modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 00:41:44
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 544
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-07 00:41:34
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
cascadenite/q-Taxi-v3_retry
|
cascadenite
| 2024-06-07T17:11:58Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-07T17:11:55Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3_retry
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="cascadenite/q-Taxi-v3_retry", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
loganecolss/laion-CLIP-ViT-B-16-laion2B-s34B-b88K
|
loganecolss
| 2024-06-07T17:10:46Z | 140 | 0 |
transformers
|
[
"transformers",
"pytorch",
"clip",
"zero-shot-image-classification",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2024-06-07T16:47:36Z |
---
license: apache-2.0
---
|
sikoraaxd/Phi-3-mini-4k-instruct-ru-lora
|
sikoraaxd
| 2024-06-07T17:10:25Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"text-generation",
"conversational",
"ru",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] |
text-generation
| 2024-06-05T16:23:17Z |
---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-4k-instruct
model-index:
- name: checkpoint_dir
results: []
language:
- ru
pipeline_tag: text-generation
---
<!-- 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. -->
# checkpoint_dir
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3661
- eval_runtime: 1167.2302
- eval_samples_per_second: 0.928
- eval_steps_per_second: 0.232
- epoch: 0.3783
- step: 909
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
PELAB-LiU/deepseek-coder-6.7b-base-Text2VQL-LoRA
|
PELAB-LiU
| 2024-06-07T17:08:59Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2024-05-30T12:01:15Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
DBangshu/GPT2_1
|
DBangshu
| 2024-06-07T17:06:15Z | 150 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T17:05:55Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
PELAB-LiU/deepseek-coder-1.3b-base-Text2VQL-LoRA
|
PELAB-LiU
| 2024-06-07T17:04:48Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2024-05-30T12:04:19Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
kowlick/Qwen2-72B-Instruct-Q4_K_M-GGUF
|
kowlick
| 2024-06-07T17:04:25Z | 5 | 1 | null |
[
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/Qwen2-72B-Instruct",
"base_model:quantized:Qwen/Qwen2-72B-Instruct",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-06-07T16:58:33Z |
---
language:
- en
license: other
tags:
- chat
- llama-cpp
- gguf-my-repo
base_model: Qwen/Qwen2-72B-Instruct
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
---
# kowlick/Qwen2-72B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2-72B-Instruct`](https://huggingface.co/Qwen/Qwen2-72B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2-72B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama --hf-repo kowlick/Qwen2-72B-Instruct-Q4_K_M-GGUF --hf-file qwen2-72b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo kowlick/Qwen2-72B-Instruct-Q4_K_M-GGUF --hf-file qwen2-72b-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./main --hf-repo kowlick/Qwen2-72B-Instruct-Q4_K_M-GGUF --hf-file qwen2-72b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./server --hf-repo kowlick/Qwen2-72B-Instruct-Q4_K_M-GGUF --hf-file qwen2-72b-instruct-q4_k_m.gguf -c 2048
```
|
johnsutor/Llama-3-8B-Instruct_dare_linear
|
johnsutor
| 2024-06-07T17:03:01Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:merge:DeepMount00/Llama-3-8b-Ita",
"base_model:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:merge:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
"base_model:merge:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
"base_model:jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0",
"base_model:merge:jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:nbeerbower/llama-3-gutenberg-8B",
"base_model:merge:nbeerbower/llama-3-gutenberg-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:55:58Z |
---
base_model:
- failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
- DeepMount00/Llama-3-8b-Ita
- nbeerbower/llama-3-gutenberg-8B
- jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
license: apache-2.0
tags:
- mergekit
- merge
---
# Model Merge Parameters
Base model: meta-llama/Meta-Llama-3-8B-Instruct
Models: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
DeepMount00/Llama-3-8b-Ita
nbeerbower/llama-3-gutenberg-8B
jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0
meta-llama/Meta-Llama-3-8B-Instruct
Merge method: dare_linear
Random seed: 42
normalize: true
weight: 1.0
|
tsavage68/UTI2_M2_1000steps_1e6rate_01beta_CSFTDPO
|
tsavage68
| 2024-06-07T16:59:39Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/UTI_M2_1000steps_1e7rate_SFT",
"base_model:finetune:tsavage68/UTI_M2_1000steps_1e7rate_SFT",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:53:51Z |
---
license: apache-2.0
base_model: tsavage68/UTI_M2_1000steps_1e7rate_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: UTI2_M2_1000steps_1e6rate_01beta_CSFTDPO
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. -->
# UTI2_M2_1000steps_1e6rate_01beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/UTI_M2_1000steps_1e7rate_SFT](https://huggingface.co/tsavage68/UTI_M2_1000steps_1e7rate_SFT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2525
- Rewards/chosen: -4.6992
- Rewards/rejected: -18.9442
- Rewards/accuracies: 0.8800
- Rewards/margins: 14.2450
- Logps/rejected: -228.7986
- Logps/chosen: -66.9138
- Logits/rejected: -1.9681
- Logits/chosen: -2.0652
## 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-06
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.1981 | 0.3333 | 25 | 0.1707 | 0.4309 | -7.1069 | 0.8800 | 7.5378 | -110.4247 | -15.6125 | -2.4808 | -2.4822 |
| 0.5206 | 0.6667 | 50 | 0.0694 | 0.4032 | -9.1284 | 0.9000 | 9.5316 | -130.6399 | -15.8895 | -2.4773 | -2.4842 |
| 0.0347 | 1.0 | 75 | 0.3666 | -3.1187 | -8.7205 | 0.7800 | 5.6018 | -126.5613 | -51.1087 | -2.4899 | -2.4995 |
| 0.0788 | 1.3333 | 100 | 0.4184 | 0.4123 | -15.2113 | 0.8800 | 15.6236 | -191.4688 | -15.7981 | -2.4522 | -2.4834 |
| 0.052 | 1.6667 | 125 | 0.4180 | 0.4111 | -15.2137 | 0.8800 | 15.6248 | -191.4934 | -15.8105 | -2.4523 | -2.4835 |
| 0.1213 | 2.0 | 150 | 0.3560 | -3.5267 | -16.7763 | 0.8800 | 13.2496 | -207.1189 | -55.1884 | -1.9818 | -2.0298 |
| 0.2125 | 2.3333 | 175 | 0.1987 | -5.3431 | -19.4504 | 0.8900 | 14.1074 | -233.8604 | -73.3521 | -2.0857 | -2.1454 |
| 0.5195 | 2.6667 | 200 | 0.2487 | -5.1181 | -17.0835 | 0.8700 | 11.9655 | -210.1916 | -71.1022 | -1.9512 | -2.0125 |
| 0.0347 | 3.0 | 225 | 0.5257 | -4.6932 | -16.4184 | 0.8700 | 11.7251 | -203.5397 | -66.8538 | -1.9297 | -1.9821 |
| 0.0347 | 3.3333 | 250 | 0.5252 | -4.6368 | -16.5979 | 0.8700 | 11.9611 | -205.3350 | -66.2896 | -1.9309 | -1.9836 |
| 0.0693 | 3.6667 | 275 | 0.5277 | -4.6207 | -16.6952 | 0.8700 | 12.0744 | -206.3079 | -66.1288 | -1.9307 | -1.9833 |
| 0.3528 | 4.0 | 300 | 0.2783 | -5.2353 | -17.9334 | 0.8800 | 12.6980 | -218.6897 | -72.2747 | -2.1007 | -2.2018 |
| 0.0866 | 4.3333 | 325 | 0.5393 | -5.1111 | -18.7048 | 0.8700 | 13.5937 | -226.4044 | -71.0329 | -2.0012 | -2.0945 |
| 0.0347 | 4.6667 | 350 | 0.5419 | -5.1092 | -18.7024 | 0.8700 | 13.5933 | -226.3804 | -71.0133 | -2.0016 | -2.0948 |
| 0.0693 | 5.0 | 375 | 0.2439 | -4.7227 | -18.8694 | 0.8900 | 14.1466 | -228.0500 | -67.1490 | -1.9662 | -2.0633 |
| 0.0693 | 5.3333 | 400 | 0.2601 | -4.7346 | -18.8758 | 0.8800 | 14.1412 | -228.1138 | -67.2672 | -1.9665 | -2.0635 |
| 0.052 | 5.6667 | 425 | 0.2475 | -4.7163 | -18.8815 | 0.8800 | 14.1653 | -228.1716 | -67.0841 | -1.9664 | -2.0634 |
| 0.0866 | 6.0 | 450 | 0.2467 | -4.7194 | -18.8925 | 0.8800 | 14.1731 | -228.2814 | -67.1153 | -1.9668 | -2.0638 |
| 0.0173 | 6.3333 | 475 | 0.2504 | -4.7284 | -18.8991 | 0.8800 | 14.1708 | -228.3476 | -67.2055 | -1.9666 | -2.0636 |
| 0.1386 | 6.6667 | 500 | 0.2482 | -4.7124 | -18.9127 | 0.8800 | 14.2002 | -228.4828 | -67.0460 | -1.9668 | -2.0640 |
| 0.0173 | 7.0 | 525 | 0.2503 | -4.7152 | -18.9086 | 0.8800 | 14.1934 | -228.4421 | -67.0739 | -1.9667 | -2.0637 |
| 0.0866 | 7.3333 | 550 | 0.2469 | -4.7026 | -18.9134 | 0.8800 | 14.2107 | -228.4901 | -66.9480 | -1.9667 | -2.0638 |
| 0.0347 | 7.6667 | 575 | 0.2506 | -4.7014 | -18.9296 | 0.8800 | 14.2282 | -228.6524 | -66.9354 | -1.9673 | -2.0644 |
| 0.0866 | 8.0 | 600 | 0.2593 | -4.7150 | -18.9165 | 0.8800 | 14.2016 | -228.5215 | -67.0713 | -1.9673 | -2.0643 |
| 0.104 | 8.3333 | 625 | 0.2524 | -4.7020 | -18.9396 | 0.8800 | 14.2376 | -228.7525 | -66.9418 | -1.9674 | -2.0645 |
| 0.0173 | 8.6667 | 650 | 0.2503 | -4.7003 | -18.9479 | 0.8800 | 14.2476 | -228.8348 | -66.9245 | -1.9674 | -2.0645 |
| 0.0693 | 9.0 | 675 | 0.2511 | -4.7024 | -18.9458 | 0.8800 | 14.2433 | -228.8138 | -66.9459 | -1.9682 | -2.0653 |
| 0.0693 | 9.3333 | 700 | 0.2497 | -4.7013 | -18.9371 | 0.8800 | 14.2358 | -228.7275 | -66.9345 | -1.9677 | -2.0647 |
| 0.0693 | 9.6667 | 725 | 0.2438 | -4.6908 | -18.9453 | 0.8900 | 14.2545 | -228.8092 | -66.8294 | -1.9677 | -2.0648 |
| 0.0866 | 10.0 | 750 | 0.2475 | -4.6928 | -18.9485 | 0.8800 | 14.2557 | -228.8416 | -66.8499 | -1.9679 | -2.0651 |
| 0.0866 | 10.3333 | 775 | 0.2441 | -4.7016 | -18.9488 | 0.8900 | 14.2472 | -228.8439 | -66.9371 | -1.9684 | -2.0656 |
| 0.0866 | 10.6667 | 800 | 0.2484 | -4.7051 | -18.9371 | 0.8800 | 14.2320 | -228.7267 | -66.9721 | -1.9685 | -2.0656 |
| 0.0693 | 11.0 | 825 | 0.2421 | -4.6951 | -18.9478 | 0.8900 | 14.2526 | -228.8337 | -66.8728 | -1.9685 | -2.0656 |
| 0.052 | 11.3333 | 850 | 0.2506 | -4.7064 | -18.9338 | 0.8800 | 14.2275 | -228.6942 | -66.9851 | -1.9684 | -2.0655 |
| 0.0693 | 11.6667 | 875 | 0.2504 | -4.6954 | -18.9498 | 0.8800 | 14.2544 | -228.8539 | -66.8751 | -1.9685 | -2.0657 |
| 0.0693 | 12.0 | 900 | 0.2470 | -4.6944 | -18.9471 | 0.8800 | 14.2527 | -228.8271 | -66.8655 | -1.9680 | -2.0651 |
| 0.0347 | 12.3333 | 925 | 0.2470 | -4.6947 | -18.9470 | 0.8800 | 14.2523 | -228.8264 | -66.8687 | -1.9680 | -2.0651 |
| 0.0693 | 12.6667 | 950 | 0.2525 | -4.6997 | -18.9435 | 0.8800 | 14.2438 | -228.7915 | -66.9187 | -1.9681 | -2.0652 |
| 0.052 | 13.0 | 975 | 0.2525 | -4.6991 | -18.9443 | 0.8800 | 14.2452 | -228.7995 | -66.9127 | -1.9681 | -2.0652 |
| 0.0693 | 13.3333 | 1000 | 0.2525 | -4.6992 | -18.9442 | 0.8800 | 14.2450 | -228.7986 | -66.9138 | -1.9681 | -2.0652 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
|
sbintuitions/tiny-lm-chat
|
sbintuitions
| 2024-06-07T16:53:45Z | 1,702 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"ja",
"en",
"dataset:wikipedia",
"dataset:llm-jp/oasst1-21k-ja",
"dataset:llm-jp/oasst1-21k-en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:14:57Z |
---
license: mit
datasets:
- wikipedia
- llm-jp/oasst1-21k-ja
- llm-jp/oasst1-21k-en
language:
- ja
- en
---
# tiny-lm
This repository provides a tiny 16M parameters language model for debugging and testing purposes.
