voice-change samples here*](vc-samples.md))
- There are currently **63** girl voice models (A-K3) and 6 male voice models (man-A - man-F) (Will be updated regularly, and often epochs are updated to better version)
- The model names are unordered and do not carry any meaning.
- Epochs are adjusted for voice-changing purpose, but singing is maybe possible as samples at some quality.
*Please use the data provided by this model at your own risk.*
---
</details>
# RVC置き場
[モデルのダウンロードはこちらから](https://huggingface.co/litagin/rvc_okiba/tree/main/models)
[Text-to-Speech（テキストからの音声合成）のデモはこちら](https://huggingface.co/spaces/litagin/rvc_okiba_TTS)
[更新履歴](https://huggingface.co/litagin/rvc_okiba/commits/main)
[RVC v2](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)のモデル集（index付き）。
[**ボイチェンで実際に使用したサンプル（男声から変換）はこちらから**](vc-samples.md)、また**通常サンプルは下の表から聴けます**。
- 現在女性**63**モデル(A-K3)に男性6モデル(man-A - man-F)（随時更新します、またエポック数をたまにより良いバージョンに置き換えることもあります）
- モデル名は順不同で意味はありません
- 主にボイチェン用途を想定にエポックの調整をしています（が歌唱もサンプルの通りある程度はできると思われます）
<details>
<summary>学習詳細</summary>
- 学習データ: 高品質な日本語発話データ、歌は無し。1ファイルの長さは4秒以上～多くても10数秒程度、無音カットやらの前処理はしていません。
- データセット量: 1モデルにつき基本は合計60分弱、素材が足りなかったときは30分~
- エポック数: 100-300エポックあたりから良さそうなものを選んだもの
</details>
## 注意事項
使用は自己責任でお願いします。
## 出力サンプルと雑感 (Samples and comments)
[**ボイチェンで実際に使用したサンプル（男声から変換）は別ページにまとめてあります。**](vc-samples.md)
<details>
<summary>変換前音声 (Input data)</summary>
- 発話 (Japanese speech)
<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/jvs002.wav">
「また東寺のように五大明王と呼ばれる主要な明王の中央に配されることも多い」
- 歌 (Japanese child-song)
<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/input.wav">
でんでんむしむしかたつむり🐌
出典：[sample jvs002, JVS corpus](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus), [sample_jvs001, JVS-MuSiC](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_music)
</details>
<details>
<summary>出力設定</summary>
[RVC v2](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)の推論タブでの変換
- ピッチ抽出: harvest
- ピッチ調整: 発話は[jvs-samples](https://huggingface.co/litagin/rvc_okiba/tree/main/jvs-samples)のファイル名に記載（記載なしはピッチ0）、歌唱は女性モデルは+12、男性モデルは0で統一
- index使用率: 発話は1、歌唱は0.75
- 何か保護するアレ: 0.33（デフォルト値のまま）
</details>
### 女性モデル (Girl models)
- ~正直声のコメントの語彙が尽きてきた~
- 「高め」「低め」は、（学習データの声の高さが高め・低めなので、）「高い声（ピッチ）と相性がいい」「低い声と相性がいい」程度の意味です。
- 正確な声質や詳しい個人的な感想・5段階評価は[ボイチェンサンプル音声](vc-samples.md)のページをご覧ください。
|Name| Speech |Song|Comment |
|----| ---- |----|---- |
|A|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/A.wav">|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/A.wav">| ハスキーダウナー系、低め |
|B|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/B-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/B.wav">| 癖なく明るい |
|C|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/C-plus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/C.wav">| 吐息混じり、ちょい低め |
|D|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/D-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/D.wav">| 元気、高め |
|E|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/E-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/E.wav">| ウィスパー、少し特徴的 |
|F|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/F.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/F.wav">| ちょい低め、ちょい微妙かも |
|G|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/G-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/G.wav">| ナレーターのような声、ちょい微妙かも |
|H|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/H-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/H.wav">| さわやか、ツンデレ風味、ちょい高め |
|I|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/I-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/I.wav">| 元気、やんちゃ、特徴的 |
|J|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/J.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/J.wav">| ダウナー、低め、特徴的 |
|K|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/K-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/K.wav">| 明るく朗らか、ちょい高め |
|L|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/L-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/L.wav">| 明るく元気、ちょい高め、ちょい微妙かも |
|M|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/M-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/M.wav">| Iと似てる、Iより癖無し |
|N|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/N-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/N.wav">| Dと似てる、Dよりちょい幼い |
|O|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/O-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/O.wav">| 高め、明るく元気 |
|P|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/P.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/P.wav">| ショタボ |
|Q|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/Q-plus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/Q.wav">| 朗らかで響く声 |
|R|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/R.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/R.wav">| はっきりした声、ちょい低め |
|S|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/S-minus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/S.wav">| 低め、ダウナー、ちょい微妙かも |
|T|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/T.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/T.wav">| 低音に強い、ちょっと特徴的 |
|U|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/U-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/U.wav">| 澄んだ声、ちょい高め、特徴的 |
|V|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/V-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/V.wav">| 凛とした声、ちょい高め |
|W|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/W-plus6.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/W.wav">| かなり高いロリ声 |
|X|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/X-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/X.wav">| Oと似てるがOより低く落ち着いてる |
|Y|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/Y-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/Y.wav">| 元気で明るい声、適度な高さ |
|Z|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/Z.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/Z.wav">| Fと似ている、低く目で落ち着いてたぶんFより質高い |
|AA|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/AA-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/AA.wav">| ほどほどの高さ、包容力のある声（？） |
|BB|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/BB-plus4.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/BB.wav">| 高め（低めもイケる）、甘えた感じ |
|CC|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/CC-minus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/CC.wav">| 低め、中性的な女子声 |
|DD|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/DD-plus6.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/DD.wav">| かなり高くてたどたどしい声 |
|EE|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/EE-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/EE.wav">| 適度な高さ、優しい声音 |
|FF|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/FF-plus5.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/FF.wav">| Eと似てる、より高く幼い |
|GG|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/GG-plus5.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/GG.wav">| ちょい高めの元気ではっきりした声 |
|HH|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/HH-plus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/HH.wav">| ちょっと低めで大人なはっきり明朗とした声 |
|II|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/II-plus4.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/II.wav">| ちょい高めで吐息混じりで特徴的 |
|JJ|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/JJ-plus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/JJ.wav">| Uと似てる、より低めで落ち着いた感じ |
|KK|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/KK-plus5.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/KK.wav">| 高く響くちょい特徴的 |
|LL|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/LL-plus6.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/LL.wav">| Cと似てる、高め優しげ癖あり |
|MM|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/MM-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/MM.wav">| ほどよい高さ、透明、飾らないけどかわいい |
|NN|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/NN-plus4.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/NN.wav">| ちょい高め、舌っ足らず感 |
|OO|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/OO-plus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/OO.wav">| 低めの大人のお姉さん |
|PP|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/PP-plus4.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/PP.wav">| ちょい高め、特徴的舌っ足らず感 |
|QQ|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/QQ-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/QQ.wav">| ちょい低め、ジト目声感 |
|RR|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/RR-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/RR.wav">| ちょい低め、強気な感じの女の子 |
|SS|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/SS-plus6.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/SS.wav">| 高め、儚げ、質微妙かも |
|TT|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/TT-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/TT.wav">| ほどよい高さ、ちょい中性的? |
|UU|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/UU-plus8.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/UU.wav">| 高め、包容力あるけど若い |
|VV|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/VV-plus3.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/VV.wav">| ほどよい高さ、ツンデレ風味 |
|WW|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/WW.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/WW.wav">| 低め、強気なお姉さん |
|XX|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/XX-plus7.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/XX.wav">| 高めだけどしっかりした声 |
|YY|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/YY.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/YY.wav">| 低めの女性のかわいい地声感 |
|ZZ|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/ZZ-plus11.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/ZZ.wav">| すごく高め、おどおど声、極端に音域がせまい |
|AAA|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/AAA-plus4.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/AAA.wav">| ほどよい高さ、ちょっと演技がかったかわいい |
|BBB|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/BBB-plus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/BBB.wav">| 低い、D・Nと似た声、ボーイッシュ？ |
|CCC|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/CCC-plus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/CCC.wav">| ちょい低めだけど高め声もいける、落ち着いてるけど少女な声音 |
|DDD|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/DDD-plus1.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/DDD.wav">| ちょい低め、ちょい強気な明朗な口調 |
|EEE|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/EEE-plus7.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/EEE.wav">| 高め、柔らかく演技したふんわり声音 |
|FFF|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/FFF-plus2.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/FFF.wav">| 低め、ダミ声演技声 |
|GGG|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/GGG-plus5.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/GGG.wav">| 高め、アニメ声のような特徴的な声音 |
|HHH|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/HHH-plus7.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/HHH.wav">| 高め、幼いロリ声ロリ発音 |
|III|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/III-plus7.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/III.wav">| 高い元気なアニメ声 |
|JJJ|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/JJJ.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/JJJ.wav">| 女性の演じる少し芝居がかった男性声 |
|K3|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/K3-plus5.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/K3.wav">| 程よい高さ |
### 男性モデル (Male models)
|Name|Speech|Song|Comment|
|----| ---- |----|---- |
|man-A|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/man-A-minus13.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/man-A.wav">| 渋いおっさん|
|man-B|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/man-B-minus13.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/man-B.wav">| 低めの男 |
|man-C|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/man-C-minus11.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/man-C.wav">| チャラそうなお兄さん |
|man-D|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/man-D-minus14.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/man-D.wav">| 低め、ぶっきらぼう男 |
|man-E|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/man-E-minus12.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/man-E.wav">| キザっぽい男 |
|man-F|<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/jvs-samples/man-F-minus10.wav"> |<audio controls preload="none" src="https://huggingface.co/litagin/rvc_okiba/resolve/main/song-samples/man-F.wav">| 優しそうなお兄さん |
|
eclipsesnow/csat_model1
|
eclipsesnow
| 2023-10-09T02:58:02Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-09T02:42:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: csat_model1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# csat_model1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
isshogirl/flan-t5-large-financial-phrasebank-lora
|
isshogirl
| 2023-10-09T02:57:03Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-10-08T18:38:19Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
mthxz/GwenRVCV2_V2
|
mthxz
| 2023-10-09T02:54:04Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-10-09T02:45:57Z |
---
license: other
license_name: me
license_link: LICENSE
---
|
lofcz/mistral-7b-dolphin-ff-cs1
|
lofcz
| 2023-10-09T02:53:10Z | 2 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:cognitivecomputations/dolphin-2.0-mistral-7b",
"base_model:adapter:cognitivecomputations/dolphin-2.0-mistral-7b",
"region:us"
] | null | 2023-10-09T02:52:55Z |
---
library_name: peft
base_model: ehartford/dolphin-2.0-mistral-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
lmdeploy/internlm-chat-7b-w4
|
lmdeploy
| 2023-10-09T02:51:54Z | 166 | 3 |
transformers
|
[
"transformers",
"pytorch",
"internlm",
"feature-extraction",
"text-generation-inference",
"text-generation",
"custom_code",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-08-04T11:23:19Z |
---
license: apache-2.0
tags:
- text-generation-inference
pipeline_tag: text-generation
---
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64ccdc322e592905f922a06e/VhwQtaklohkUXFWkjA-3M.png" width="450"/>
English | [简体中文](README_zh-CN.md)
</div>
<p align="center">
👋 join us on <a href="https://twitter.com/intern_lm" target="_blank">Twitter</a>, <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=internwx" target="_blank">WeChat</a>
</p>
# W4A16 LLM Model Deployment
LMDeploy supports LLM model inference of 4-bit weight, with the minimum requirement for NVIDIA graphics cards being sm80.
Before proceeding with the inference, please ensure that lmdeploy(>=v0.0.4) is installed.
```shell
pip install lmdeploy
```
## 4-bit LLM model Inference
You can download the pre-quantized 4-bit weight models from LMDeploy's [model zoo](https://huggingface.co/lmdeploy) and conduct inference using the following command.
Alternatively, you can quantize 16-bit weights to 4-bit weights following the ["4-bit Weight Quantization"](#4-bit-weight-quantization) section, and then perform inference as per the below instructions.
```shell
git-lfs install
git clone https://huggingface.co/lmdeploy/internlm-chat-7b-w4
```
As demonstrated in the command below, first convert the model's layout using `turbomind.deploy`, and then you can interact with the AI assistant in the terminal
```shell
## Convert the model's layout and store it in the default path, ./workspace.
python3 -m lmdeploy.serve.turbomind.deploy \
--model-name internlm \
--model-path ./internlm-chat-7b-w4 \
--model-format awq \
--group-size 128
## inference
python3 -m lmdeploy.turbomind.chat ./workspace
```
## Serve with gradio
If you wish to interact with the model via web ui, please initiate the gradio server as indicated below:
```shell
python3 -m lmdeploy.serve.turbomind ./workspace --server_name {ip_addr} ----server_port {port}
```
Subsequently, you can open the website `http://{ip_addr}:{port}` in your browser and interact with the model
## Inference Performance
We benchmarked the Llama 2 7B and 13B with 4-bit quantization on NVIDIA GeForce RTX 4090 using [profile_generation.py](https://github.com/InternLM/lmdeploy/blob/main/benchmark/profile_generation.py). And we measure the token generation throughput (tokens/s) by setting a single prompt token and generating 512 tokens. All the results are measured for single batch inference.
| model | llm-awq | mlc-llm | turbomind |
| ----------- | ------- | ------- | --------- |
| Llama 2 7B | 112.9 | 159.4 | 206.4 |
| Llama 2 13B | N/A | 90.7 | 115.8 |
```shell
python benchmark/profile_generation.py \
./workspace \
--concurrency 1 --input_seqlen 1 --output_seqlen 512
```
## 4-bit Weight Quantization
It includes two steps:
- generate quantization parameter
- quantize model according to the parameter
### Step 1: Generate Quantization Parameter
```shell
python3 -m lmdeploy.lite.apis.calibrate \
--model $HF_MODEL \
--calib_dataset 'c4' \ # Calibration dataset, supports c4, ptb, wikitext2, pileval
--calib_samples 128 \ # Number of samples in the calibration set, if memory is insufficient, you can appropriately reduce this
--calib_seqlen 2048 \ # Length of a single piece of text, if memory is insufficient, you can appropriately reduce this
--work_dir $WORK_DIR \ # Folder storing Pytorch format quantization statistics parameters and post-quantization weight
```
### Step2: Quantize Weights
LMDeploy employs AWQ algorithm for model weight quantization.
```shell
python3 -m lmdeploy.lite.apis.auto_awq \
--model $HF_MODEL \
--w_bits 4 \ # Bit number for weight quantization
--w_sym False \ # Whether to use symmetric quantization for weights
--w_group_size 128 \ # Group size for weight quantization statistics
--work_dir $WORK_DIR \ # Directory saving quantization parameters from Step 1
```
After the quantization is complete, the quantized model is saved to `$WORK_DIR`. Then you can proceed with model inference according to the instructions in the ["4-Bit Weight Model Inference"](#4-bit-llm-model-inference) section.
|
quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v3
|
quastrinos
| 2023-10-09T02:47:04Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"deberta-v2",
"multiple-choice",
"generated_from_keras_callback",
"base_model:quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU",
"base_model:finetune:quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-10-09T02:46:04Z |
---
license: mit
base_model: quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU
tags:
- generated_from_keras_callback
model-index:
- name: race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v3
This model is a fine-tuned version of [quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU](https://huggingface.co/quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7333
- Validation Loss: 0.9902
- Train Map3: 0.7764
- Train Lr: 5.0733553e-11
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 312, 'alpha': 5e-09, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Train Map3 | Train Lr | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 0.7333 | 0.9902 | 0.7764 | 5.0733553e-11 | 0 |
### Framework versions
- Transformers 4.35.0.dev0
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
hasibirok0/whisper-large-v2-bengali-3000steps
|
hasibirok0
| 2023-10-09T02:42:40Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai/whisper-large-v2",
"base_model:adapter:openai/whisper-large-v2",
"region:us"
] | null | 2023-10-09T02:41:12Z |
---
library_name: peft
base_model: openai/whisper-large-v2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
rlmjy/biogpt_test
|
rlmjy
| 2023-10-09T02:42:15Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T02:42:14Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
Kamal99919/llama2-qlora-finetunined-kamal
|
Kamal99919
| 2023-10-09T02:09:23Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-10-09T02:09:04Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
St4n/wav2vec2-base-960h-demo-google-colab
|
St4n
| 2023-10-09T02:08:18Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base-960h",
"base_model:finetune:facebook/wav2vec2-base-960h",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-09T00:09:16Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-960h-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-960h-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1495
- Wer: 0.1503
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.7708 | 0.42 | 200 | 3.3194 | 0.9999 |
| 3.0354 | 0.84 | 400 | 3.1933 | 0.9999 |
| 2.796 | 1.26 | 600 | 1.4082 | 0.7669 |
| 1.0912 | 1.68 | 800 | 0.8231 | 0.3675 |
| 0.6568 | 2.1 | 1000 | 0.3944 | 0.2863 |
| 0.4604 | 2.52 | 1200 | 0.3303 | 0.2421 |
| 0.3932 | 2.94 | 1400 | 0.2730 | 0.2103 |
| 0.3356 | 3.35 | 1600 | 0.2189 | 0.1789 |
| 0.3117 | 3.77 | 1800 | 0.2189 | 0.1688 |
| 0.2332 | 4.19 | 2000 | 0.1802 | 0.1563 |
| 0.2283 | 4.61 | 2200 | 0.1495 | 0.1503 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Kamal99919/llama2-qlora-finetunined-french
|
Kamal99919
| 2023-10-09T02:06:59Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-10-09T02:06:40Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
xszhou/vit-base-patch16-224-in21k-finetuned-lora-food101
|
xszhou
| 2023-10-09T02:01:17Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T01:54:28Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
fancyerii/t5-large_PREFIX_TUNING_SEQ2SEQ
|
fancyerii
| 2023-10-09T01:57:57Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T01:57:53Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
Shawaylaintown/d
|
Shawaylaintown
| 2023-10-09T01:50:15Z | 0 | 0 |
allennlp
|
[
"allennlp",
"license:apache-2.0",
"region:us"
] | null | 2023-09-23T23:40:59Z |
---
license: apache-2.0
metrics:
- character
library_name: allennlp
---
|
quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v1
|
quastrinos
| 2023-10-09T01:50:12Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"deberta-v2",
"multiple-choice",
"generated_from_keras_callback",
"base_model:quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU",
"base_model:finetune:quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-10-08T20:50:27Z |
---
license: mit
base_model: quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU
tags:
- generated_from_keras_callback
model-index:
- name: race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v1
This model is a fine-tuned version of [quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU](https://huggingface.co/quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7361
- Validation Loss: 0.9878
- Train Map3: 0.7749
- Train Lr: 5.0733553e-11
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 312, 'alpha': 5e-09, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Train Map3 | Train Lr | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 0.7361 | 0.9878 | 0.7749 | 5.0733553e-11 | 0 |
### Framework versions
- Transformers 4.35.0.dev0
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
pinot/wav2vec2-xls-r-300m-ja-phoneme-cv-14_bench
|
pinot
| 2023-10-09T01:39:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_3",
"base_model:finetune:pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-09T01:14:13Z |
---
license: apache-2.0
base_model: pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_3
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-ja-phoneme-cv-14_bench
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.17938553022794845
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-ja-phoneme-cv-14_bench
This model is a fine-tuned version of [pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_3](https://huggingface.co/pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_3) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0125
- Wer: 0.1794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.97 | 9 | 5.2662 | 0.2116 |
| No log | 1.95 | 18 | 2.0125 | 0.1794 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
|
sam-babayev/test__1
|
sam-babayev
| 2023-10-09T01:31:05Z | 0 | 0 | null |
[
"ts",
"model-index",
"region:us"
] | null | 2023-10-08T22:45:27Z |
---
tags:
- ts
model-index:
- name: new7
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 90.25373134328359
- type: ap
value: 65.16915484773354
- type: f1
value: 86.23066728099059
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.974875
- type: ap
value: 91.14317344009288
- type: f1
value: 93.9685240564202
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.77799999999999
- type: f1
value: 55.30626203111084
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.663
- type: map_at_10