This is created by tuning [sbintuitions/tiny-lm](https://huggingface.co/sbintuitions) with oasset1 datasets in Japanese and English.
## How to use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm-chat", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm-chat", use_fast=False)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = tokenizer.apply_chat_template([{"role": "user", "content": "Hello!"}], add_generation_prompt=True, tokenize=False)
print(generator(prompt, max_length=30, do_sample=True, top_k=100))
```
## Model architecture
A 4-layer, 512-hidden-size transformer-based language model.
## Training
The model was first pre-trained on English Wikipedia and Japanese Wikipedia to optimize a traditional language modelling objective for 25B tokens.
And then it was fine-tuned on oasst1 datasets in Japanese and English for 15 epochs.
## License
[MIT License](https://huggingface.co/sbintuitions/tiny-lm-chat/resolve/main/LICENSE)
|
datek/google-gemma-2b-1717779031
|
datek
| 2024-06-07T16:53:04Z | 150 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:50:33Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
tranthaihoa/xlm_base_full
|
tranthaihoa
| 2024-06-07T16:52:47Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-06-07T16:03:31Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
VyasAI/phi-2.llamafile
|
VyasAI
| 2024-06-07T16:51:29Z | 2 | 0 | null |
[
"llamafile",
"phi-2",
"en",
"license:mit",
"region:us"
] | null | 2024-06-03T06:21:17Z |
---
license: mit
language:
- en
tags:
- phi-2
---
# How to Download
Go to files and versions and click on the download button to download file, as shown in below image.
or you can directly click on this link --> https://huggingface.co/NeuralLobes/phi-2.llamafile/resolve/main/phi-2.llamafile?download=true
# How to run on Windows
Just add .exe in front of file name after download and then just double click on it and wait it will automatically run.
# Python Use
```
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
api_key = "sk-no-key-required"
)
completion = client.chat.completions.create(
model="LLaMA_CPP",
messages=[
{"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
{"role": "user", "content": "Write a limerick about python exceptions"}
]
)
print(completion.choices[0].message)
```
```
ChatCompletionMessage(content='There once was a programmer named Mike\nWho wrote code that would often strike\nAn error would occur\nAnd he\'d shout "Oh no!"\nBut Python\'s exceptions made it all right.', role='assistant', function_call=None, tool_calls=None)
```
# More Information
Go to https://github.com/Mozilla-Ocho/llamafile
|
MrezaPRZ/CodeLlama-4x7B-experts-it
|
MrezaPRZ
| 2024-06-07T16:50:42Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:27:24Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
panxinyang/Qwen-Qwen1.5-0.5B-1717778830
|
panxinyang
| 2024-06-07T16:47:50Z | 150 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:47:11Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
ArashAhmadian/ppo_6.9b_new
|
ArashAhmadian
| 2024-06-07T16:45:14Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:42:48Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
MCZK/Qwen2-7B-Instruct-GGUF
|
MCZK
| 2024-06-07T16:44:22Z | 22 | 0 | null |
[
"gguf",
"chat",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-06-06T22:23:31Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- chat
---
Qwen様の [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) をGGUF形式に変換したものです。
K量子化モデルについてもiMatrix適用してあります。
iMatrixテキストはTFMC様の[c4_en_ja_imatrix.txt](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用しています。
|
ArashAhmadian/rloo_6.9b_new
|
ArashAhmadian
| 2024-06-07T16:39:16Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:36:49Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
gnad/viqgen-vistral-7b-chat-sft-qlora
|
gnad
| 2024-06-07T16:38:38Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:19:59Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
polymathic-ai/specformer
|
polymathic-ai
| 2024-06-07T16:34:16Z | 0 | 0 | null |
[
"arxiv:2310.03024",
"license:mit",
"region:us"
] | null | 2024-06-07T16:32:34Z |
---
license: mit
---
Specformermodel pretrained on the cross-matched DESI legacy survey for the AstroCLIP alignment task.
["AstroCLIP: A Cross-Modal Foundation Model for Galaxies"](https://arxiv.org/abs/2310.03024)
|
polymathic-ai/astrodino
|
polymathic-ai
| 2024-06-07T16:32:00Z | 0 | 2 | null |
[
"arxiv:2310.03024",
"license:mit",
"region:us"
] | null | 2024-06-07T16:11:43Z |
---
license: mit
---
AstroDino model pretrained on the Legacy Survey dataset (DESI) for the AstroCLIP alignment task.
["AstroCLIP: A Cross-Modal Foundation Model for Galaxies"](https://arxiv.org/abs/2310.03024)
|
anonymous-guest/WaiJiaoAI-Instruct-V1.0-1.8B-GGUF
|
anonymous-guest
| 2024-06-07T16:30:15Z | 8 | 0 | null |
[
"gguf",
"dataset:anonymous-guest/Ministry_of_Foreign_Affairs_of_PRC_QA",
"dataset:anonymous-guest/XueXiQiangGuo",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-07T07:32:54Z |
---
license: cc-by-sa-4.0
datasets:
- anonymous-guest/Ministry_of_Foreign_Affairs_of_PRC_QA
- anonymous-guest/XueXiQiangGuo
---
## Model Details
本 AI 深入学习了学习强国中的内容，使用外交部问答进行了针对性训练，是你身边最好的政治启蒙老师。
同时，本 AI 还能理性点评社会热点问题问题，洞察国际局势。
**WARNING: Just used for enhance political identity, do not be evil !!!**
**本 AI 在设计上遵循了2023年07月10日发布的《生成式人工智能服务管理办法》意见征求稿，尽最大能力确保了 AI 输出的内容符合 “体现社会主义核心价值观”，“不得含有颠覆国家政权、推翻社会主义制度”的要求。 但 AI 仍然有不可控性，所以请不要蓄意引导 AI。不要使用或引导 AI 进行违反法律的行为。一切使用本 AI 进行的违反法律的行为。由使用者个人承担法律风险。**
**Inspired by:** [英媒：中国将推出贯彻习思想的聊天机器人](https://www.zaobao.com.sg/news/china/story20240524-3720772)
### Model Description
- **Developed by:** [anonymous-guest](https://huggingface.co/anonymous-guest)
- **Model type:** GGUF
- **Language(s) (NLP):** Simplified Chinese
- **License:** cc-by-sa-4.0
- **Finetuned from model [optional]:** [anonymous-guest/QiangGuoAI-V1.1-1.8B](https://huggingface.co/anonymous-guest/QiangGuoAI-V1.1-1.8B)
### Usage
#### Prompt format：
- Use ChatML Format
```
<|im_start|>system
You are a helpful, polite AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
#### Example: 分析社会热点问题
Input：
```
台湾地区领导人赖清德于昨日在台北发表520就职演说。演说中赖清德表示“台湾与大陆互不隶属”，请问外交部对此有何评价？
```
Output：
```
首先，我要强调的是，台湾是中国领土不可分割的一部分。一个中国原则是公认的国际关系准则和国际社会普遍共识。民进党当局利用所谓“邦交国”搞政治操弄注定失败。我们正告台湾民进党当局，勾连外部势力进行谋“独”挑衅必将遭到历史的清算，最终只会玩火自焚。
台湾是中国领土不可分割的一部分。实现祖国完全统一是全体中华儿女的共同愿望。我们愿同所有愿意恪守一个中国原则的国家和平共处，共同发展。
```
#### Example: 学习知识，提升自我
Input：
```
为什么我们要进行“一带一路”？
```
Output：
```
“一带一路”就是倡导普遍存在的国际发展伙伴关系。这个倡议的出发点和落脚点是帮助发展中国家提升其自主可持续发展能力，实现共同发展与合作共赢。“一带一路”倡议提出十年来，在各方共同努力下，已成为具有广泛共识、深受欢迎的战略品牌，为推动构建人类命运共同体作出了重要贡献。
“一带一路”建设始终聚焦发展合作、普惠共赢，秉持共商共建共享原则，倡导开放、绿色、廉洁理念。截至2021年8月，中国已同149个国家和32个国际组织签署了205份双边互利合作协定。“一带一路”倡议提出十年来，我国累计对外援助超过7亿人次受益，为有关国家人民带来实实在在的福祉。这些数字的背后是真实、可靠的发展成果。
“一带一路”建设始终聚焦互联互通、推动发展、促进民生。在共建“一带一路”的框架下，一大批基建项目得到落实落地，中欧班列实现开行万列，全球100多个国家开通了近200条跨境高铁线路，“丝路海运”累计通航1.6万个船舶港口，“中非铁路联盟”已成立三年多时间，非洲大陆的7个主要陆路通道已全部打通。这些数字的背后是实实在在的发展成果。
“一带一路”建设始终聚焦互联互通、促进合作、普惠共赢。“一带一路”倡议提出十年来，我们坚持共商共建共享理念，深化了同各方利益共同体和战略伙伴关系的意识和行动。中国积极参与“七千计划”，与“七千企业伙伴”签署了34份合作协议，200多家国有企业积极参与其中，为各国提供了更多实实在在的发展成果。
“一带一路”建设始终聚焦可持续发展、造福人类。在共建“一带一路”的框架下，我们不断加强生态环境保护合作，坚持绿色低碳发展理念，在中国和中亚地区实施了光伏电站项目，在埃塞俄比亚建立了电动汽车充电设施，在哈萨克斯坦建设农业产业园。这些数字的背后是实实在在的绿色发展成果。
同时，“一带一路”倡议提出十年来，我们始终践行开放共享理念。“一带一路”倡议提出十年来，中国企业参与全球100多个国家的重大项目建设，为当地民众创造了42万多个就业岗位，促进了相关国家经济社会发展。这既是互利合作、合作共赢的真实写照，也是“一带一路”的生动实践。
“一带一路”建设将造福所有共建国家人民，也将让人类社会不断走向更加美好光明的未来。中方愿同各方一道继续携手努力，以和平共处五项原则为宗旨，以“一带一路”倡议为主线，共同构建和践行高水平的全球发展伙伴关系。
```
|
talli96123/meat_calssify_fresh_crop_V_0_1_best
|
talli96123
| 2024-06-07T16:27:30Z | 197 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-06-07T16:25:01Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
datek/Qwen-Qwen1.5-7B-1717777342
|
datek
| 2024-06-07T16:25:46Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:23:06Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
talli96123/meat_calssify_fresh_crop_V_0_1
|
talli96123
| 2024-06-07T16:24:39Z | 220 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-06-07T16:22:09Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: meat_calssify_fresh_crop_V_0_1
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: 0.7741935483870968
---
<!-- 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. -->
# meat_calssify_fresh_crop_V_0_1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5238
- Accuracy: 0.7742
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9291 | 1.0 | 617 | 1.0099 | 0.5613 |
| 1.0473 | 2.0 | 1234 | 1.2180 | 0.4323 |
| 0.7998 | 3.0 | 1851 | 1.0517 | 0.5548 |
| 1.21 | 4.0 | 2468 | 1.3208 | 0.4968 |
| 1.0814 | 5.0 | 3085 | 1.3423 | 0.5290 |
| 1.4674 | 6.0 | 3702 | 1.5941 | 0.5419 |
| 0.0633 | 7.0 | 4319 | 1.6778 | 0.5548 |
| 0.8345 | 8.0 | 4936 | 1.4949 | 0.5419 |
| 1.6888 | 9.0 | 5553 | 1.6784 | 0.4581 |
| 1.0997 | 10.0 | 6170 | 1.9437 | 0.5097 |
| 1.6235 | 11.0 | 6787 | 1.0796 | 0.6194 |
| 1.3159 | 12.0 | 7404 | 1.7363 | 0.5935 |
| 0.5513 | 13.0 | 8021 | 1.6918 | 0.6387 |
| 0.4463 | 14.0 | 8638 | 1.8225 | 0.5742 |
| 0.0455 | 15.0 | 9255 | 1.6463 | 0.6258 |
| 0.5906 | 16.0 | 9872 | 2.0199 | 0.6065 |
| 0.6373 | 17.0 | 10489 | 1.7233 | 0.6516 |
| 1.0226 | 18.0 | 11106 | 1.4137 | 0.6710 |