value: 43.903
- type: map_at_100
value: 44.779
- type: map_at_1000
value: 44.799
- type: map_at_3
value: 39.486
- type: map_at_5
value: 42.199
- type: mrr_at_1
value: 28.663
- type: mrr_at_10
value: 43.903
- type: mrr_at_100
value: 44.779
- type: mrr_at_1000
value: 44.799
- type: mrr_at_3
value: 39.486
- type: mrr_at_5
value: 42.199
- type: ndcg_at_1
value: 28.663
- type: ndcg_at_10
value: 51.983999999999995
- type: ndcg_at_100
value: 55.981
- type: ndcg_at_1000
value: 56.474000000000004
- type: ndcg_at_3
value: 43.025000000000006
- type: ndcg_at_5
value: 47.916
- type: precision_at_1
value: 28.663
- type: precision_at_10
value: 7.76
- type: precision_at_100
value: 0.9570000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.757
- type: precision_at_5
value: 13.03
- type: recall_at_1
value: 28.663
- type: recall_at_10
value: 77.596
- type: recall_at_100
value: 95.661
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 53.272
- type: recall_at_5
value: 65.149
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 41.06284026514476
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.96711301401968
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.05094332005456
- type: mrr
value: 70.90808160752759
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 93.67415724859552
- type: cos_sim_spearman
value: 93.37019979249912
- type: euclidean_pearson
value: 91.767368542047
- type: euclidean_spearman
value: 92.75874007684216
- type: manhattan_pearson
value: 91.7931347639689
- type: manhattan_spearman
value: 92.94428647331738
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 91.6720779220779
- type: f1
value: 91.68597413806214
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 30.160011542775695
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 24.890267612946595
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.52
- type: map_at_10
value: 31.905
- type: map_at_100
value: 33.146
- type: map_at_1000
value: 33.315
- type: map_at_3
value: 29.567
- type: map_at_5
value: 30.729
- type: mrr_at_1
value: 28.469
- type: mrr_at_10
value: 37.884
- type: mrr_at_100
value: 38.757000000000005
- type: mrr_at_1000
value: 38.827
- type: mrr_at_3
value: 36.004000000000005
- type: mrr_at_5
value: 36.927
- type: ndcg_at_1
value: 28.469
- type: ndcg_at_10
value: 37.436
- type: ndcg_at_100
value: 42.754
- type: ndcg_at_1000
value: 45.744
- type: ndcg_at_3
value: 34.121
- type: ndcg_at_5
value: 35.315000000000005
- type: precision_at_1
value: 28.469
- type: precision_at_10
value: 7.167
- type: precision_at_100
value: 1.24
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 17.072000000000003
- type: precision_at_5
value: 11.731
- type: recall_at_1
value: 22.52
- type: recall_at_10
value: 47.61
- type: recall_at_100
value: 70.494
- type: recall_at_1000
value: 90.081
- type: recall_at_3
value: 37.012
- type: recall_at_5
value: 41.053
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.167
- type: map_at_10
value: 29.227999999999998
- type: map_at_100
value: 30.361
- type: map_at_1000
value: 30.483
- type: map_at_3
value: 27.046
- type: map_at_5
value: 28.253
- type: mrr_at_1
value: 27.961999999999996
- type: mrr_at_10
value: 34.474
- type: mrr_at_100
value: 35.257
- type: mrr_at_1000
value: 35.312
- type: mrr_at_3
value: 32.633
- type: mrr_at_5
value: 33.7
- type: ndcg_at_1
value: 27.961999999999996
- type: ndcg_at_10
value: 33.800000000000004
- type: ndcg_at_100
value: 38.435
- type: ndcg_at_1000
value: 40.753
- type: ndcg_at_3
value: 30.584
- type: ndcg_at_5
value: 32.036
- type: precision_at_1
value: 27.961999999999996
- type: precision_at_10
value: 6.338000000000001
- type: precision_at_100
value: 1.127
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 14.649999999999999
- type: precision_at_5
value: 10.408000000000001
- type: recall_at_1
value: 22.167
- type: recall_at_10
value: 41.735
- type: recall_at_100
value: 61.612
- type: recall_at_1000
value: 77.046
- type: recall_at_3
value: 31.985000000000003
- type: recall_at_5
value: 36.216
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.88
- type: map_at_10
value: 39.483000000000004
- type: map_at_100
value: 40.65
- type: map_at_1000
value: 40.727000000000004
- type: map_at_3
value: 36.095
- type: map_at_5
value: 38.138
- type: mrr_at_1
value: 33.292
- type: mrr_at_10
value: 42.655
- type: mrr_at_100
value: 43.505
- type: mrr_at_1000
value: 43.555
- type: mrr_at_3
value: 39.634
- type: mrr_at_5
value: 41.589999999999996
- type: ndcg_at_1
value: 33.292
- type: ndcg_at_10
value: 45.216
- type: ndcg_at_100
value: 50.029999999999994
- type: ndcg_at_1000
value: 51.795
- type: ndcg_at_3
value: 39.184000000000005
- type: ndcg_at_5
value: 42.416
- type: precision_at_1
value: 33.292
- type: precision_at_10
value: 7.661
- type: precision_at_100
value: 1.089
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 17.701
- type: precision_at_5
value: 12.878
- type: recall_at_1
value: 28.88
- type: recall_at_10
value: 59.148
- type: recall_at_100
value: 80.10300000000001
- type: recall_at_1000
value: 92.938
- type: recall_at_3
value: 43.262
- type: recall_at_5
value: 51.05800000000001
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.732
- type: map_at_10
value: 24.104999999999997
- type: map_at_100
value: 25.085
- type: map_at_1000
value: 25.180000000000003
- type: map_at_3
value: 21.826999999999998
- type: map_at_5
value: 22.988
- type: mrr_at_1
value: 19.209
- type: mrr_at_10
value: 25.528000000000002
- type: mrr_at_100
value: 26.477
- type: mrr_at_1000
value: 26.56
- type: mrr_at_3
value: 23.315
- type: mrr_at_5
value: 24.427
- type: ndcg_at_1
value: 19.209
- type: ndcg_at_10
value: 28.055000000000003
- type: ndcg_at_100
value: 33.357
- type: ndcg_at_1000
value: 35.996
- type: ndcg_at_3
value: 23.526
- type: ndcg_at_5
value: 25.471
- type: precision_at_1
value: 19.209
- type: precision_at_10
value: 4.463
- type: precision_at_100
value: 0.756
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 9.981
- type: precision_at_5
value: 7.119000000000001
- type: recall_at_1
value: 17.732
- type: recall_at_10
value: 39.086999999999996
- type: recall_at_100
value: 64.264
- type: recall_at_1000
value: 84.589
- type: recall_at_3
value: 26.668999999999997
- type: recall_at_5
value: 31.361
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.99
- type: map_at_10
value: 16.661
- type: map_at_100
value: 17.763
- type: map_at_1000
value: 17.892
- type: map_at_3
value: 14.813
- type: map_at_5
value: 15.678
- type: mrr_at_1
value: 13.930000000000001
- type: mrr_at_10
value: 20.25
- type: mrr_at_100
value: 21.233
- type: mrr_at_1000
value: 21.325
- type: mrr_at_3
value: 18.262999999999998
- type: mrr_at_5
value: 19.177
- type: ndcg_at_1
value: 13.930000000000001
- type: ndcg_at_10
value: 20.558
- type: ndcg_at_100
value: 26.137
- type: ndcg_at_1000
value: 29.54
- type: ndcg_at_3
value: 17.015
- type: ndcg_at_5
value: 18.314
- type: precision_at_1
value: 13.930000000000001
- type: precision_at_10
value: 3.9050000000000002
- type: precision_at_100
value: 0.782
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 8.333
- type: precision_at_5
value: 5.92
- type: recall_at_1
value: 10.99
- type: recall_at_10
value: 29.156
- type: recall_at_100
value: 54.06100000000001
- type: recall_at_1000
value: 78.69699999999999
- type: recall_at_3
value: 19.11
- type: recall_at_5
value: 22.609
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.351
- type: map_at_10
value: 29.961
- type: map_at_100
value: 31.214
- type: map_at_1000
value: 31.349
- type: map_at_3
value: 27.283
- type: map_at_5
value: 28.851
- type: mrr_at_1
value: 25.602000000000004
- type: mrr_at_10
value: 34.554
- type: mrr_at_100
value: 35.423
- type: mrr_at_1000
value: 35.492000000000004
- type: mrr_at_3
value: 31.97
- type: mrr_at_5
value: 33.399
- type: ndcg_at_1
value: 25.602000000000004
- type: ndcg_at_10
value: 35.339999999999996
- type: ndcg_at_100
value: 40.89
- type: ndcg_at_1000
value: 43.732
- type: ndcg_at_3
value: 30.657
- type: ndcg_at_5
value: 32.945
- type: precision_at_1
value: 25.602000000000004
- type: precision_at_10
value: 6.574000000000001
- type: precision_at_100
value: 1.095
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 14.629
- type: precision_at_5
value: 10.645
- type: recall_at_1
value: 21.351
- type: recall_at_10
value: 46.754
- type: recall_at_100
value: 70.247
- type: recall_at_1000
value: 89.653
- type: recall_at_3
value: 33.894000000000005
- type: recall_at_5
value: 39.667
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.052999999999997
- type: map_at_10
value: 24.291999999999998
- type: map_at_100
value: 25.348
- type: map_at_1000
value: 25.487
- type: map_at_3
value: 21.922
- type: map_at_5
value: 23.256
- type: mrr_at_1
value: 20.776
- type: mrr_at_10
value: 28.17
- type: mrr_at_100
value: 28.99
- type: mrr_at_1000
value: 29.082
- type: mrr_at_3
value: 25.951
- type: mrr_at_5
value: 27.241
- type: ndcg_at_1
value: 20.776
- type: ndcg_at_10
value: 28.909000000000002
- type: ndcg_at_100
value: 33.917
- type: ndcg_at_1000
value: 37.173
- type: ndcg_at_3
value: 24.769
- type: ndcg_at_5
value: 26.698
- type: precision_at_1
value: 20.776
- type: precision_at_10
value: 5.445
- type: precision_at_100
value: 0.943
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 11.985999999999999
- type: precision_at_5
value: 8.699
- type: recall_at_1
value: 17.052999999999997
- type: recall_at_10
value: 38.922000000000004
- type: recall_at_100
value: 60.624
- type: recall_at_1000
value: 83.83
- type: recall_at_3
value: 27.35
- type: recall_at_5
value: 32.513999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.981
- type: map_at_10
value: 24.99583333333333
- type: map_at_100
value: 26.054083333333335
- type: map_at_1000
value: 26.180916666666672
- type: map_at_3
value: 22.802666666666667
- type: map_at_5
value: 24.00508333333333
- type: mrr_at_1
value: 21.373916666666666
- type: mrr_at_10
value: 28.53433333333333
- type: mrr_at_100
value: 29.404000000000003
- type: mrr_at_1000
value: 29.481999999999996
- type: mrr_at_3
value: 26.462999999999997
- type: mrr_at_5
value: 27.596083333333333
- type: ndcg_at_1
value: 21.373916666666666
- type: ndcg_at_10
value: 29.40908333333333
- type: ndcg_at_100
value: 34.43266666666666
- type: ndcg_at_1000
value: 37.334916666666665
- type: ndcg_at_3
value: 25.518250000000002
- type: ndcg_at_5
value: 27.286916666666666
- type: precision_at_1
value: 21.373916666666666
- type: precision_at_10
value: 5.265666666666667
- type: precision_at_100
value: 0.9175833333333334
- type: precision_at_1000
value: 0.13533333333333336
- type: precision_at_3
value: 11.92425
- type: precision_at_5
value: 8.532250000000001
- type: recall_at_1
value: 17.981
- type: recall_at_10
value: 39.14641666666667
- type: recall_at_100
value: 61.65433333333334
- type: recall_at_1000
value: 82.39216666666665
- type: recall_at_3
value: 28.15266666666667
- type: recall_at_5
value: 32.795
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.834
- type: map_at_10
value: 22.046
- type: map_at_100
value: 22.954
- type: map_at_1000
value: 23.051
- type: map_at_3
value: 20.602999999999998
- type: map_at_5
value: 21.387999999999998
- type: mrr_at_1
value: 19.172
- type: mrr_at_10
value: 24.558
- type: mrr_at_100
value: 25.439
- type: mrr_at_1000
value: 25.509999999999998
- type: mrr_at_3
value: 23.185
- type: mrr_at_5
value: 23.852
- type: ndcg_at_1
value: 19.172
- type: ndcg_at_10
value: 25.189
- type: ndcg_at_100
value: 29.918
- type: ndcg_at_1000
value: 32.677
- type: ndcg_at_3
value: 22.496
- type: ndcg_at_5
value: 23.677
- type: precision_at_1
value: 19.172
- type: precision_at_10
value: 3.834
- type: precision_at_100
value: 0.679
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 9.611
- type: precision_at_5
value: 6.4719999999999995
- type: recall_at_1
value: 16.834
- type: recall_at_10
value: 32.554
- type: recall_at_100
value: 54.416
- type: recall_at_1000
value: 75.334
- type: recall_at_3
value: 25.057000000000002
- type: recall_at_5
value: 28.155
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.778
- type: map_at_10
value: 15.885
- type: map_at_100
value: 16.716
- type: map_at_1000
value: 16.838
- type: map_at_3
value: 14.283999999999999
- type: map_at_5
value: 15.067
- type: mrr_at_1
value: 13.421
- type: mrr_at_10
value: 19.022
- type: mrr_at_100
value: 19.819
- type: mrr_at_1000
value: 19.912
- type: mrr_at_3
value: 17.366
- type: mrr_at_5
value: 18.18
- type: ndcg_at_1
value: 13.421
- type: ndcg_at_10
value: 19.375
- type: ndcg_at_100
value: 23.733999999999998
- type: ndcg_at_1000
value: 26.878
- type: ndcg_at_3
value: 16.383
- type: ndcg_at_5
value: 17.53
- type: precision_at_1
value: 13.421
- type: precision_at_10
value: 3.637
- type: precision_at_100
value: 0.681
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 7.983
- type: precision_at_5
value: 5.671
- type: recall_at_1
value: 10.778
- type: recall_at_10
value: 26.985999999999997
- type: recall_at_100
value: 47.143
- type: recall_at_1000
value: 69.842
- type: recall_at_3
value: 18.289
- type: recall_at_5
value: 21.459
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.077
- type: map_at_10
value: 23.31
- type: map_at_100
value: 24.351
- type: map_at_1000
value: 24.471
- type: map_at_3
value: 21.272
- type: map_at_5
value: 22.320999999999998
- type: mrr_at_1
value: 19.683
- type: mrr_at_10
value: 26.44
- type: mrr_at_100
value: 27.395000000000003
- type: mrr_at_1000
value: 27.479
- type: mrr_at_3
value: 24.549000000000003
- type: mrr_at_5
value: 25.477
- type: ndcg_at_1
value: 19.683
- type: ndcg_at_10
value: 27.33
- type: ndcg_at_100
value: 32.595
- type: ndcg_at_1000
value: 35.671
- type: ndcg_at_3
value: 23.536
- type: ndcg_at_5
value: 25.09
- type: precision_at_1
value: 19.683
- type: precision_at_10
value: 4.711
- type: precision_at_100
value: 0.84
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 10.697
- type: precision_at_5
value: 7.5
- type: recall_at_1
value: 17.077
- type: recall_at_10
value: 36.532
- type: recall_at_100
value: 59.955999999999996
- type: recall_at_1000
value: 82.536
- type: recall_at_3
value: 25.982
- type: recall_at_5
value: 29.965999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.137
- type: map_at_10
value: 23.889
- type: map_at_100
value: 25.089
- type: map_at_1000
value: 25.284000000000002
- type: map_at_3
value: 21.844
- type: map_at_5
value: 23.185
- type: mrr_at_1
value: 20.552999999999997
- type: mrr_at_10
value: 27.996
- type: mrr_at_100
value: 28.921000000000003
- type: mrr_at_1000
value: 28.999999999999996
- type: mrr_at_3
value: 25.955000000000002
- type: mrr_at_5
value: 27.269
- type: ndcg_at_1
value: 20.552999999999997
- type: ndcg_at_10
value: 28.555000000000003
- type: ndcg_at_100
value: 34.035
- type: ndcg_at_1000
value: 37.466
- type: ndcg_at_3
value: 25.105
- type: ndcg_at_5
value: 27.13
- type: precision_at_1
value: 20.552999999999997
- type: precision_at_10
value: 5.534
- type: precision_at_100
value: 1.117
- type: precision_at_1000
value: 0.20400000000000001
- type: precision_at_3
value: 12.253
- type: precision_at_5
value: 9.17
- type: recall_at_1
value: 17.137
- type: recall_at_10
value: 37.527
- type: recall_at_100
value: 62.905
- type: recall_at_1000
value: 85.839
- type: recall_at_3
value: 27.262999999999998
- type: recall_at_5
value: 32.735
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.253
- type: map_at_10
value: 19.185
- type: map_at_100
value: 19.972
- type: map_at_1000
value: 20.094
- type: map_at_3
value: 17.076
- type: map_at_5
value: 18.207
- type: mrr_at_1
value: 14.418000000000001
- type: mrr_at_10
value: 20.881
- type: mrr_at_100
value: 21.632
- type: mrr_at_1000
value: 21.73
- type: mrr_at_3
value: 18.731
- type: mrr_at_5
value: 19.914
- type: ndcg_at_1
value: 14.418000000000001
- type: ndcg_at_10
value: 23.146
- type: ndcg_at_100
value: 27.389999999999997
- type: ndcg_at_1000
value: 30.593999999999998
- type: ndcg_at_3
value: 18.843
- type: ndcg_at_5
value: 20.821
- type: precision_at_1
value: 14.418000000000001
- type: precision_at_10
value: 3.9190000000000005
- type: precision_at_100
value: 0.662
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 8.195
- type: precision_at_5
value: 6.174
- type: recall_at_1
value: 13.253
- type: recall_at_10
value: 33.745999999999995
- type: recall_at_100
value: 54.027
- type: recall_at_1000
value: 78.321
- type: recall_at_3
value: 21.959
- type: recall_at_5
value: 26.747
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 9.446
- type: ndcg_at_3
value: 8.708
- type: ndcg_at_5
value: 9.583
- type: ndcg_at_10
value: 11.324
- type: ndcg_at_100
value: 16.563
- type: ndcg_at_1000
value: 20.402
- type: map_at_1
value: 4.407
- type: map_at_3
value: 6.283999999999999
- type: map_at_5
value: 6.888
- type: map_at_10
value: 7.545
- type: map_at_100
value: 8.502
- type: map_at_1000
value: 8.677
- type: recall_at_1
value: 4.407
- type: recall_at_3
value: 8.341999999999999
- type: recall_at_5
value: 10.609
- type: recall_at_10
value: 14.572
- type: recall_at_100
value: 33.802
- type: recall_at_1000
value: 56.13
- type: precision_at_1
value: 9.446
- type: precision_at_3
value: 6.3839999999999995
- type: precision_at_5
value: 5.029
- type: precision_at_10
value: 3.655
- type: precision_at_100
value: 0.9169999999999999
- type: precision_at_1000
value: 0.159
- type: mrr_at_1
value: 9.446
- type: mrr_at_3
value: 12.975
- type: mrr_at_5
value: 14.102
- type: mrr_at_10
value: 15.223999999999998
- type: mrr_at_100
value: 16.378
- type: mrr_at_1000
value: 16.469
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.3839999999999995
- type: map_at_10
value: 8.92
- type: map_at_100
value: 12.509999999999998
- type: map_at_1000
value: 13.555
- type: map_at_3
value: 6.508
- type: map_at_5
value: 7.521
- type: mrr_at_1
value: 38.0
- type: mrr_at_10
value: 47.796
- type: mrr_at_100
value: 48.554
- type: mrr_at_1000
value: 48.579
- type: mrr_at_3
value: 44.708
- type: mrr_at_5
value: 46.521
- type: ndcg_at_1
value: 29.125
- type: ndcg_at_10
value: 22.126
- type: ndcg_at_100
value: 26.369999999999997
- type: ndcg_at_1000
value: 33.604
- type: ndcg_at_3
value: 24.102999999999998
- type: ndcg_at_5
value: 22.926
- type: precision_at_1
value: 38.0
- type: precision_at_10
value: 18.2
- type: precision_at_100
value: 6.208
- type: precision_at_1000
value: 1.3679999999999999
- type: precision_at_3
value: 26.5
- type: precision_at_5
value: 22.900000000000002
- type: recall_at_1
value: 4.3839999999999995
- type: recall_at_10
value: 13.520999999999999
- type: recall_at_100
value: 33.053
- type: recall_at_1000
value: 56.516
- type: recall_at_3
value: 7.515
- type: recall_at_5
value: 9.775
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 90.38999999999999
- type: f1
value: 87.12778738994012
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.132
- type: map_at_10
value: 79.527
- type: map_at_100
value: 79.81200000000001
- type: map_at_1000
value: 79.828
- type: map_at_3
value: 78.191
- type: map_at_5
value: 79.092
- type: mrr_at_1
value: 75.563
- type: mrr_at_10
value: 83.80199999999999
- type: mrr_at_100
value: 83.93
- type: mrr_at_1000
value: 83.933
- type: mrr_at_3
value: 82.818
- type: mrr_at_5
value: 83.505
- type: ndcg_at_1
value: 75.563
- type: ndcg_at_10
value: 83.692
- type: ndcg_at_100
value: 84.706
- type: ndcg_at_1000
value: 85.001
- type: ndcg_at_3
value: 81.51
- type: ndcg_at_5
value: 82.832
- type: precision_at_1
value: 75.563
- type: precision_at_10
value: 10.245
- type: precision_at_100
value: 1.0959999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 31.518
- type: precision_at_5
value: 19.772000000000002
- type: recall_at_1
value: 70.132
- type: recall_at_10
value: 92.204
- type: recall_at_100
value: 96.261
- type: recall_at_1000
value: 98.17399999999999
- type: recall_at_3
value: 86.288
- type: recall_at_5
value: 89.63799999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.688000000000001
- type: map_at_10
value: 13.839000000000002
- type: map_at_100
value: 15.082999999999998
- type: map_at_1000
value: 15.276