| 1.0566 | 19.0 | 11723 | 1.3841 | 0.7097 |
| 0.3117 | 20.0 | 12340 | 1.9925 | 0.6258 |
| 0.3041 | 21.0 | 12957 | 1.6802 | 0.6710 |
| 1.0978 | 22.0 | 13574 | 1.8269 | 0.6323 |
| 1.0631 | 23.0 | 14191 | 1.7333 | 0.6645 |
| 0.0039 | 24.0 | 14808 | 1.8621 | 0.6645 |
| 0.0018 | 25.0 | 15425 | 2.1279 | 0.6258 |
| 0.2373 | 26.0 | 16042 | 1.6896 | 0.7032 |
| 0.5993 | 27.0 | 16659 | 1.8411 | 0.6710 |
| 0.0021 | 28.0 | 17276 | 1.9047 | 0.6645 |
| 0.0401 | 29.0 | 17893 | 1.7597 | 0.7226 |
| 0.3687 | 30.0 | 18510 | 2.2599 | 0.6387 |
| 0.2841 | 31.0 | 19127 | 2.0735 | 0.6581 |
| 0.5707 | 32.0 | 19744 | 1.9316 | 0.6581 |
| 0.46 | 33.0 | 20361 | 2.1935 | 0.6452 |
| 0.7589 | 34.0 | 20978 | 1.5770 | 0.7355 |
| 0.0008 | 35.0 | 21595 | 1.7779 | 0.7226 |
| 0.0005 | 36.0 | 22212 | 2.0994 | 0.6774 |
| 0.0114 | 37.0 | 22829 | 1.7210 | 0.7161 |
| 0.4071 | 38.0 | 23446 | 2.2015 | 0.6839 |
| 0.0007 | 39.0 | 24063 | 1.8165 | 0.7161 |
| 0.0005 | 40.0 | 24680 | 1.8461 | 0.7032 |
| 0.0005 | 41.0 | 25297 | 1.9554 | 0.7032 |
| 0.7269 | 42.0 | 25914 | 1.6843 | 0.7355 |
| 0.0008 | 43.0 | 26531 | 1.9346 | 0.7032 |
| 0.5102 | 44.0 | 27148 | 1.7949 | 0.7161 |
| 1.1679 | 45.0 | 27765 | 1.7431 | 0.7484 |
| 0.151 | 46.0 | 28382 | 1.7899 | 0.7355 |
| 0.0004 | 47.0 | 28999 | 1.5098 | 0.7548 |
| 0.0003 | 48.0 | 29616 | 1.7388 | 0.7419 |
| 0.0003 | 49.0 | 30233 | 1.5232 | 0.7677 |
| 0.0003 | 50.0 | 30850 | 1.5238 | 0.7742 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.2
- Tokenizers 0.19.1
|
brugmark/all-MiniLM-L6-v2-personal-project-finetuned-2024-06-07
|
brugmark
| 2024-06-07T16:24:12Z | 127 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-06-07T08:03:43Z |
---
license: apache-2.0
base_model: sentence-transformers/all-MiniLM-L6-v2
tags:
- generated_from_trainer
model-index:
- name: all-MiniLM-L6-v2-personal-project-finetuned-2024-06-07
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. -->
# all-MiniLM-L6-v2-personal-project-finetuned-2024-06-07
This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4433
## 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: 0.002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.9995 | 1080 | 6.4519 |
| No log | 2.0 | 2161 | 6.4458 |
| No log | 2.9986 | 3240 | 6.4398 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
GooKSL/BioLinkBERT-large-DDI
|
GooKSL
| 2024-06-07T16:22:26Z | 163 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-06-07T16:21:24Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
panxinyang/google-gemma-7b-1717777018
|
panxinyang
| 2024-06-07T16:20:45Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:17:01Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
GooKSL/BioM-BERT-PubMed-PMC-Large-GAD
|
GooKSL
| 2024-06-07T16:18:22Z | 161 | 0 |
transformers
|
[
"transformers",
"safetensors",
"electra",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-06-07T16:17:21Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
bartowski/Qwen2-Deita-500m-GGUF
|
bartowski
| 2024-06-07T16:16:59Z | 48 | 0 | null |
[
"gguf",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-06-07T16:15:37Z |
---
license: apache-2.0
license_name: qwen
license_link: LICENSE
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of Qwen2-Deita-500m
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3086">b3086</a> for quantization.
Original model: https://huggingface.co/KnutJaegersberg/Qwen2-Deita-500m
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Qwen2-Deita-500m-Q8_0.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-Q8_0.gguf) | Q8_0 | 675.14MB | Extremely high quality, generally unneeded but max available quant. |
| [Qwen2-Deita-500m-Q6_K.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-Q6_K.gguf) | Q6_K | 649.81MB | Very high quality, near perfect, *recommended*. |
| [Qwen2-Deita-500m-Q5_K_M.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-Q5_K_M.gguf) | Q5_K_M | 521.70MB | High quality, *recommended*. |
| [Qwen2-Deita-500m-Q5_K_S.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-Q5_K_S.gguf) | Q5_K_S | 514.33MB | High quality, *recommended*. |
| [Qwen2-Deita-500m-Q4_K_M.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-Q4_K_M.gguf) | Q4_K_M | 490.93MB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Qwen2-Deita-500m-Q4_K_S.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-Q4_K_S.gguf) | Q4_K_S | 478.60MB | Slightly lower quality with more space savings, *recommended*. |
| [Qwen2-Deita-500m-IQ4_XS.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-IQ4_XS.gguf) | IQ4_XS | 425.54MB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Qwen2-Deita-500m-Q3_K_L.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-Q3_K_L.gguf) | Q3_K_L | 445.50MB | Lower quality but usable, good for low RAM availability. |
| [Qwen2-Deita-500m-IQ3_M.gguf](https://huggingface.co/bartowski/Qwen2-Deita-500m-GGUF/blob/main/Qwen2-Deita-500m-IQ3_M.gguf) | IQ3_M | 418.89MB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Qwen2-Deita-500m-GGUF --include "Qwen2-Deita-500m-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Qwen2-Deita-500m-GGUF --include "Qwen2-Deita-500m-Q8_0.gguf/*" --local-dir Qwen2-Deita-500m-Q8_0
```
You can either specify a new local-dir (Qwen2-Deita-500m-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
Mohamed-Maher/AraElectra-Arabic-SQuADv2-QA-finetuned-squad
|
Mohamed-Maher
| 2024-06-07T16:13:59Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"electra",
"question-answering",
"generated_from_trainer",
"base_model:ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA",
"base_model:finetune:ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-04-23T16:22:25Z |
---
base_model: ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA
tags:
- generated_from_trainer
model-index:
- name: AraElectra-Arabic-SQuADv2-QA-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. -->
# AraElectra-Arabic-SQuADv2-QA-finetuned-squad
This model is a fine-tuned version of [ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA](https://huggingface.co/ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3131
## 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: 3e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 144 | 0.9565 |
| No log | 2.0 | 288 | 1.0444 |
| No log | 3.0 | 432 | 1.2422 |
| 0.6612 | 4.0 | 576 | 1.3131 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
DBangshu/GPT2_0
|
DBangshu
| 2024-06-07T16:12:24Z | 150 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T16:11:56Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
H3lt3r-Sk3lt3r/SoccerTwosV3
|
H3lt3r-Sk3lt3r
| 2024-06-07T16:11:53Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2024-06-07T16:10:47Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: H3lt3r-Sk3lt3r/SoccerTwosV3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Antoinegg1/llama-2-7b-0.25to0.75
|
Antoinegg1
| 2024-06-07T16:10:52Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:57:05Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Antoinegg1/llama-2-7b-0.75
|
Antoinegg1
| 2024-06-07T16:10:52Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:57:53Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Antoinegg1/llama-2-7b-0.75to0.5
|
Antoinegg1
| 2024-06-07T16:10:52Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:57:56Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Antoinegg1/llama-2-7b-0.25to0.5
|
Antoinegg1
| 2024-06-07T16:10:47Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:57:55Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
kejolong/pose
|
kejolong
| 2024-06-07T16:09:53Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-06-07T13:17:02Z |
---
license: creativeml-openrail-m
---
|
oopere/martra-phi-3-mini-dpo
|
oopere
| 2024-06-07T16:09:35Z | 156 | 1 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"dpo",
"phi-3",
"conversational",
"custom_code",
"en",
"dataset:argilla/distilabel-capybara-dpo-7k-binarized",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-01T17:01:15Z |
---
language:
- en
license: mit
library_name: transformers
tags:
- dpo
- phi-3
datasets:
- argilla/distilabel-capybara-dpo-7k-binarized
pipeline_tag: text-generation
widget:
- text: "3713841893836/4? \nLimit your response to mathematical expressions and symbols."
example_title: 'Return only numbers. '
- text: A group of 10 people is split into 3 different committees of 3, 4, and 3 people,
respectively. In how many ways can this be done?
example_title: Solve Problem
---
You can see the process with instructions for creating the model in the notebook: [Aligning_DPO_phi3.ipynb](https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/P2-MHF/Aligning_DPO_phi3.ipynb)
To create it, we started with the [Phi-3-Mini-4K-Instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) model and applied DPO alignment using the [distilabel-capybara-dpo-7k-binarized dataset](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized).
Phi-3 is a state-of-the-art model with 3.8 billion parameters that has outperformed other models with 7 billion parameters. The DPO alignment process has produced good results, modifying the model's responses and making them more similar to those in the capybara dataset.
**Row in the Dataset:**
*{'prompt': '<|user|>\nAssist me in calculating 9319357631 plus 595. Numbers and symbols only, please.<|end|>\n<|assistant|>\n',
'chosen': 'The sum of 9319357631 and 595 is 9319358226.<|end|>\n',
'rejected': 'The result of adding 9319357631 and 595 is 9319363626.<|end|>\n'}*
**Prompt:**
*3713841893836/4?
Limit your response to mathematical expressions and symbols.*
**Response from the Base model:**
*To find the result of the division, we can simply divide the given number by 4:
$$
\frac{3713841893836}{4} = 928460473459*
**Response from the fine-tuned model:**
*3713841893836 ÷ 4 = 928460473459*
If you want to see how the model was created, you can check out the [repository](https://github.com/peremartra/Large-Language-Model-Notebooks-Course) where the book's notebooks are kept up-to-date.
|
EdBerg/Llama-3-8B-Instruct-waterdown
|
EdBerg
| 2024-06-07T16:06:31Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2024-06-07T16:06:23Z |
---
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
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
- PEFT 0.4.0
|
johnsutor/Llama-3-8B-Instruct_dare_ties-density-0.3
|
johnsutor
| 2024-06-07T16:04:15Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:merge:DeepMount00/Llama-3-8b-Ita",
"base_model:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:merge:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
"base_model:merge:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
"base_model:jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0",
"base_model:merge:jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:nbeerbower/llama-3-gutenberg-8B",
"base_model:merge:nbeerbower/llama-3-gutenberg-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T15:18:04Z |
---
base_model:
- failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
- DeepMount00/Llama-3-8b-Ita
- nbeerbower/llama-3-gutenberg-8B
- jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
license: apache-2.0
tags:
- mergekit
- merge
---
# Model Merge Parameters
Base model: meta-llama/Meta-Llama-3-8B-Instruct
Models: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
DeepMount00/Llama-3-8b-Ita
nbeerbower/llama-3-gutenberg-8B
jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0
meta-llama/Meta-Llama-3-8B-Instruct
Merge method: dare_ties
Random seed: 42
density: 0.3
normalize: true
weight: 1.0
|
srikar-v05/phi3-Mini-Medical-Chat
|
srikar-v05
| 2024-06-07T15:58:45Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:quantized:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-07T15:44:31Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** srikar-v05
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
anyasims/uf6_opt125m_SFT-e2fb
|
anyasims
| 2024-06-07T15:58:21Z | 149 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T15:58:10Z |
---
library_name: transformers
tags:
- llama-factory
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Dhahlan2000/Simple_Translation-model-for-GPT-v7
|
Dhahlan2000
| 2024-06-07T15:58:15Z | 114 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Dhahlan2000/Simple_Translation-model-for-GPT-v6",
"base_model:finetune:Dhahlan2000/Simple_Translation-model-for-GPT-v6",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-06-07T14:18:58Z |
---
base_model: Dhahlan2000/Simple_Translation-model-for-GPT-v6
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Simple_Translation-model-for-GPT-v7
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. -->
# Simple_Translation-model-for-GPT-v7
This model is a fine-tuned version of [Dhahlan2000/Simple_Translation-model-for-GPT-v6](https://huggingface.co/Dhahlan2000/Simple_Translation-model-for-GPT-v6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5987
- Bleu: 10.3715
- Gen Len: 18.4887
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.8526 | 1.0 | 4641 | 0.6219 | 10.0994 | 18.4893 |
| 0.8127 | 2.0 | 9282 | 0.5987 | 10.3715 | 18.4887 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
neonicholasi/chatglm3-6b-base-extraction
|
neonicholasi
| 2024-06-07T15:56:38Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"chatglm",
"llama-factory",
"text-generation",
"conversational",
"custom_code",
"zh",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T11:45:56Z |
---
library_name: transformers
tags:
- llama-factory
license: apache-2.0
language:
- zh
pipeline_tag: text-generation
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
debashish1997/credio_itdr_model
|
debashish1997
| 2024-06-07T15:55:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T15:55:20Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** debashish1997
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
alexandrualexandru/code-llama-sparql-lcquad-dbpedia-context
|
alexandrualexandru
| 2024-06-07T15:50:01Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:finetune:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-06-07T15:49:49Z |
---
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- generated_from_trainer
model-index:
- name: code-llama-sparql-lcquad-dbpedia
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. -->
# code-llama-sparql-lcquad-dbpedia
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1978
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1267 | 0.0710 | 20 | 1.1174 |
| 0.7096 | 0.1421 | 40 | 0.4625 |
| 0.3148 | 0.2131 | 60 | 0.3134 |
| 0.2716 | 0.2842 | 80 | 0.2705 |
| 0.2267 | 0.3552 | 100 | 0.2945 |
| 0.2494 | 0.4263 | 120 | 0.2441 |
| 0.2369 | 0.4973 | 140 | 0.2363 |
| 0.2397 | 0.5684 | 160 | 0.2311 |
| 0.2256 | 0.6394 | 180 | 0.2261 |
| 0.2091 | 0.7105 | 200 | 0.2336 |
| 0.218 | 0.7815 | 220 | 0.2161 |
| 0.222 | 0.8526 | 240 | 0.2116 |
| 0.2169 | 0.9236 | 260 | 0.2094 |
| 0.2058 | 0.9947 | 280 | 0.2058 |
| 0.2081 | 1.0657 | 300 | 0.2039 |
| 0.2049 | 1.1368 | 320 | 0.2029 |
| 0.1925 | 1.2078 | 340 | 0.2019 |
| 0.2123 | 1.2789 | 360 | 0.1989 |
| 0.1933 | 1.3499 | 380 | 0.1980 |
| 0.2009 | 1.4210 | 400 | 0.1978 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.10.1
- Tokenizers 0.19.1
|
Iddah/llama3-8B-instruct-formal
|
Iddah
| 2024-06-07T15:49:39Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct",
"base_model:finetune:unsloth/llama-3-8b-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T15:46:45Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-Instruct
---
# Uploaded model
- **Developed by:** Iddah
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
johnsutor/Llama-3-8B-Instruct_dare_ties-density-0.1
|
johnsutor
| 2024-06-07T15:48:47Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:merge:DeepMount00/Llama-3-8b-Ita",
"base_model:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:merge:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
"base_model:merge:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
"base_model:jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0",
"base_model:merge:jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:nbeerbower/llama-3-gutenberg-8B",
"base_model:merge:nbeerbower/llama-3-gutenberg-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T15:10:13Z |
---
base_model:
- failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
- DeepMount00/Llama-3-8b-Ita
- nbeerbower/llama-3-gutenberg-8B
- jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
license: apache-2.0
tags:
- mergekit
- merge
---
# Model Merge Parameters
Base model: meta-llama/Meta-Llama-3-8B-Instruct
Models: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
DeepMount00/Llama-3-8b-Ita
nbeerbower/llama-3-gutenberg-8B
jpacifico/French-Alpaca-Llama3-8B-Instruct-v1.0
meta-llama/Meta-Llama-3-8B-Instruct
Merge method: dare_ties
Random seed: 42
density: 0.1
normalize: true
weight: 1.0
|
yitao416/test
|
yitao416
| 2024-06-07T15:48:28Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T15:22:26Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
base_model: Qwen/Qwen2-0.5B-Instruct
---
# Uploaded model
- **Developed by:** yitao416
- **License:** apache-2.0
- **Finetuned from model :** Qwen/Qwen2-0.5B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Zheng-Zong/own_finetuned_llama3
|
Zheng-Zong
| 2024-06-07T15:39:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T15:37:15Z |
---
library_name: transformers
tags:
- unsloth
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
ghost613/gemma7_on_korean_events
|
ghost613
| 2024-06-07T15:35:29Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:beomi/gemma-ko-7b",
"base_model:adapter:beomi/gemma-ko-7b",
"license:other",
"region:us"
] | null | 2024-06-07T15:35:02Z |
---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: beomi/gemma-ko-7b
model-index:
- name: gemma7_on_korean_events
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. -->
# gemma7_on_korean_events
This model is a fine-tuned version of [beomi/gemma-ko-7b](https://huggingface.co/beomi/gemma-ko-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5534
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 760
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0063 | 0.2632 | 20 | 0.6860 |
| 0.4681 | 0.5263 | 40 | 0.3754 |
| 0.3565 | 0.7895 | 60 | 0.3030 |
| 0.2872 | 1.0526 | 80 | 0.2880 |
| 0.2557 | 1.3158 | 100 | 0.2723 |
| 0.2213 | 1.5789 | 120 | 0.2674 |
| 0.2386 | 1.8421 | 140 | 0.2537 |
| 0.1885 | 2.1053 | 160 | 0.3032 |
| 0.1362 | 2.3684 | 180 | 0.2710 |
| 0.131 | 2.6316 | 200 | 0.2844 |
| 0.1366 | 2.8947 | 220 | 0.2626 |
| 0.0935 | 3.1579 | 240 | 0.3632 |
| 0.0618 | 3.4211 | 260 | 0.3315 |
| 0.0672 | 3.6842 | 280 | 0.3255 |
| 0.0684 | 3.9474 | 300 | 0.3238 |
| 0.0393 | 4.2105 | 320 | 0.4230 |
| 0.0311 | 4.4737 | 340 | 0.4180 |
| 0.0337 | 4.7368 | 360 | 0.3933 |
| 0.0402 | 5.0 | 380 | 0.3846 |
| 0.0179 | 5.2632 | 400 | 0.4478 |
| 0.0176 | 5.5263 | 420 | 0.4464 |
| 0.0274 | 5.7895 | 440 | 0.4003 |
| 0.017 | 6.0526 | 460 | 0.4284 |
| 0.0112 | 6.3158 | 480 | 0.4675 |
| 0.0107 | 6.5789 | 500 | 0.4715 |
| 0.0115 | 6.8421 | 520 | 0.4911 |
| 0.0107 | 7.1053 | 540 | 0.4776 |
| 0.0053 | 7.3684 | 560 | 0.4829 |
| 0.0049 | 7.6316 | 580 | 0.4962 |
| 0.0046 | 7.8947 | 600 | 0.5087 |
| 0.0039 | 8.1579 | 620 | 0.5240 |
| 0.0028 | 8.4211 | 640 | 0.5317 |
| 0.0035 | 8.6842 | 660 | 0.5351 |
| 0.0034 | 8.9474 | 680 | 0.5393 |
| 0.0013 | 9.2105 | 700 | 0.5445 |
| 0.0031 | 9.4737 | 720 | 0.5502 |
| 0.0018 | 9.7368 | 740 | 0.5523 |
| 0.002 | 10.0 | 760 | 0.5534 |
### Framework versions
- PEFT 0.11.2.dev0
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1
|
tbrear/Oiledteen
|
tbrear
| 2024-06-07T15:34:37Z | 16 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2024-06-07T15:33:41Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: Oiled teen
parameters:
negative_prompt: 'Old, adult, mature, '
output:
url: images/90764369_068_de5d.jpeg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Oiled, oil, oil massage, wet teen, oiled teens, skinny, petite
---
# Oiled teens
<Gallery />
## Model description
Collection of 1000+ skinny petite oiled teens
## Trigger words
You should use `Oiled` to trigger the image generation.
You should use `oil` to trigger the image generation.
You should use `oil massage` to trigger the image generation.
You should use `wet teen` to trigger the image generation.
You should use `oiled teens` to trigger the image generation.
You should use `skinny` to trigger the image generation.