- type: map_at_3
value: 11.662
- type: map_at_5
value: 12.827
- type: mrr_at_1
value: 15.741
- type: mrr_at_10
value: 23.304
- type: mrr_at_100
value: 24.239
- type: mrr_at_1000
value: 24.319
- type: mrr_at_3
value: 20.962
- type: mrr_at_5
value: 22.243
- type: ndcg_at_1
value: 15.741
- type: ndcg_at_10
value: 18.914
- type: ndcg_at_100
value: 24.742
- type: ndcg_at_1000
value: 28.938000000000002
- type: ndcg_at_3
value: 16.181
- type: ndcg_at_5
value: 17.078
- type: precision_at_1
value: 15.741
- type: precision_at_10
value: 5.7410000000000005
- type: precision_at_100
value: 1.168
- type: precision_at_1000
value: 0.19
- type: precision_at_3
value: 11.368
- type: precision_at_5
value: 8.735
- type: recall_at_1
value: 7.688000000000001
- type: recall_at_10
value: 24.442
- type: recall_at_100
value: 47.288999999999994
- type: recall_at_1000
value: 73.49900000000001
- type: recall_at_3
value: 15.15
- type: recall_at_5
value: 18.858
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.412
- type: map_at_10
value: 66.376
- type: map_at_100
value: 67.217
- type: map_at_1000
value: 67.271
- type: map_at_3
value: 62.741
- type: map_at_5
value: 65.069
- type: mrr_at_1
value: 80.824
- type: mrr_at_10
value: 86.53
- type: mrr_at_100
value: 86.67399999999999
- type: mrr_at_1000
value: 86.678
- type: mrr_at_3
value: 85.676
- type: mrr_at_5
value: 86.256
- type: ndcg_at_1
value: 80.824
- type: ndcg_at_10
value: 74.332
- type: ndcg_at_100
value: 77.154
- type: ndcg_at_1000
value: 78.12400000000001
- type: ndcg_at_3
value: 69.353
- type: ndcg_at_5
value: 72.234
- type: precision_at_1
value: 80.824
- type: precision_at_10
value: 15.652
- type: precision_at_100
value: 1.7840000000000003
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 44.911
- type: precision_at_5
value: 29.221000000000004
- type: recall_at_1
value: 40.412
- type: recall_at_10
value: 78.25800000000001
- type: recall_at_100
value: 89.196
- type: recall_at_1000
value: 95.544
- type: recall_at_3
value: 67.367
- type: recall_at_5
value: 73.05199999999999
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 88.8228
- type: ap
value: 84.52103126779862
- type: f1
value: 88.797782219813
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 8.461
- type: map_at_10
value: 14.979999999999999
- type: map_at_100
value: 16.032
- type: map_at_1000
value: 16.128
- type: map_at_3
value: 12.64
- type: map_at_5
value: 13.914000000000001
- type: mrr_at_1
value: 8.681999999999999
- type: mrr_at_10
value: 15.341
- type: mrr_at_100
value: 16.377
- type: mrr_at_1000
value: 16.469
- type: mrr_at_3
value: 12.963
- type: mrr_at_5
value: 14.262
- type: ndcg_at_1
value: 8.681999999999999
- type: ndcg_at_10
value: 19.045
- type: ndcg_at_100
value: 24.735
- type: ndcg_at_1000
value: 27.556000000000004
- type: ndcg_at_3
value: 14.154
- type: ndcg_at_5
value: 16.448
- type: precision_at_1
value: 8.681999999999999
- type: precision_at_10
value: 3.292
- type: precision_at_100
value: 0.623
- type: precision_at_1000
value: 0.087
- type: precision_at_3
value: 6.275
- type: precision_at_5
value: 4.92
- type: recall_at_1
value: 8.461
- type: recall_at_10
value: 31.729000000000003
- type: recall_at_100
value: 59.367000000000004
- type: recall_at_1000
value: 81.86
- type: recall_at_3
value: 18.234
- type: recall_at_5
value: 23.74
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 98.1623347013224
- type: f1
value: 97.95934123221338
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 93.0141358869129
- type: f1
value: 77.42161481798763
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 77.20242098184264
- type: f1
value: 73.64580701123289
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 88.38264963012777
- type: f1
value: 87.6445935642575
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 28.982276213044095
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 26.08731318128303
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.680164236394784
- type: mrr
value: 30.60242075910688
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.35
- type: map_at_10
value: 10.03
- type: map_at_100
value: 12.61
- type: map_at_1000
value: 13.916999999999998
- type: map_at_3
value: 7.428
- type: map_at_5
value: 8.625
- type: mrr_at_1
value: 39.009
- type: mrr_at_10
value: 47.63
- type: mrr_at_100
value: 48.259
- type: mrr_at_1000
value: 48.302
- type: mrr_at_3
value: 45.408
- type: mrr_at_5
value: 46.971000000000004
- type: ndcg_at_1
value: 36.997
- type: ndcg_at_10
value: 28.781000000000002
- type: ndcg_at_100
value: 26.644000000000002
- type: ndcg_at_1000
value: 35.812
- type: ndcg_at_3
value: 34.056
- type: ndcg_at_5
value: 31.804
- type: precision_at_1
value: 38.080000000000005
- type: precision_at_10
value: 20.96
- type: precision_at_100
value: 6.808
- type: precision_at_1000
value: 1.991
- type: precision_at_3
value: 32.095
- type: precision_at_5
value: 27.43
- type: recall_at_1
value: 4.35
- type: recall_at_10
value: 14.396
- type: recall_at_100
value: 28.126
- type: recall_at_1000
value: 60.785
- type: recall_at_3
value: 9.001000000000001
- type: recall_at_5
value: 11.197
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.408
- type: map_at_10
value: 17.247
- type: map_at_100
value: 18.578
- type: map_at_1000
value: 18.683
- type: map_at_3
value: 14.424999999999999
- type: map_at_5
value: 15.967999999999998
- type: mrr_at_1
value: 10.718
- type: mrr_at_10
value: 18.974
- type: mrr_at_100
value: 20.153
- type: mrr_at_1000
value: 20.238
- type: mrr_at_3
value: 16.087
- type: mrr_at_5
value: 17.685000000000002
- type: ndcg_at_1
value: 10.718
- type: ndcg_at_10
value: 22.313
- type: ndcg_at_100
value: 28.810999999999996
- type: ndcg_at_1000
value: 31.495
- type: ndcg_at_3
value: 16.487
- type: ndcg_at_5
value: 19.252
- type: precision_at_1
value: 10.718
- type: precision_at_10
value: 4.256
- type: precision_at_100
value: 0.7979999999999999
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 7.976
- type: precision_at_5
value: 6.3149999999999995
- type: recall_at_1
value: 9.408
- type: recall_at_10
value: 36.364999999999995
- type: recall_at_100
value: 66.16499999999999
- type: recall_at_1000
value: 86.47399999999999
- type: recall_at_3
value: 20.829
- type: recall_at_5
value: 27.296
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 65.499
- type: map_at_10
value: 78.432
- type: map_at_100
value: 79.169
- type: map_at_1000
value: 79.199
- type: map_at_3
value: 75.476
- type: map_at_5
value: 77.28399999999999
- type: mrr_at_1
value: 75.55
- type: mrr_at_10
value: 82.16499999999999
- type: mrr_at_100
value: 82.37
- type: mrr_at_1000
value: 82.375
- type: mrr_at_3
value: 80.925
- type: mrr_at_5
value: 81.748
- type: ndcg_at_1
value: 75.58
- type: ndcg_at_10
value: 82.663
- type: ndcg_at_100
value: 84.526
- type: ndcg_at_1000
value: 84.843
- type: ndcg_at_3
value: 79.38300000000001
- type: ndcg_at_5
value: 81.133
- type: precision_at_1
value: 75.58
- type: precision_at_10
value: 12.562000000000001
- type: precision_at_100
value: 1.48
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 34.583000000000006
- type: precision_at_5
value: 22.858
- type: recall_at_1
value: 65.499
- type: recall_at_10
value: 90.71000000000001
- type: recall_at_100
value: 97.717
- type: recall_at_1000
value: 99.551
- type: recall_at_3
value: 81.273
- type: recall_at_5
value: 86.172
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 43.28689524907211
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 54.41734813535957
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.305
- type: map_at_10
value: 8.502
- type: map_at_100
value: 10.288
- type: map_at_1000
value: 10.599
- type: map_at_3
value: 6.146
- type: map_at_5
value: 7.207
- type: mrr_at_1
value: 16.400000000000002
- type: mrr_at_10
value: 26.054
- type: mrr_at_100
value: 27.319
- type: mrr_at_1000
value: 27.400000000000002
- type: mrr_at_3
value: 22.967000000000002
- type: mrr_at_5
value: 24.542
- type: ndcg_at_1
value: 16.400000000000002
- type: ndcg_at_10
value: 14.943000000000001
- type: ndcg_at_100
value: 22.596
- type: ndcg_at_1000
value: 28.345
- type: ndcg_at_3
value: 14.011000000000001
- type: ndcg_at_5
value: 12.065
- type: precision_at_1
value: 16.400000000000002
- type: precision_at_10
value: 7.93
- type: precision_at_100
value: 1.902
- type: precision_at_1000
value: 0.328
- type: precision_at_3
value: 13.233
- type: precision_at_5
value: 10.620000000000001
- type: recall_at_1
value: 3.305
- type: recall_at_10
value: 16.07
- type: recall_at_100
value: 38.592999999999996
- type: recall_at_1000
value: 66.678
- type: recall_at_3
value: 8.025
- type: recall_at_5
value: 10.743
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 94.03602783680165
- type: cos_sim_spearman
value: 91.93466287712853
- type: euclidean_pearson
value: 91.5804659261222
- type: euclidean_spearman
value: 91.84239224991634
- type: manhattan_pearson
value: 91.57789872896991
- type: manhattan_spearman
value: 91.82031929038708
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 97.2530615783017
- type: cos_sim_spearman
value: 95.61025838976805
- type: euclidean_pearson
value: 95.41071037458771
- type: euclidean_spearman
value: 95.6207550803838
- type: manhattan_pearson
value: 95.39723545188045
- type: manhattan_spearman
value: 95.61540593501014
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 95.27491458980685
- type: cos_sim_spearman
value: 95.1521844663505
- type: euclidean_pearson
value: 94.63883752108002
- type: euclidean_spearman
value: 94.85954995945424
- type: manhattan_pearson
value: 94.59749433419627
- type: manhattan_spearman
value: 94.80626857571967
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 97.10518525877228
- type: cos_sim_spearman
value: 96.85836209648471
- type: euclidean_pearson
value: 95.8019730340664
- type: euclidean_spearman
value: 96.78892865690494
- type: manhattan_pearson
value: 95.79265816494754
- type: manhattan_spearman
value: 96.7712534155723
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 96.66550105336606
- type: cos_sim_spearman