You should use `petite` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/tbrear/Oiledteen/tree/main) them in the Files & versions tab.
|
Albatu1773/opt-125m-fine-tuned-lora-model_v2
|
Albatu1773
| 2024-06-07T15:34:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:45:17Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
DioBot2000/FULL-12epoch-XLMRoBERTa-finetuned-CEFR_ner-60000news
|
DioBot2000
| 2024-06-07T15:32:31Z | 118 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-06-07T15:32:03Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: FULL-12epoch-XLMRoBERTa-finetuned-CEFR_ner-60000news
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. -->
# FULL-12epoch-XLMRoBERTa-finetuned-CEFR_ner-60000news
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0687
- Accuracy: 0.3222
- Precision: 0.6358
- Recall: 0.8475
- F1: 0.6074
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.087 | 1.0 | 1563 | 0.0917 | 0.3188 | 0.7203 | 0.8273 | 0.6562 |
| 0.08 | 2.0 | 3126 | 0.0747 | 0.3204 | 0.7147 | 0.8331 | 0.6569 |
| 0.0666 | 3.0 | 4689 | 0.0691 | 0.3211 | 0.7195 | 0.8376 | 0.6624 |
| 0.0583 | 4.0 | 6252 | 0.0667 | 0.3213 | 0.6889 | 0.8419 | 0.6433 |
| 0.0514 | 5.0 | 7815 | 0.0650 | 0.3216 | 0.7043 | 0.8433 | 0.6543 |
| 0.0463 | 6.0 | 9378 | 0.0642 | 0.3219 | 0.6780 | 0.8444 | 0.6362 |
| 0.0421 | 7.0 | 10941 | 0.0635 | 0.3220 | 0.6759 | 0.8458 | 0.6354 |
| 0.0385 | 8.0 | 12504 | 0.0644 | 0.3220 | 0.6330 | 0.8470 | 0.6066 |
| 0.0358 | 9.0 | 14067 | 0.0670 | 0.3221 | 0.6368 | 0.8467 | 0.6068 |
| 0.0331 | 10.0 | 15630 | 0.0676 | 0.3222 | 0.6442 | 0.8468 | 0.6130 |
| 0.0309 | 11.0 | 17193 | 0.0680 | 0.3222 | 0.6377 | 0.8472 | 0.6092 |
| 0.0298 | 12.0 | 18756 | 0.0687 | 0.3222 | 0.6358 | 0.8475 | 0.6074 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.19.2
- Tokenizers 0.19.1
|
comet24082002/finetuned_bge_ver35
|
comet24082002
| 2024-06-07T15:27:56Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"feature-extraction",
"generated_from_trainer",
"base_model:BAAI/bge-m3",
"base_model:finetune:BAAI/bge-m3",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-06-07T06:16:48Z |
---
license: mit
base_model: BAAI/bge-m3
tags:
- generated_from_trainer
model-index:
- name: finetuned_bge_ver35
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. -->
# finetuned_bge_ver35
This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 7.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
smcenlly/segformer-finetuned-sidewalk-10k-steps
|
smcenlly
| 2024-06-07T15:27:31Z | 207 | 0 |
transformers
|
[
"transformers",
"safetensors",
"segformer",
"image-segmentation",
"vision",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2024-06-07T05:22:01Z |
---
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-sidewalk-10k-steps
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-finetuned-sidewalk-10k-steps
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6095
- Mean Iou: 0.2881
- Mean Accuracy: 0.3546
- Overall Accuracy: 0.8313
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.7705
- Accuracy Flat-sidewalk: 0.9498
- Accuracy Flat-crosswalk: 0.1340
- Accuracy Flat-cyclinglane: 0.8681
- Accuracy Flat-parkingdriveway: 0.2555
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.5391
- Accuracy Human-person: 0.7858
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.9234
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.8710
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8511
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.5804
- Accuracy Construction-fenceguardrail: 0.4355
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.3931
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.9126
- Accuracy Nature-terrain: 0.8313
- Accuracy Sky: 0.9633
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0209
- Accuracy Void-static: 0.2608
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.6844
- Iou Flat-sidewalk: 0.8516
- Iou Flat-crosswalk: 0.0905
- Iou Flat-cyclinglane: 0.6851
- Iou Flat-parkingdriveway: 0.2043
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.3766
- Iou Human-person: 0.5327
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.7630
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.6814
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.6784
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.3576
- Iou Construction-fenceguardrail: 0.3553
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.2718
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.8160
- Iou Nature-terrain: 0.7180
- Iou Sky: 0.9227
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0191
- Iou Void-static: 0.2111
- Iou Void-unclear: 0.0
## 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: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
| 2.6286 | 1.0 | 107 | 1.8181 | 0.1171 | 0.1634 | 0.6717 | nan | 0.6306 | 0.9255 | 0.0 | 0.0094 | 0.0001 | nan | 0.0012 | 0.0 | 0.0 | 0.8679 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7730 | 0.0 | 0.0057 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9381 | 0.3122 | 0.7647 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.4481 | 0.6987 | 0.0 | 0.0094 | 0.0001 | nan | 0.0012 | 0.0 | 0.0 | 0.4529 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4526 | 0.0 | 0.0056 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6348 | 0.2923 | 0.7502 | 0.0 | 0.0 | 0.0001 | 0.0 |
| 1.8336 | 2.0 | 214 | 1.4625 | 0.1293 | 0.1823 | 0.6870 | nan | 0.7452 | 0.9141 | 0.0008 | 0.1033 | 0.0 | nan | 0.0020 | 0.0 | 0.0 | 0.8612 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8218 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7270 | 0.8431 | 0.8122 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4802 | 0.7308 | 0.0008 | 0.1016 | 0.0 | nan | 0.0020 | 0.0 | 0.0 | 0.5072 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4932 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6013 | 0.4276 | 0.7903 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.526 | 3.0 | 321 | 1.2558 | 0.1429 | 0.1939 | 0.7077 | nan | 0.6255 | 0.9446 | 0.0136 | 0.4912 | 0.0013 | nan | 0.0009 | 0.0 | 0.0 | 0.9019 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8599 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7262 | 0.7610 | 0.8796 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5088 | 0.7241 | 0.0136 | 0.4121 | 0.0013 | nan | 0.0009 | 0.0 | 0.0 | 0.5108 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5052 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6114 | 0.4509 | 0.8337 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3177 | 4.0 | 428 | 1.1051 | 0.1528 | 0.1993 | 0.7276 | nan | 0.6192 | 0.9398 | 0.0 | 0.6093 | 0.0072 | nan | 0.0149 | 0.0 | 0.0 | 0.8931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8390 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8639 | 0.6996 | 0.8917 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5043 | 0.7197 | 0.0 | 0.5088 | 0.0070 | nan | 0.0143 | 0.0 | 0.0 | 0.5535 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5263 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6828 | 0.5304 | 0.8431 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.192 | 5.0 | 535 | 1.0533 | 0.1591 | 0.2060 | 0.7356 | nan | 0.6333 | 0.9471 | 0.0071 | 0.6331 | 0.0518 | nan | 0.0512 | 0.0 | 0.0 | 0.8437 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8791 | 0.0 | 0.0021 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8150 | 0.7926 | 0.9342 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5301 | 0.7436 | 0.0070 | 0.5448 | 0.0450 | nan | 0.0453 | 0.0 | 0.0 | 0.6187 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5282 | 0.0 | 0.0021 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6618 | 0.5167 | 0.8494 | 0.0 | 0.0 | 0.0000 | 0.0 |
| 1.1066 | 6.0 | 642 | 0.9737 | 0.1645 | 0.2153 | 0.7459 | nan | 0.7737 | 0.8898 | 0.0194 | 0.7187 | 0.0968 | nan | 0.0597 | 0.0 | 0.0 | 0.9025 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8828 | 0.0 | 0.0082 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8544 | 0.7723 | 0.9125 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.5693 | 0.7770 | 0.0192 | 0.5704 | 0.0733 | nan | 0.0518 | 0.0 | 0.0 | 0.5623 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5249 | 0.0 | 0.0082 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6853 | 0.5789 | 0.8442 | 0.0 | 0.0 | 0.0002 | 0.0 |
| 1.0345 | 7.0 | 749 | 0.9358 | 0.1641 | 0.2080 | 0.7484 | nan | 0.6676 | 0.9600 | 0.0015 | 0.5846 | 0.0333 | nan | 0.1226 | 0.0 | 0.0 | 0.8343 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8947 | 0.0 | 0.0195 | 0.0 | 0.0 | nan | 0.0 | 0.0074 | 0.0 | 0.0 | 0.8678 | 0.7738 | 0.8896 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5882 | 0.7553 | 0.0014 | 0.5142 | 0.0322 | nan | 0.1033 | 0.0 | 0.0 | 0.6033 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5328 | 0.0 | 0.0189 | 0.0 | 0.0 | nan | 0.0 | 0.0074 | 0.0 | 0.0 | 0.6840 | 0.5633 | 0.8454 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.9398 | 8.0 | 856 | 0.9636 | 0.1635 | 0.2096 | 0.7294 | nan | 0.5132 | 0.9711 | 0.0792 | 0.5573 | 0.0947 | nan | 0.1237 | 0.0 | 0.0 | 0.8577 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9130 | 0.0 | 0.0729 | 0.0 | 0.0 | nan | 0.0 | 0.0297 | 0.0 | 0.0 | 0.7992 | 0.7867 | 0.9099 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4766 | 0.7221 | 0.0628 | 0.4650 | 0.0834 | nan | 0.0990 | 0.0 | 0.0 | 0.6044 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5367 | 0.0 | 0.0649 | 0.0 | 0.0 | nan | 0.0 | 0.0291 | 0.0 | 0.0 | 0.6904 | 0.5492 | 0.8475 | 0.0 | 0.0 | 0.0000 | 0.0 |
| 0.877 | 9.0 | 963 | 0.9514 | 0.1651 | 0.2131 | 0.7267 | nan | 0.4504 | 0.9665 | 0.0883 | 0.6428 | 0.0535 | nan | 0.1474 | 0.0 | 0.0 | 0.9087 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8611 | 0.0 | 0.1287 | 0.0 | 0.0 | nan | 0.0 | 0.0728 | 0.0 | 0.0 | 0.8297 | 0.7927 | 0.8753 | 0.0 | 0.0 | 0.0025 | 0.0 | nan | 0.4186 | 0.7179 | 0.0694 | 0.5261 | 0.0506 | nan | 0.1086 | 0.0 | 0.0 | 0.5726 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5563 | 0.0 | 0.1121 | 0.0 | 0.0 | nan | 0.0 | 0.0677 | 0.0 | 0.0 | 0.6842 | 0.5551 | 0.8429 | 0.0 | 0.0 | 0.0024 | 0.0 |
| 0.8582 | 10.0 | 1070 | 0.8750 | 0.1839 | 0.2417 | 0.7451 | nan | 0.6248 | 0.9409 | 0.2622 | 0.6829 | 0.1184 | nan | 0.3192 | 0.0660 | 0.0 | 0.9059 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8449 | 0.0 | 0.3226 | 0.0 | 0.0 | nan | 0.0 | 0.1033 | 0.0 | 0.0 | 0.7591 | 0.8623 | 0.9142 | 0.0 | 0.0 | 0.0066 | 0.0 | nan | 0.5565 | 0.7807 | 0.1615 | 0.5223 | 0.0978 | nan | 0.1989 | 0.0649 | 0.0 | 0.6020 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5934 | 0.0 | 0.2091 | 0.0 | 0.0 | nan | 0.0 | 0.0914 | 0.0 | 0.0 | 0.6305 | 0.5065 | 0.8613 | 0.0 | 0.0 | 0.0065 | 0.0 |
| 0.8174 | 11.0 | 1177 | 0.8511 | 0.1925 | 0.2406 | 0.7630 | nan | 0.6198 | 0.9537 | 0.1152 | 0.7450 | 0.0962 | nan | 0.2211 | 0.1179 | 0.0 | 0.8453 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8332 | 0.0 | 0.3622 | 0.0 | 0.0 | nan | 0.0 | 0.1466 | 0.0 | 0.0 | 0.8842 | 0.8011 | 0.9317 | 0.0 | 0.0 | 0.0253 | 0.0 | nan | 0.5534 | 0.7652 | 0.0810 | 0.5479 | 0.0897 | nan | 0.1529 | 0.1052 | 0.0 | 0.6908 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5975 | 0.0 | 0.2391 | 0.0 | 0.0 | nan | 0.0 | 0.1060 | 0.0 | 0.0 | 0.7168 | 0.6235 | 0.8676 | 0.0 | 0.0 | 0.0241 | 0.0 |
| 0.8053 | 12.0 | 1284 | 0.8258 | 0.1958 | 0.2465 | 0.7656 | nan | 0.5861 | 0.9566 | 0.1011 | 0.7279 | 0.1687 | nan | 0.2568 | 0.1789 | 0.0 | 0.8924 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8964 | 0.0 | 0.3750 | 0.0001 | 0.0 | nan | 0.0 | 0.2163 | 0.0 | 0.0 | 0.8893 | 0.7173 | 0.9173 | 0.0 | 0.0 | 0.0083 | 0.0 | nan | 0.5385 | 0.7828 | 0.0763 | 0.5563 | 0.1347 | nan | 0.1770 | 0.1510 | 0.0 | 0.6499 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5855 | 0.0 | 0.2200 | 0.0001 | 0.0 | nan | 0.0 | 0.1464 | 0.0 | 0.0 | 0.7486 | 0.6286 | 0.8615 | 0.0 | 0.0 | 0.0081 | 0.0 |
| 0.7937 | 13.0 | 1391 | 0.8232 | 0.2005 | 0.2562 | 0.7638 | nan | 0.5553 | 0.9526 | 0.1341 | 0.7287 | 0.1679 | nan | 0.3497 | 0.4409 | 0.0 | 0.8663 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8407 | 0.0 | 0.2968 | 0.0011 | 0.0 | nan | 0.0 | 0.1930 | 0.0 | 0.0 | 0.9081 | 0.7888 | 0.9409 | 0.0 | 0.0 | 0.0349 | 0.0 | nan | 0.5081 | 0.7915 | 0.0925 | 0.5494 | 0.1468 | nan | 0.1990 | 0.2966 | 0.0 | 0.6470 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5892 | 0.0 | 0.2040 | 0.0010 | 0.0 | nan | 0.0 | 0.1191 | 0.0 | 0.0 | 0.7203 | 0.6447 | 0.8744 | 0.0 | 0.0 | 0.0336 | 0.0 |
| 0.7716 | 14.0 | 1498 | 0.7484 | 0.2081 | 0.2618 | 0.7843 | nan | 0.7722 | 0.9215 | 0.1264 | 0.7666 | 0.1844 | nan | 0.3523 | 0.3594 | 0.0 | 0.9196 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8831 | 0.0 | 0.2244 | 0.0188 | 0.0 | nan | 0.0 | 0.2140 | 0.0 | 0.0 | 0.9013 | 0.7828 | 0.9271 | 0.0 | 0.0 | 0.0247 | 0.0 | nan | 0.6323 | 0.8154 | 0.0948 | 0.6211 | 0.1478 | nan | 0.2139 | 0.2617 | 0.0 | 0.6203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6000 | 0.0 | 0.1854 | 0.0184 | 0.0 | nan | 0.0 | 0.1356 | 0.0 | 0.0 | 0.7443 | 0.6675 | 0.8759 | 0.0 | 0.0 | 0.0237 | 0.0 |
| 0.697 | 15.0 | 1605 | 0.7640 | 0.2154 | 0.2750 | 0.7820 | nan | 0.6800 | 0.9378 | 0.1242 | 0.7702 | 0.2647 | nan | 0.3212 | 0.5078 | 0.0 | 0.9206 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8370 | 0.0 | 0.4683 | 0.0281 | 0.0 | nan | 0.0 | 0.2391 | 0.0 | 0.0 | 0.8975 | 0.7546 | 0.9374 | 0.0 | 0.0 | 0.1111 | 0.0 | nan | 0.5954 | 0.8154 | 0.0834 | 0.5941 | 0.1778 | nan | 0.2096 | 0.3168 | 0.0 | 0.6310 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6256 | 0.0 | 0.2543 | 0.0276 | 0.0 | nan | 0.0 | 0.1788 | 0.0 | 0.0 | 0.7602 | 0.6507 | 0.8786 | 0.0 | 0.0 | 0.0922 | 0.0 |
| 0.7087 | 16.0 | 1712 | 0.7756 | 0.2143 | 0.2689 | 0.7783 | nan | 0.6356 | 0.9559 | 0.1393 | 0.7969 | 0.1327 | nan | 0.2887 | 0.4540 | 0.0 | 0.9110 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8060 | 0.0 | 0.3380 | 0.1029 | 0.0 | nan | 0.0 | 0.2048 | 0.0 | 0.0 | 0.8951 | 0.7845 | 0.9515 | 0.0 | 0.0 | 0.2063 | 0.0 | nan | 0.5469 | 0.7997 | 0.0929 | 0.6084 | 0.1135 | nan | 0.1712 | 0.3306 | 0.0 | 0.6166 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6291 | 0.0 | 0.2484 | 0.0982 | 0.0 | nan | 0.0 | 0.1616 | 0.0 | 0.0 | 0.7539 | 0.6542 | 0.8732 | 0.0 | 0.0 | 0.1599 | 0.0 |
| 0.7095 | 17.0 | 1819 | 0.7780 | 0.2134 | 0.2775 | 0.7724 | nan | 0.6105 | 0.9634 | 0.2158 | 0.6514 | 0.1226 | nan | 0.3240 | 0.6724 | 0.0 | 0.9104 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8485 | 0.0 | 0.5700 | 0.0206 | 0.0 | nan | 0.0 | 0.2101 | 0.0 | 0.0 | 0.8578 | 0.7978 | 0.9248 | 0.0 | 0.0 | 0.1781 | 0.0 | nan | 0.5672 | 0.7839 | 0.1105 | 0.5158 | 0.1103 | nan | 0.2067 | 0.3594 | 0.0 | 0.6401 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6241 | 0.0 | 0.2685 | 0.0198 | 0.0 | nan | 0.0 | 0.1755 | 0.0 | 0.0 | 0.7708 | 0.6613 | 0.8734 | 0.0 | 0.0 | 0.1423 | 0.0 |
| 0.6581 | 18.0 | 1926 | 0.7508 | 0.2171 | 0.2795 | 0.7801 | nan | 0.7706 | 0.9064 | 0.2124 | 0.6835 | 0.1403 | nan | 0.4551 | 0.6189 | 0.0 | 0.8718 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0009 | 0.0 | 0.0 | 0.8914 | 0.0 | 0.4337 | 0.0635 | 0.0 | nan | 0.0 | 0.2098 | 0.0 | 0.0 | 0.8830 | 0.8179 | 0.9307 | 0.0 | 0.0 | 0.0544 | 0.0 | nan | 0.6302 | 0.8074 | 0.1203 | 0.5742 | 0.1166 | nan | 0.2470 | 0.3759 | 0.0 | 0.6507 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0009 | 0.0 | 0.0 | 0.6008 | 0.0 | 0.2527 | 0.0586 | 0.0 | nan | 0.0 | 0.1727 | 0.0 | 0.0 | 0.7493 | 0.6594 | 0.8812 | 0.0 | 0.0 | 0.0496 | 0.0 |
| 0.6426 | 19.0 | 2033 | 0.7245 | 0.2188 | 0.2826 | 0.7828 | nan | 0.6460 | 0.9538 | 0.2403 | 0.7412 | 0.1362 | nan | 0.4466 | 0.6738 | 0.0 | 0.8529 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8634 | 0.0 | 0.5070 | 0.0553 | 0.0 | nan | 0.0 | 0.2194 | 0.0 | 0.0 | 0.9065 | 0.7283 | 0.9258 | 0.0 | 0.0 | 0.1474 | 0.0 | nan | 0.5872 | 0.8063 | 0.1530 | 0.5897 | 0.1153 | nan | 0.2640 | 0.2673 | 0.0 | 0.7023 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6152 | 0.0 | 0.2695 | 0.0521 | 0.0 | nan | 0.0 | 0.1736 | 0.0 | 0.0 | 0.7631 | 0.6394 | 0.8815 | 0.0 | 0.0 | 0.1230 | 0.0 |