value: 96.73134982392861
- type: euclidean_pearson
value: 95.50375963201927
- type: euclidean_spearman
value: 96.46785996403956
- type: manhattan_pearson
value: 95.47555707089327
- type: manhattan_spearman
value: 96.40825860300748
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 96.07365154052914
- type: cos_sim_spearman
value: 96.1720485037732
- type: euclidean_pearson
value: 95.58880196128803
- type: euclidean_spearman
value: 96.02102007396296
- type: manhattan_pearson
value: 95.60295336628664
- type: manhattan_spearman
value: 96.03461694944212
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 96.14907313714893
- type: cos_sim_spearman
value: 96.14822520805113
- type: euclidean_pearson
value: 95.62140726773103
- type: euclidean_spearman
value: 96.01818385482282
- type: manhattan_pearson
value: 95.60795162280982
- type: manhattan_spearman
value: 96.00703635484169
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.35513203366195
- type: cos_sim_spearman
value: 64.92002333937089
- type: euclidean_pearson
value: 67.06304516009153
- type: euclidean_spearman
value: 65.3504536039936
- type: manhattan_pearson
value: 67.22016756598737
- type: manhattan_spearman
value: 65.64455991383844
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 96.59372149477922
- type: cos_sim_spearman
value: 96.97247348665515
- type: euclidean_pearson
value: 95.64890160850817
- type: euclidean_spearman
value: 96.84619618958573
- type: manhattan_pearson
value: 95.65581449537562
- type: manhattan_spearman
value: 96.853383309355
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.9991957697061
- type: mrr
value: 93.85864317236866
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 42.25
- type: map_at_10
value: 51.257
- type: map_at_100
value: 52.261
- type: map_at_1000
value: 52.309000000000005
- type: map_at_3
value: 48.759
- type: map_at_5
value: 50.413
- type: mrr_at_1
value: 44.0
- type: mrr_at_10
value: 52.367
- type: mrr_at_100
value: 53.181999999999995
- type: mrr_at_1000
value: 53.223
- type: mrr_at_3
value: 50.222
- type: mrr_at_5
value: 51.656
- type: ndcg_at_1
value: 44.0
- type: ndcg_at_10
value: 55.672
- type: ndcg_at_100
value: 59.779
- type: ndcg_at_1000
value: 61.114999999999995
- type: ndcg_at_3
value: 51.136
- type: ndcg_at_5
value: 53.822
- type: precision_at_1
value: 44.0
- type: precision_at_10
value: 7.6
- type: precision_at_100
value: 0.9730000000000001
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 20.111
- type: precision_at_5
value: 13.733
- type: recall_at_1
value: 42.25
- type: recall_at_10
value: 67.989
- type: recall_at_100
value: 85.56700000000001
- type: recall_at_1000
value: 96.267
- type: recall_at_3
value: 56.27799999999999
- type: recall_at_5
value: 62.678
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.75346534653465
- type: cos_sim_ap
value: 92.92934020206276
- type: cos_sim_f1
value: 87.37373737373737
- type: cos_sim_precision
value: 88.26530612244898
- type: cos_sim_recall
value: 86.5
- type: dot_accuracy
value: 99.7
- type: dot_ap
value: 90.30253078505329
- type: dot_f1
value: 84.55696202531644
- type: dot_precision
value: 85.64102564102564
- type: dot_recall
value: 83.5
- type: euclidean_accuracy
value: 99.75742574257426
- type: euclidean_ap
value: 92.97542565802068
- type: euclidean_f1
value: 87.48083801737351
- type: euclidean_precision
value: 89.44618599791013
- type: euclidean_recall
value: 85.6
- type: manhattan_accuracy
value: 99.75643564356436
- type: manhattan_ap
value: 92.92733519229752
- type: manhattan_f1
value: 87.41044012282498
- type: manhattan_precision
value: 89.51781970649894
- type: manhattan_recall
value: 85.39999999999999
- type: max_accuracy
value: 99.75742574257426
- type: max_ap
value: 92.97542565802068
- type: max_f1
value: 87.48083801737351
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 46.968629347107225
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.76101811464947
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 47.838618465936364
- type: mrr
value: 48.51134772090654
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.101149949190837
- type: cos_sim_spearman
value: 30.99886288816569
- type: dot_pearson
value: 28.905040829977978
- type: dot_spearman
value: 28.101690957830428
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.129
- type: map_at_10
value: 0.6930000000000001
- type: map_at_100
value: 2.408
- type: map_at_1000
value: 4.731
- type: map_at_3
value: 0.314
- type: map_at_5
value: 0.43
- type: mrr_at_1
value: 44.0
- type: mrr_at_10
value: 55.132999999999996
- type: mrr_at_100
value: 56.455
- type: mrr_at_1000
value: 56.474000000000004
- type: mrr_at_3
value: 53.333
- type: mrr_at_5
value: 55.132999999999996
- type: ndcg_at_1
value: 40.0
- type: ndcg_at_10
value: 33.283
- type: ndcg_at_100
value: 18.892
- type: ndcg_at_1000
value: 17.457
- type: ndcg_at_3
value: 39.073
- type: ndcg_at_5
value: 35.609
- type: precision_at_1
value: 44.0
- type: precision_at_10
value: 33.800000000000004
- type: precision_at_100
value: 17.44
- type: precision_at_1000
value: 7.04
- type: precision_at_3
value: 40.666999999999994
- type: precision_at_5
value: 36.4
- type: recall_at_1
value: 0.129
- type: recall_at_10
value: 0.91
- type: recall_at_100
value: 4.449
- type: recall_at_1000
value: 16.091
- type: recall_at_3
value: 0.349
- type: recall_at_5
value: 0.518
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.189
- type: map_at_10
value: 5.196
- type: map_at_100
value: 8.984
- type: map_at_1000
value: 10.333
- type: map_at_3
value: 2.513
- type: map_at_5
value: 3.8089999999999997
- type: mrr_at_1
value: 14.285999999999998
- type: mrr_at_10
value: 26.295
- type: mrr_at_100
value: 28.285
- type: mrr_at_1000
value: 28.303
- type: mrr_at_3
value: 22.109
- type: mrr_at_5
value: 24.864
- type: ndcg_at_1
value: 12.245000000000001
- type: ndcg_at_10
value: 13.196
- type: ndcg_at_100
value: 24.189
- type: ndcg_at_1000
value: 36.015
- type: ndcg_at_3
value: 12.153
- type: ndcg_at_5
value: 13.459999999999999
- type: precision_at_1
value: 14.285999999999998
- type: precision_at_10
value: 12.653
- type: precision_at_100
value: 5.673
- type: precision_at_1000
value: 1.32
- type: precision_at_3
value: 12.925
- type: precision_at_5
value: 15.101999999999999
- type: recall_at_1
value: 1.189
- type: recall_at_10
value: 9.478
- type: recall_at_100
value: 36.076
- type: recall_at_1000
value: 71.88900000000001
- type: recall_at_3
value: 3.1710000000000003
- type: recall_at_5
value: 5.944
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 81.1632
- type: ap
value: 21.801031224655016
- type: f1
value: 63.93057804886679
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 68.15789473684211
- type: f1
value: 68.55744497973521
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.77313771942972
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.79603027954938
- type: cos_sim_ap
value: 73.19931192854375
- type: cos_sim_f1
value: 66.7699457784663
- type: cos_sim_precision
value: 65.3690596562184
- type: cos_sim_recall
value: 68.23218997361478
- type: dot_accuracy
value: 84.72313286046374
- type: dot_ap
value: 69.84066382008972
- type: dot_f1
value: 64.42618869803336
- type: dot_precision
value: 60.98020735155514
- type: dot_recall
value: 68.28496042216359
- type: euclidean_accuracy
value: 85.81391190320082
- type: euclidean_ap
value: 73.4051677083228
- type: euclidean_f1
value: 67.35092864125122
- type: euclidean_precision
value: 62.721893491124256
- type: euclidean_recall
value: 72.71767810026385
- type: manhattan_accuracy
value: 85.81391190320082
- type: manhattan_ap
value: 73.33759860950396
- type: manhattan_f1
value: 67.32576589771757
- type: manhattan_precision
value: 62.63910969793323
- type: manhattan_recall
value: 72.77044854881267
- type: max_accuracy
value: 85.81391190320082
- type: max_ap
value: 73.4051677083228
- type: max_f1
value: 67.35092864125122
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.17479722125199
- type: cos_sim_ap
value: 84.37486145048878
- type: cos_sim_f1
value: 76.65294717365856
- type: cos_sim_precision
value: 75.21304186735827
- type: cos_sim_recall
value: 78.14906067138897
- type: dot_accuracy
value: 87.72460899600264
- type: dot_ap
value: 83.01188676406672
- type: dot_f1
value: 75.8810775054206
- type: dot_precision
value: 72.58665541728186
- type: dot_recall
value: 79.48875885432707
- type: euclidean_accuracy
value: 88.16315442232313
- type: euclidean_ap
value: 84.32021529803454
- type: euclidean_f1
value: 76.60147856804691
- type: euclidean_precision
value: 72.67638725727316
- type: euclidean_recall
value: 80.97474591931014
- type: manhattan_accuracy
value: 88.19226141964528
- type: manhattan_ap
value: 84.30111334073442
- type: manhattan_f1
value: 76.48944401459048
- type: manhattan_precision
value: 73.34134105843285
- type: manhattan_recall
value: 79.91992608561749
- type: max_accuracy
value: 88.19226141964528
- type: max_ap
value: 84.37486145048878
- type: max_f1
value: 76.65294717365856
---
|
praneethvasarla/bert-finetuned-conll-ner
|
praneethvasarla
| 2023-10-09T01:14:53Z | 109 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-06T02:11:20Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-conll-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9371267418712674
- name: Recall
type: recall
value: 0.9506900033658701
- name: F1
type: f1
value: 0.9438596491228071
- name: Accuracy
type: accuracy
value: 0.986504385706717
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-conll-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
This uses the Cased version of Bert, so keep the casing unchanged before using this model
It achieves the following results on the evaluation set:
- Loss: 0.0615
- Precision: 0.9371
- Recall: 0.9507
- F1: 0.9439
- Accuracy: 0.9865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0766 | 1.0 | 1756 | 0.0793 | 0.9100 | 0.9360 | 0.9228 | 0.9795 |
| 0.0416 | 2.0 | 3512 | 0.0602 | 0.9283 | 0.9473 | 0.9377 | 0.9857 |