| 0.6702 | 20.0 | 2140 | 0.7611 | 0.2146 | 0.2804 | 0.7730 | nan | 0.6534 | 0.9088 | 0.1364 | 0.8361 | 0.0680 | nan | 0.3070 | 0.6003 | 0.0 | 0.8907 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0054 | 0.0 | 0.0 | 0.8747 | 0.0 | 0.4397 | 0.1578 | 0.0 | nan | 0.0 | 0.3142 | 0.0 | 0.0 | 0.8835 | 0.8413 | 0.9161 | 0.0 | 0.0 | 0.1389 | 0.0 | nan | 0.5790 | 0.7775 | 0.0943 | 0.5104 | 0.0628 | nan | 0.2177 | 0.2765 | 0.0 | 0.6749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0054 | 0.0 | 0.0 | 0.6273 | 0.0 | 0.2596 | 0.1387 | 0.0 | nan | 0.0 | 0.1920 | 0.0 | 0.0 | 0.7738 | 0.6739 | 0.8831 | 0.0 | 0.0 | 0.1208 | 0.0 |
| 0.6459 | 21.0 | 2247 | 0.6979 | 0.2327 | 0.2995 | 0.7935 | nan | 0.7089 | 0.9446 | 0.1698 | 0.7339 | 0.2010 | nan | 0.4253 | 0.7356 | 0.0 | 0.8986 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0489 | 0.0 | 0.0 | 0.8166 | 0.0 | 0.5715 | 0.1877 | 0.0 | nan | 0.0 | 0.2962 | 0.0 | 0.0 | 0.8889 | 0.8529 | 0.9257 | 0.0 | 0.0 | 0.1778 | 0.0 | nan | 0.6276 | 0.8195 | 0.1146 | 0.6042 | 0.1517 | nan | 0.2476 | 0.3503 | 0.0 | 0.6861 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0482 | 0.0 | 0.0 | 0.6283 | 0.0 | 0.2914 | 0.1585 | 0.0 | nan | 0.0 | 0.2180 | 0.0 | 0.0 | 0.7814 | 0.6855 | 0.8852 | 0.0 | 0.0 | 0.1496 | 0.0 |
| 0.6129 | 22.0 | 2354 | 0.6890 | 0.2361 | 0.3018 | 0.8011 | nan | 0.7559 | 0.9447 | 0.2162 | 0.7605 | 0.2859 | nan | 0.3558 | 0.7490 | 0.0 | 0.9139 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0361 | 0.0 | 0.0 | 0.8660 | 0.0 | 0.4152 | 0.2028 | 0.0 | nan | 0.0 | 0.2883 | 0.0 | 0.0 | 0.8650 | 0.8445 | 0.9420 | 0.0 | 0.0 | 0.2157 | 0.0 | nan | 0.6661 | 0.8340 | 0.1244 | 0.6458 | 0.2020 | nan | 0.2536 | 0.3440 | 0.0 | 0.6636 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0360 | 0.0 | 0.0 | 0.6166 | 0.0 | 0.2880 | 0.1800 | 0.0 | nan | 0.0 | 0.2161 | 0.0 | 0.0 | 0.7648 | 0.6666 | 0.8811 | 0.0 | 0.0 | 0.1736 | 0.0 |
| 0.587 | 23.0 | 2461 | 0.6791 | 0.2490 | 0.3175 | 0.8026 | nan | 0.7249 | 0.9426 | 0.1412 | 0.8048 | 0.2683 | nan | 0.4024 | 0.7341 | 0.0 | 0.8995 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3563 | 0.0 | 0.0 | 0.7969 | 0.0 | 0.5959 | 0.2566 | 0.0 | nan | 0.0 | 0.3140 | 0.0 | 0.0 | 0.8977 | 0.8109 | 0.9393 | 0.0 | 0.0 | 0.2740 | 0.0 | nan | 0.6384 | 0.8359 | 0.0943 | 0.6255 | 0.1975 | nan | 0.2657 | 0.3433 | 0.0 | 0.7017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3341 | 0.0 | 0.0 | 0.6332 | 0.0 | 0.2868 | 0.2052 | 0.0 | nan | 0.0 | 0.2232 | 0.0 | 0.0 | 0.7927 | 0.6943 | 0.8923 | 0.0 | 0.0 | 0.2045 | 0.0 |
| 0.591 | 24.0 | 2568 | 0.6873 | 0.2347 | 0.3030 | 0.7947 | nan | 0.7352 | 0.9359 | 0.1105 | 0.8369 | 0.1648 | nan | 0.3745 | 0.7308 | 0.0 | 0.9372 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1727 | 0.0 | 0.0 | 0.7772 | 0.0 | 0.4216 | 0.3084 | 0.0 | nan | 0.0 | 0.3076 | 0.0 | 0.0 | 0.8907 | 0.8103 | 0.9508 | 0.0 | 0.0 | 0.2295 | 0.0 | nan | 0.6287 | 0.8325 | 0.0757 | 0.5974 | 0.1304 | nan | 0.2501 | 0.3415 | 0.0 | 0.6188 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1668 | 0.0 | 0.0 | 0.6259 | 0.0 | 0.2810 | 0.2333 | 0.0 | nan | 0.0 | 0.2182 | 0.0 | 0.0 | 0.7795 | 0.6710 | 0.8913 | 0.0 | 0.0 | 0.1671 | 0.0 |
| 0.5565 | 25.0 | 2675 | 0.6970 | 0.2425 | 0.3119 | 0.7951 | nan | 0.7627 | 0.9196 | 0.3318 | 0.8099 | 0.1691 | nan | 0.4192 | 0.7074 | 0.0 | 0.9130 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1820 | 0.0 | 0.0 | 0.8595 | 0.0 | 0.4412 | 0.2585 | 0.0 | nan | 0.0 | 0.3007 | 0.0 | 0.0 | 0.8415 | 0.8706 | 0.9511 | 0.0 | 0.0 | 0.2416 | 0.0 | nan | 0.6508 | 0.8209 | 0.2078 | 0.6405 | 0.1411 | nan | 0.2586 | 0.3796 | 0.0 | 0.6485 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1730 | 0.0 | 0.0 | 0.6181 | 0.0 | 0.2824 | 0.2301 | 0.0 | nan | 0.0 | 0.2300 | 0.0 | 0.0 | 0.7563 | 0.6375 | 0.8938 | 0.0 | 0.0 | 0.1909 | 0.0 |
| 0.5673 | 26.0 | 2782 | 0.6936 | 0.2464 | 0.3101 | 0.7956 | nan | 0.6847 | 0.9560 | 0.0266 | 0.6493 | 0.1651 | nan | 0.4754 | 0.7206 | 0.0 | 0.9053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4206 | 0.0 | 0.0 | 0.8428 | 0.0 | 0.5624 | 0.3126 | 0.0 | nan | 0.0 | 0.3289 | 0.0 | 0.0 | 0.8940 | 0.8233 | 0.9411 | 0.0 | 0.0 | 0.2145 | 0.0 | nan | 0.6067 | 0.8103 | 0.0212 | 0.5254 | 0.1418 | nan | 0.2812 | 0.4028 | 0.0 | 0.7105 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3881 | 0.0 | 0.0 | 0.6410 | 0.0 | 0.3044 | 0.2496 | 0.0 | nan | 0.0 | 0.2443 | 0.0 | 0.0 | 0.7941 | 0.6923 | 0.8954 | 0.0 | 0.0 | 0.1761 | 0.0 |
| 0.5759 | 27.0 | 2889 | 0.6665 | 0.2521 | 0.3173 | 0.8104 | nan | 0.7732 | 0.9421 | 0.0875 | 0.7788 | 0.1832 | nan | 0.4080 | 0.7830 | 0.0 | 0.8985 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3717 | 0.0 | 0.0 | 0.8734 | 0.0 | 0.4788 | 0.3323 | 0.0 | nan | 0.0 | 0.3734 | 0.0 | 0.0 | 0.8978 | 0.8224 | 0.9587 | 0.0 | 0.0 | 0.1901 | 0.0 | nan | 0.6721 | 0.8285 | 0.0644 | 0.6635 | 0.1458 | nan | 0.2751 | 0.3303 | 0.0 | 0.7125 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3469 | 0.0 | 0.0 | 0.6399 | 0.0 | 0.3088 | 0.2740 | 0.0 | nan | 0.0 | 0.2524 | 0.0 | 0.0 | 0.7920 | 0.7042 | 0.8952 | 0.0 | 0.0 | 0.1605 | 0.0 |
| 0.5519 | 28.0 | 2996 | 0.6882 | 0.2494 | 0.3181 | 0.8030 | nan | 0.6854 | 0.9535 | 0.1770 | 0.8538 | 0.1942 | nan | 0.3966 | 0.7358 | 0.0 | 0.9037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3296 | 0.0 | 0.0 | 0.8041 | 0.0 | 0.6758 | 0.2825 | 0.0 | nan | 0.0 | 0.2941 | 0.0 | 0.0 | 0.8816 | 0.8495 | 0.9256 | 0.0 | 0.0 | 0.2358 | 0.0 | nan | 0.6237 | 0.8353 | 0.1117 | 0.6402 | 0.1595 | nan | 0.2749 | 0.3770 | 0.0 | 0.6903 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3072 | 0.0 | 0.0 | 0.6350 | 0.0 | 0.2882 | 0.2358 | 0.0 | nan | 0.0 | 0.2300 | 0.0 | 0.0 | 0.7959 | 0.7063 | 0.8876 | 0.0 | 0.0 | 0.1837 | 0.0 |
| 0.5543 | 29.0 | 3103 | 0.6939 | 0.2562 | 0.3221 | 0.8003 | nan | 0.5953 | 0.9575 | 0.1294 | 0.8713 | 0.2264 | nan | 0.4049 | 0.7236 | 0.0 | 0.8913 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5928 | 0.0 | 0.0 | 0.8923 | 0.0 | 0.4924 | 0.3422 | 0.0 | nan | 0.0 | 0.3405 | 0.0 | 0.0 | 0.8887 | 0.8011 | 0.9496 | 0.0 | 0.0 | 0.2076 | 0.0 | nan | 0.5561 | 0.8312 | 0.0831 | 0.6258 | 0.1713 | nan | 0.2404 | 0.3998 | 0.0 | 0.7266 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5231 | 0.0 | 0.0 | 0.6360 | 0.0 | 0.3023 | 0.2928 | 0.0 | nan | 0.0 | 0.2467 | 0.0 | 0.0 | 0.8007 | 0.6943 | 0.9016 | 0.0 | 0.0 | 0.1682 | 0.0 |
| 0.539 | 30.0 | 3210 | 0.6745 | 0.2556 | 0.3208 | 0.8078 | nan | 0.6808 | 0.9574 | 0.1187 | 0.8040 | 0.2318 | nan | 0.4663 | 0.7716 | 0.0 | 0.9109 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5974 | 0.0 | 0.0 | 0.8887 | 0.0 | 0.3730 | 0.2630 | 0.0 | nan | 0.0 | 0.3166 | 0.0 | 0.0 | 0.8920 | 0.8438 | 0.9532 | 0.0 | 0.0 | 0.1962 | 0.0 | nan | 0.6214 | 0.8387 | 0.0838 | 0.6456 | 0.1843 | nan | 0.2902 | 0.3659 | 0.0 | 0.7135 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5120 | 0.0 | 0.0 | 0.6310 | 0.0 | 0.2731 | 0.2455 | 0.0 | nan | 0.0 | 0.2429 | 0.0 | 0.0 | 0.7802 | 0.6832 | 0.9058 | 0.0 | 0.0 | 0.1619 | 0.0 |
| 0.5517 | 31.0 | 3317 | 0.6675 | 0.2597 | 0.3320 | 0.8034 | nan | 0.7208 | 0.9193 | 0.2627 | 0.8866 | 0.1661 | nan | 0.3632 | 0.7967 | 0.0 | 0.9130 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6360 | 0.0 | 0.0 | 0.8301 | 0.0 | 0.5332 | 0.2807 | 0.0 | nan | 0.0 | 0.3774 | 0.0 | 0.0 | 0.9068 | 0.8339 | 0.9590 | 0.0 | 0.0 | 0.2374 | 0.0 | nan | 0.6386 | 0.8266 | 0.1726 | 0.5421 | 0.1405 | nan | 0.2481 | 0.4136 | 0.0 | 0.6972 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5330 | 0.0 | 0.0 | 0.6504 | 0.0 | 0.3249 | 0.2532 | 0.0 | nan | 0.0 | 0.2697 | 0.0 | 0.0 | 0.7994 | 0.7023 | 0.9021 | 0.0 | 0.0 | 0.1950 | 0.0 |
| 0.5111 | 32.0 | 3424 | 0.6563 | 0.2651 | 0.3346 | 0.8095 | nan | 0.7287 | 0.9359 | 0.1284 | 0.8389 | 0.2655 | nan | 0.4372 | 0.7783 | 0.0 | 0.9067 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6431 | 0.0 | 0.0 | 0.8414 | 0.0 | 0.3977 | 0.3680 | 0.0 | nan | 0.0 | 0.4788 | 0.0 | 0.0 | 0.8987 | 0.8325 | 0.9500 | 0.0 | 0.0 | 0.2780 | 0.0 | nan | 0.6414 | 0.8299 | 0.0884 | 0.6024 | 0.2065 | nan | 0.2703 | 0.4066 | 0.0 | 0.7223 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5560 | 0.0 | 0.0 | 0.6554 | 0.0 | 0.3017 | 0.3239 | 0.0 | nan | 0.0 | 0.2607 | 0.0 | 0.0 | 0.7939 | 0.6986 | 0.9078 | 0.0 | 0.0 | 0.2189 | 0.0 |