| 0.0253 | 3.0 | 5268 | 0.0615 | 0.9371 | 0.9507 | 0.9439 | 0.9865 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
diwank/dfe-base-en-1
|
diwank
| 2023-10-09T00:55:54Z | 1,257 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-10-09T00:55:46Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# diwank/dfe-base-en-1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1536 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('diwank/dfe-base-en-1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=diwank/dfe-base-en-1)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2562 with parameters:
```
{'batch_size': 1320, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 6,
"evaluation_steps": 1500,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'lion_pytorch.lion_pytorch.Lion'>",
"optimizer_params": {
"lr": 0.0001,
"weight_decay": 0.01
},
"scheduler": "WarmupCosine",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Asym(
(dialog-0): Dense({'in_features': 768, 'out_features': 1536, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(dialog-1): Dense({'in_features': 1536, 'out_features': 1536, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(dialog-2): Dense({'in_features': 1536, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(fact-0): Dense({'in_features': 768, 'out_features': 1536, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(fact-1): Dense({'in_features': 1536, 'out_features': 1536, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(fact-2): Dense({'in_features': 1536, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Tural/bert-base-uncased-ml
|
Tural
| 2023-10-09T00:51:39Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-06T20:03:31Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-ml
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-ml
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1621
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 150
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.7729 | 1.0 | 14050 | 3.8005 |
| 2.408 | 2.0 | 28100 | 2.3630 |
| 2.1739 | 3.0 | 42150 | 2.1621 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
VuongQuoc/checkpoints_10_1_microsoft_deberta_V1.1_384
|
VuongQuoc
| 2023-10-09T00:32:38Z | 66 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"multiple-choice",
"generated_from_trainer",
"base_model:VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384",
"base_model:finetune:VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-10-01T11:49:45Z |
---
base_model: VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoints_10_1_microsoft_deberta_V1.1_384
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# checkpoints_10_1_microsoft_deberta_V1.1_384
This model is a fine-tuned version of [VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384](https://huggingface.co/VuongQuoc/checkpoints_30_9_microsoft_deberta_V1.0_384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7688
- Map@3: 0.8458
- Accuracy: 0.75
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map@3 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.5583 | 0.05 | 100 | 1.4269 | 0.7675 | 0.65 |
| 1.1541 | 0.11 | 200 | 1.0838 | 0.7692 | 0.67 |
| 1.0124 | 0.16 | 300 | 0.9475 | 0.8108 | 0.715 |
| 0.9627 | 0.21 | 400 | 0.8969 | 0.8233 | 0.73 |
| 0.9241 | 0.27 | 500 | 0.8473 | 0.8392 | 0.755 |
| 0.885 | 0.32 | 600 | 0.8336 | 0.8333 | 0.745 |
| 0.8606 | 0.37 | 700 | 0.7937 | 0.8508 | 0.76 |
| 0.8495 | 0.43 | 800 | 0.7755 | 0.8517 | 0.76 |
| 0.8787 | 0.48 | 900 | 0.7706 | 0.8475 | 0.75 |
| 0.8535 | 0.53 | 1000 | 0.7714 | 0.8458 | 0.75 |
| 0.8499 | 0.59 | 1100 | 0.7694 | 0.8458 | 0.75 |
| 0.8353 | 0.64 | 1200 | 0.7688 | 0.8458 | 0.75 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.0
- Datasets 2.9.0
- Tokenizers 0.13.3
|
paisanx/ppo-LunarLander-v2-test
|
paisanx
| 2023-10-09T00:28:31Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-09T00:27:48Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 115.31 +/- 85.24
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
NysPsycho/TrailerParkSteve
|
NysPsycho
| 2023-10-09T00:28:30Z | 0 | 0 | null |
[
"graph-ml",
"en",
"license:mit",
"region:us"
] |
graph-ml
| 2023-10-09T00:10:31Z |
---
license: mit
language:
- en
metrics:
- accuracy
- recall
pipeline_tag: graph-ml
---
|
TanmaySah/jan
|
TanmaySah
| 2023-10-09T00:23:07Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-04T21:14:20Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
|
Brecon/bert_validation_model
|
Brecon
| 2023-10-09T00:18:42Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-04T20:43:15Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Brecon/bert_validation_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Brecon/bert_validation_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0222
- Validation Loss: 1.0468
- Train Accuracy: 0.3182
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 50, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.0559 | 1.0606 | 0.4091 | 0 |
| 1.0222 | 1.0468 | 0.3182 | 1 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.11.0
|
thevyasamit/t5-fine-tuned-with-25-yake-keywords
|
thevyasamit
| 2023-10-08T23:55:25Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-07T17:47:05Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-fine-tuned-with-25-yake-keywords
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-fine-tuned-with-25-yake-keywords
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7255
- Rouge1: 25.5531
- Rouge2: 11.1657
- Rougel: 20.7513
- Rougelsum: 24.054
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3097 | 1.0 | 604 | 1.3789 | 25.5146 | 11.2077 | 20.8249 | 23.9199 | 19.0 |
| 1.1951 | 2.0 | 1208 | 1.3779 | 25.3347 | 11.2485 | 20.6781 | 23.7106 | 19.0 |
| 1.1081 | 3.0 | 1812 | 1.3903 | 26.1109 | 11.8345 | 21.2205 | 24.551 | 18.994 |
| 1.0272 | 4.0 | 2416 | 1.4042 | 26.027 | 11.5618 | 21.1159 | 24.3576 | 18.992 |
| 0.919 | 5.0 | 3020 | 1.4225 | 25.8294 | 11.5972 | 21.0053 | 24.3003 | 18.992 |
| 0.8643 | 6.0 | 3624 | 1.4410 | 25.9719 | 11.6151 | 21.0454 | 24.4411 | 18.99 |
| 0.8215 | 7.0 | 4228 | 1.4599 | 25.68 | 11.2692 | 20.9075 | 24.2681 | 19.0 |
| 0.7931 | 8.0 | 4832 | 1.4926 | 25.0808 | 10.9178 | 20.4053 | 23.6258 | 19.0 |
| 0.7664 | 9.0 | 5436 | 1.5090 | 25.458 | 10.9978 | 20.6381 | 23.9113 | 19.0 |
| 0.7053 | 10.0 | 6040 | 1.5259 | 25.4787 | 10.8938 | 20.5842 | 23.9459 | 18.998 |
| 0.6725 | 11.0 | 6644 | 1.5481 | 25.2993 | 10.7172 | 20.5288 | 23.8319 | 19.0 |
| 0.6462 | 12.0 | 7248 | 1.5710 | 25.6251 | 11.0816 | 20.7758 | 24.082 | 19.0 |
| 0.6275 | 13.0 | 7852 | 1.5884 | 25.8573 | 11.0737 | 20.988 | 24.294 | 19.0 |
| 0.5838 | 14.0 | 8456 | 1.6131 | 26.1096 | 11.3973 | 21.3659 | 24.6114 | 19.0 |
| 0.5682 | 15.0 | 9060 | 1.6259 | 25.7213 | 11.1484 | 20.8604 | 24.1114 | 19.0 |
| 0.5629 | 16.0 | 9664 | 1.6473 | 25.6197 | 11.2045 | 20.8956 | 24.1237 | 19.0 |
| 0.5446 | 17.0 | 10268 | 1.6645 | 25.4284 | 10.7362 | 20.4946 | 23.9147 | 19.0 |
| 0.5108 | 18.0 | 10872 | 1.6716 | 25.6986 | 11.2317 | 20.8851 | 24.272 | 19.0 |
| 0.5358 | 19.0 | 11476 | 1.6882 | 25.8002 | 11.2396 | 21.0001 | 24.2643 | 19.0 |
| 0.4959 | 20.0 | 12080 | 1.7027 | 25.636 | 11.2417 | 20.8785 | 24.1355 | 18.992 |
| 0.4942 | 21.0 | 12684 | 1.7131 | 25.6154 | 11.1795 | 20.7925 | 24.1343 | 19.0 |
| 0.4833 | 22.0 | 13288 | 1.7178 | 25.7708 | 11.2434 | 20.9096 | 24.1974 | 19.0 |
| 0.4702 | 23.0 | 13892 | 1.7227 | 25.6977 | 11.2352 | 20.9147 | 24.1121 | 19.0 |
| 0.4747 | 24.0 | 14496 | 1.7241 | 25.6248 | 11.2042 | 20.8192 | 24.1186 | 19.0 |
| 0.4691 | 25.0 | 15100 | 1.7255 | 25.5531 | 11.1657 | 20.7513 | 24.054 | 19.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
euneeei/Finetune_flan_t5_large_bnb_peft.ipynb
|
euneeei
| 2023-10-08T23:51:46Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-10-08T23:26:11Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
hongyin/self-management-1.5b
|
hongyin
| 2023-10-08T23:47:15Z | 153 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"zh",
"arxiv:2302.13173",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-01T23:54:06Z |
---
language:
- en
- zh
pipeline_tag: text-generation
---
## hongyin/self-management-1.5b
Warning: There are some problems with the tokenizer of this model, which will be corrected in the next version of the model (informer-1b).
I am pleased to introduce to you an English-Chinese bilingual autoregressive language model. This model is trained from scratch and has a unique vocabulary and 150 million parameters based on the LLAMA2 model structure. Our goal is to provide a solution that is computationally cheap and easy to inference. It's important to note that this is a base model, not intended to be used as a chatbot, but rather for alchemy. We look forward to providing you with a practical model product.
```python
```
## Bibtex entry and citation info
Please cite if you find it helpful.
```
@article{zhu2023metaaid,
title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
author={Zhu, Hongyin},
journal={arXiv preprint arXiv:2302.13173},
year={2023}
}
```
---
license: other
---
|
hongyin/chat-self-management-1.5b
|
hongyin
| 2023-10-08T23:46:44Z | 153 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"zh",
"arxiv:2302.13173",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-08T09:25:05Z |
---
language:
- en
- zh
pipeline_tag: text-generation
---
## hongyin/chat-self-management-1.5b
Warning: There are some problems with the tokenizer of this model, which will be corrected in the next version of the model (chat-informer-1b).
We are honored to introduce a lightweight Chinese-English conversation assistant designed to reduce the cost of inference. It is trained from scratch, based on the LLAMA2 architecture, with 150 million parameters and a completely new vocabulary. The training process consists of two parts: (1) NTP task. (2) Instruction tuning. The model improves data quality for pre-training and instruction tuning.
```python
Human: Paraphrasing the sentence: I love you.
Assistant: Sure, I love you.
```
## Bibtex entry and citation info
Please cite if you find it helpful.
```
@article{zhu2023metaaid,
title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
author={Zhu, Hongyin},
journal={arXiv preprint arXiv:2302.13173},
year={2023}
}
```
---
license: other
---
|
pinot/wav2vec2-xls-r-300m-ja-phoneme_cv_14_3
|
pinot
| 2023-10-08T23:45:35Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-08T22:53:06Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-ja-phoneme_cv_14_3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train[:50%]
args: default
metrics:
- name: Wer
type: wer
value: 0.1460970338882424
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-ja-phoneme_cv_14_3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7558
- Wer: 0.1461
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2909 | 0.44 | 400 | 2.8888 | 1.0 |
| 1.7369 | 0.88 | 800 | 0.7558 | 0.1461 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
|
taufeeque/TokFSM_k1_codebook_model
|
taufeeque
| 2023-10-08T23:43:01Z | 50 | 0 |
transformers
|
[
"transformers",
"pytorch",
"codebook",
"generated_from_trainer",
"dataset:toy_graph",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2023-10-02T17:31:55Z |
---
tags:
- generated_from_trainer
datasets:
- toy_graph
metrics:
- accuracy
model-index:
- name: output_toy
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: toy_graph
type: toy_graph
metrics:
- name: Accuracy
type: accuracy
value: 0.4525254617525837
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output_toy
This model is a fine-tuned version of [toy/model](https://huggingface.co/toy/model) on the toy_graph dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2691
- Accuracy: 0.4525
- Transition Accuracy: 0.5634
- First Transition Accuracy: 0.88
- Multicode K: 1
- Dead Code Fraction/layer0: 0.9969
- Mse/layer0: 220380.4595
- Input Norm/layer0: 333.7717
- Output Norm/layer0: 12.9360
- Dead Code Fraction/layer1: 0.9535
- Mse/layer1: 132.7843
- Input Norm/layer1: 6.5450
- Output Norm/layer1: 13.1449
- Dead Code Fraction/layer2: 0.9349
- Mse/layer2: 365.9396
- Input Norm/layer2: 6.1370
- Output Norm/layer2: 18.3248
- Dead Code Fraction/layer3: 0.9819
- Mse/layer3: 415.9804
- Input Norm/layer3: 7.4097
- Output Norm/layer3: 18.4665
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 1024
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Transition Accuracy | First Transition Accuracy | Multicode K | Dead Code Fraction/layer0 | Mse/layer0 | Input Norm/layer0 | Output Norm/layer0 | Dead Code Fraction/layer1 | Mse/layer1 | Input Norm/layer1 | Output Norm/layer1 | Dead Code Fraction/layer2 | Mse/layer2 | Input Norm/layer2 | Output Norm/layer2 | Dead Code Fraction/layer3 | Mse/layer3 | Input Norm/layer3 | Output Norm/layer3 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------------:|:-------------------------:|:-----------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:|
| 2.2465 | 0.03 | 500 | 1.8386 | 0.3565 | 0.3555 | 0.31 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.5981 | 0.05 | 1000 | 1.4652 | 0.4204 | 0.5015 | 0.58 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.3928 | 0.07 | 1500 | 1.3541 | 0.4378 | 0.555 | 0.79 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.3405 | 0.1 | 2000 | 1.3264 | 0.4427 | 0.5756 | 0.82 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.3189 | 0.12 | 2500 | 1.3187 | 0.4446 | 0.5576 | 0.86 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.308 | 0.15 | 3000 | 1.3064 | 0.4468 | 0.5573 | 0.82 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.3009 | 0.17 | 3500 | 1.2963 | 0.4493 | 0.5763 | 0.87 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2965 | 0.2 | 4000 | 1.2922 | 0.4494 | 0.5677 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2919 | 0.23 | 4500 | 1.2880 | 0.4499 | 0.5821 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2889 | 0.25 | 5000 | 1.2856 | 0.4501 | 0.56 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2855 | 0.28 | 5500 | 1.2816 | 0.4503 | 0.6016 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2828 | 0.3 | 6000 | 1.2844 | 0.4502 | 0.5734 | 0.87 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2805 | 0.33 | 6500 | 1.2777 | 0.4516 | 0.6084 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2793 | 0.35 | 7000 | 1.2796 | 0.4511 | 0.5681 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2785 | 0.38 | 7500 | 1.2748 | 0.4519 | 0.5919 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2764 | 0.4 | 8000 | 1.2767 | 0.4518 | 0.5760 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2763 | 0.42 | 8500 | 1.2801 | 0.4507 | 0.5827 | 0.94 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2755 | 0.45 | 9000 | 1.2755 | 0.4516 | 0.5765 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2746 | 0.47 | 9500 | 1.2736 | 0.4523 | 0.5865 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2734 | 0.5 | 10000 | 1.2740 | 0.4519 | 0.5779 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2732 | 0.53 | 10500 | 1.2744 | 0.4516 | 0.5879 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2723 | 0.55 | 11000 | 1.2690 | 0.4525 | 0.5811 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2712 | 0.57 | 11500 | 1.2705 | 0.4526 | 0.5779 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2716 | 0.6 | 12000 | 1.2701 | 0.4527 | 0.5760 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2708 | 0.62 | 12500 | 1.2716 | 0.4522 | 0.5485 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2705 | 0.65 | 13000 | 1.2676 | 0.4529 | 0.5734 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2696 | 0.68 | 13500 | 1.2717 | 0.4519 | 0.5994 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2687 | 0.7 | 14000 | 1.2687 | 0.4524 | 0.5756 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2685 | 0.72 | 14500 | 1.2709 | 0.4521 | 0.6127 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2685 | 0.75 | 15000 | 1.2706 | 0.4519 | 0.5873 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2675 | 0.78 | 15500 | 1.2691 | 0.4527 | 0.6365 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2677 | 0.8 | 16000 | 1.2686 | 0.4526 | 0.5589 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2676 | 0.82 | 16500 | 1.2639 | 0.4529 | 0.5940 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2662 | 0.85 | 17000 | 1.2655 | 0.4530 | 0.5955 | 0.94 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2666 | 0.88 | 17500 | 1.2636 | 0.4526 | 0.6013 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2664 | 0.9 | 18000 | 1.2681 | 0.4526 | 0.6034 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.266 | 0.93 | 18500 | 1.2624 | 0.4527 | 0.5839 | 0.88 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2653 | 0.95 | 19000 | 1.2688 | 0.4519 | 0.5837 | 0.92 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2654 | 0.97 | 19500 | 1.2619 | 0.4534 | 0.5973 | 0.92 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2649 | 1.0 | 20000 | 1.2647 | 0.4525 | 0.59 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
MoeenTB/Reinforce-PixelCopter
|
MoeenTB
| 2023-10-08T23:39:25Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-07T00:26:40Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 40.10 +/- 24.66
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Hishamds/Test
|
Hishamds
| 2023-10-08T23:07:45Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-10-08T23:07:45Z |
---
license: bigscience-openrail-m
---
|
Brecon/training_bert_model
|
Brecon
| 2023-10-08T22:58:50Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-04T20:24:17Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: training_bert_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# training_bert_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [fact verification](https://huggingface.co/datasets/Brecon/Train_Test) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0866
- Accuracy: 0.4318
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 11 | 1.1001 | 0.3182 |
| No log | 2.0 | 22 | 1.0924 | 0.3864 |
| No log | 3.0 | 33 | 1.0881 | 0.4091 |
| No log | 4.0 | 44 | 1.0866 | 0.4318 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.11.0
|
TheBloke/Athena-v4-GGUF
|
TheBloke
| 2023-10-08T22:25:56Z | 289 | 10 |
transformers
|
[
"transformers",
"gguf",
"llama",
"base_model:IkariDev/Athena-v4",
"base_model:quantized:IkariDev/Athena-v4",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-10-08T22:10:06Z |
---
base_model: IkariDev/Athena-v4
inference: false
license: cc-by-nc-4.0
model_creator: IkariDev + Undi95
model_name: Athena v4
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- header start -->
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</div>
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Athena v4 - GGUF
- Model creator: [IkariDev + Undi95](https://huggingface.co/IkariDev)
- Original model: [Athena v4](https://huggingface.co/IkariDev/Athena-v4)
<!-- description start -->
## Description
This repo contains GGUF format model files for [IkariDev + Undi95's Athena v4](https://huggingface.co/IkariDev/Athena-v4).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Athena-v4-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Athena-v4-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Athena-v4-GGUF)
* [IkariDev + Undi95's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/IkariDev/Athena-v4)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [IkariDev + Undi95's Athena v4](https://huggingface.co/IkariDev/Athena-v4).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [athena-v4.Q2_K.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [athena-v4.Q3_K_S.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [athena-v4.Q3_K_M.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [athena-v4.Q3_K_L.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [athena-v4.Q4_0.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [athena-v4.Q4_K_S.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [athena-v4.Q4_K_M.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [athena-v4.Q5_0.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [athena-v4.Q5_K_S.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [athena-v4.Q5_K_M.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [athena-v4.Q6_K.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [athena-v4.Q8_0.gguf](https://huggingface.co/TheBloke/Athena-v4-GGUF/blob/main/athena-v4.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Athena-v4-GGUF and below it, a specific filename to download, such as: athena-v4.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Athena-v4-GGUF athena-v4.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Athena-v4-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Athena-v4-GGUF athena-v4.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m athena-v4.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Athena-v4-GGUF", model_file="athena-v4.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: IkariDev + Undi95's Athena v4
Experimental Athena v4 model. Use Alpaca format. Suitable for RP, ERP and general stuff.
I should state here that this is a HIGHLY experimental model!
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains fp16 files of Athena-V4.
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v3-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v3-GPTQ)-->
<!-- [exl2 - by AzureBlack](https://huggingface.co/AzureBlack/Athena-v2-6.0bit-exl2) -->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v3-AWQ)-->
[fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)
[GGUF - by IkariDev](https://huggingface.co/IkariDev/Athena-v4-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v3-GGUF)-->
## Ratings:
Note: I have permission of all users to upload their ratings, i DONT screenshot random reviews without asking if i can put them here!
If you want your rating to be here, send me a message over on DC and ill put up a screenshot of it here. DC name is "ikaridev".
<!-- description end -->
<!-- description start -->
## Models+loras used and recipe
- Athena-v3
- Xwin-LM/Xwin-LM-13B-V0.1
- Undi95/PsyMedRP-v1-13B
- cgato/Thespis-13b-v0.2
- jondurbin/airoboros-l2-13b-3.0
```
Athena-v4-tmp1 = [ Athena-v3(0.85)+Xwin-LM/Xwin-LM-13B-V0.1(0.15) ]
Athena-v4-tmp2 = [ Undi95/PsyMedRP-v1-13B(0.55)+cgato/Thespis-13b-v0.2(0.45) ]
Athena-v4-tmp3 = Athena-v4-tmp1(0.55) + Athena-v4-tmp2(0.35)
Athena-v4 = Athena-v4-tmp3 + jondurbin/airoboros-l2-13b-3.0(0.1)
```
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
Thanks to [Undi95](https://huggingface.co/Undi95) for providing the machine for Athena v2 and Athena v3, and giving me infos about how things work. Going forward i will use a merging server provided by a friend.
<!-- original-model-card end -->
|
paisanx/ppo-LunarLander-v2-linc2
|
paisanx
| 2023-10-08T22:22:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-08T22:22:42Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 282.52 +/- 23.57
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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