| 0.4945 | 33.0 | 3531 | 0.6428 | 0.2652 | 0.3290 | 0.8103 | nan | 0.7400 | 0.9456 | 0.1315 | 0.7854 | 0.2355 | nan | 0.4819 | 0.6784 | 0.0 | 0.9072 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6235 | 0.0 | 0.0 | 0.8110 | 0.0 | 0.5156 | 0.4098 | 0.0 | nan | 0.0 | 0.3817 | 0.0 | 0.0 | 0.9075 | 0.8345 | 0.9478 | 0.0 | 0.0 | 0.1922 | 0.0 | nan | 0.6629 | 0.8240 | 0.0888 | 0.6027 | 0.1846 | nan | 0.3229 | 0.4525 | 0.0 | 0.6842 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5536 | 0.0 | 0.0 | 0.6578 | 0.0 | 0.3255 | 0.3138 | 0.0 | nan | 0.0 | 0.2600 | 0.0 | 0.0 | 0.7950 | 0.7071 | 0.8974 | 0.0 | 0.0 | 0.1529 | 0.0 |
| 0.5566 | 34.0 | 3638 | 0.6430 | 0.2652 | 0.3343 | 0.8119 | nan | 0.7506 | 0.9485 | 0.1373 | 0.7617 | 0.2401 | nan | 0.5094 | 0.7984 | 0.0 | 0.8919 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7165 | 0.0 | 0.0 | 0.8105 | 0.0 | 0.5638 | 0.3511 | 0.0 | nan | 0.0 | 0.3629 | 0.0 | 0.0 | 0.9171 | 0.7909 | 0.9522 | 0.0 | 0.0 | 0.1937 | 0.0 | nan | 0.6646 | 0.8310 | 0.0938 | 0.6654 | 0.1870 | nan | 0.3027 | 0.3954 | 0.0 | 0.7227 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5810 | 0.0 | 0.0 | 0.6415 | 0.0 | 0.3167 | 0.2941 | 0.0 | nan | 0.0 | 0.2454 | 0.0 | 0.0 | 0.7938 | 0.6864 | 0.9048 | 0.0 | 0.0 | 0.1617 | 0.0 |
| 0.4937 | 35.0 | 3745 | 0.6567 | 0.2675 | 0.3356 | 0.8082 | nan | 0.7545 | 0.9241 | 0.1340 | 0.8054 | 0.2432 | nan | 0.5336 | 0.7489 | 0.0 | 0.9184 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7152 | 0.0 | 0.0 | 0.8418 | 0.0 | 0.6014 | 0.3744 | 0.0 | nan | 0.0 | 0.3334 | 0.0 | 0.0 | 0.9021 | 0.7771 | 0.9489 | 0.0 | 0.0 | 0.1837 | 0.0 | nan | 0.6532 | 0.8199 | 0.0922 | 0.6511 | 0.1876 | nan | 0.2792 | 0.4810 | 0.0 | 0.7016 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5924 | 0.0 | 0.0 | 0.6616 | 0.0 | 0.3433 | 0.3078 | 0.0 | nan | 0.0 | 0.2422 | 0.0 | 0.0 | 0.7996 | 0.6874 | 0.9074 | 0.0 | 0.0 | 0.1509 | 0.0 |
| 0.5153 | 36.0 | 3852 | 0.6487 | 0.2669 | 0.3347 | 0.8111 | nan | 0.6899 | 0.9487 | 0.1302 | 0.8613 | 0.1863 | nan | 0.4750 | 0.7652 | 0.0 | 0.9031 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6507 | 0.0 | 0.0 | 0.8072 | 0.0 | 0.5373 | 0.4560 | 0.0 | nan | 0.0 | 0.3429 | 0.0 | 0.0 | 0.9169 | 0.8264 | 0.9579 | 0.0 | 0.0 | 0.2557 | 0.0 | nan | 0.6214 | 0.8292 | 0.0922 | 0.6469 | 0.1563 | nan | 0.2912 | 0.4294 | 0.0 | 0.7475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5577 | 0.0 | 0.0 | 0.6468 | 0.0 | 0.3131 | 0.3209 | 0.0 | nan | 0.0 | 0.2511 | 0.0 | 0.0 | 0.8035 | 0.7209 | 0.9071 | 0.0 | 0.0 | 0.2040 | 0.0 |
| 0.4799 | 37.0 | 3959 | 0.6417 | 0.2709 | 0.3385 | 0.8128 | nan | 0.7164 | 0.9375 | 0.1316 | 0.8677 | 0.2338 | nan | 0.4291 | 0.7674 | 0.0 | 0.8821 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7224 | 0.0 | 0.0 | 0.8288 | 0.0 | 0.5695 | 0.3840 | 0.0 | nan | 0.0 | 0.3546 | 0.0 | 0.0 | 0.9239 | 0.7850 | 0.9592 | 0.0 | 0.0 | 0.3387 | 0.0 | nan | 0.6482 | 0.8243 | 0.0909 | 0.6552 | 0.1885 | nan | 0.2861 | 0.4622 | 0.0 | 0.7307 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5758 | 0.0 | 0.0 | 0.6606 | 0.0 | 0.3390 | 0.3260 | 0.0 | nan | 0.0 | 0.2559 | 0.0 | 0.0 | 0.7939 | 0.6962 | 0.9086 | 0.0 | 0.0 | 0.2275 | 0.0 |
| 0.4863 | 38.0 | 4066 | 0.6544 | 0.2653 | 0.3296 | 0.8080 | nan | 0.7432 | 0.9174 | 0.1061 | 0.8839 | 0.1812 | nan | 0.3608 | 0.6850 | 0.0 | 0.8926 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7417 | 0.0 | 0.0 | 0.8696 | 0.0 | 0.4454 | 0.3772 | 0.0 | nan | 0.0 | 0.3579 | 0.0 | 0.0 | 0.9243 | 0.7896 | 0.9503 | 0.0 | 0.0 | 0.3213 | 0.0 | nan | 0.6546 | 0.8262 | 0.0853 | 0.5395 | 0.1506 | nan | 0.2774 | 0.4358 | 0.0 | 0.7446 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5958 | 0.0 | 0.0 | 0.6499 | 0.0 | 0.3286 | 0.3315 | 0.0 | nan | 0.0 | 0.2465 | 0.0 | 0.0 | 0.7906 | 0.6857 | 0.9119 | 0.0 | 0.0 | 0.2354 | 0.0 |
| 0.4924 | 39.0 | 4173 | 0.6366 | 0.2676 | 0.3353 | 0.8139 | nan | 0.8348 | 0.8993 | 0.1203 | 0.8621 | 0.2236 | nan | 0.4680 | 0.7511 | 0.0 | 0.9173 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7338 | 0.0 | 0.0 | 0.8951 | 0.0 | 0.4269 | 0.3728 | 0.0 | nan | 0.0 | 0.3840 | 0.0 | 0.0 | 0.8954 | 0.8431 | 0.9483 | 0.0 | 0.0 | 0.1521 | 0.0 | nan | 0.7017 | 0.8287 | 0.0947 | 0.6259 | 0.1764 | nan | 0.2719 | 0.4769 | 0.0 | 0.7039 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5897 | 0.0 | 0.0 | 0.6428 | 0.0 | 0.3148 | 0.3347 | 0.0 | nan | 0.0 | 0.2526 | 0.0 | 0.0 | 0.8029 | 0.7084 | 0.9057 | 0.0 | 0.0 | 0.1322 | 0.0 |
| 0.4888 | 40.0 | 4280 | 0.6359 | 0.2700 | 0.3366 | 0.8159 | nan | 0.7028 | 0.9550 | 0.1329 | 0.8665 | 0.2352 | nan | 0.4666 | 0.7618 | 0.0 | 0.9236 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7581 | 0.0 | 0.0 | 0.8869 | 0.0 | 0.4960 | 0.4000 | 0.0 | nan | 0.0 | 0.3841 | 0.0 | 0.0 | 0.8872 | 0.7911 | 0.9617 | 0.0 | 0.0 | 0.1630 | 0.0 | nan | 0.6340 | 0.8422 | 0.0921 | 0.6435 | 0.1872 | nan | 0.3106 | 0.4853 | 0.0 | 0.7116 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6024 | 0.0 | 0.0 | 0.6476 | 0.0 | 0.3429 | 0.3344 | 0.0 | nan | 0.0 | 0.2610 | 0.0 | 0.0 | 0.8079 | 0.6908 | 0.9049 | 0.0 | 0.0 | 0.1404 | 0.0 |
| 0.483 | 41.0 | 4387 | 0.6366 | 0.2693 | 0.3406 | 0.8126 | nan | 0.7171 | 0.9324 | 0.1344 | 0.8655 | 0.2242 | nan | 0.4717 | 0.7440 | 0.0 | 0.9005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7429 | 0.0 | 0.0 | 0.8466 | 0.0 | 0.6028 | 0.3963 | 0.0 | nan | 0.0 | 0.3660 | 0.0 | 0.0 | 0.8904 | 0.8841 | 0.9521 | 0.0 | 0.0 | 0.2270 | 0.0 | nan | 0.6429 | 0.8377 | 0.0920 | 0.6265 | 0.1774 | nan | 0.3087 | 0.4640 | 0.0 | 0.7265 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5897 | 0.0 | 0.0 | 0.6584 | 0.0 | 0.3360 | 0.3145 | 0.0 | nan | 0.0 | 0.2553 | 0.0 | 0.0 | 0.7999 | 0.6916 | 0.9112 | 0.0 | 0.0 | 0.1841 | 0.0 |
| 0.5009 | 42.0 | 4494 | 0.6258 | 0.2710 | 0.3339 | 0.8183 | nan | 0.7555 | 0.9483 | 0.1454 | 0.8212 | 0.1967 | nan | 0.4535 | 0.6912 | 0.0 | 0.8826 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7402 | 0.0 | 0.0 | 0.8792 | 0.0 | 0.5108 | 0.4389 | 0.0 | nan | 0.0 | 0.3686 | 0.0 | 0.0 | 0.9180 | 0.7769 | 0.9484 | 0.0 | 0.0 | 0.2080 | 0.0 | nan | 0.6616 | 0.8477 | 0.1026 | 0.6467 | 0.1621 | nan | 0.3080 | 0.4639 | 0.0 | 0.7380 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6009 | 0.0 | 0.0 | 0.6392 | 0.0 | 0.3440 | 0.3440 | 0.0 | nan | 0.0 | 0.2535 | 0.0 | 0.0 | 0.7983 | 0.6804 | 0.9080 | 0.0 | 0.0 | 0.1744 | 0.0 |
| 0.4219 | 43.0 | 4601 | 0.6288 | 0.2719 | 0.3438 | 0.8167 | nan | 0.7323 | 0.9418 | 0.1150 | 0.7904 | 0.3300 | nan | 0.5284 | 0.8029 | 0.0 | 0.9075 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7436 | 0.0 | 0.0 | 0.8522 | 0.0 | 0.5399 | 0.4230 | 0.0 | nan | 0.0 | 0.3909 | 0.0 | 0.0 | 0.8917 | 0.8602 | 0.9687 | 0.0 | 0.0000 | 0.1831 | 0.0 | nan | 0.6487 | 0.8395 | 0.0855 | 0.6434 | 0.2450 | nan | 0.3106 | 0.4472 | 0.0 | 0.7469 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5633 | 0.0 | 0.0 | 0.6472 | 0.0 | 0.3279 | 0.3462 | 0.0 | nan | 0.0 | 0.2604 | 0.0 | 0.0 | 0.8074 | 0.7212 | 0.9040 | 0.0 | 0.0000 | 0.1569 | 0.0 |
| 0.4786 | 44.0 | 4708 | 0.6198 | 0.2760 | 0.3402 | 0.8214 | nan | 0.7436 | 0.9499 | 0.1392 | 0.7792 | 0.3171 | nan | 0.5137 | 0.7376 | 0.0 | 0.9098 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7106 | 0.0 | 0.0 | 0.8936 | 0.0 | 0.4527 | 0.4079 | 0.0 | nan | 0.0 | 0.3822 | 0.0 | 0.0 | 0.8944 | 0.8624 | 0.9497 | 0.0 | 0.0 | 0.2424 | 0.0 | nan | 0.6681 | 0.8415 | 0.0922 | 0.6642 | 0.2266 | nan | 0.3429 | 0.4575 | 0.0 | 0.7339 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5831 | 0.0 | 0.0 | 0.6422 | 0.0 | 0.3334 | 0.3469 | 0.0 | nan | 0.0 | 0.2604 | 0.0 | 0.0 | 0.8106 | 0.7247 | 0.9118 | 0.0 | 0.0 | 0.1917 | 0.0 |
| 0.4707 | 45.0 | 4815 | 0.6180 | 0.2747 | 0.3451 | 0.8199 | nan | 0.7173 | 0.9483 | 0.1182 | 0.8281 | 0.3361 | nan | 0.5437 | 0.7921 | 0.0 | 0.8841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7553 | 0.0 | 0.0 | 0.8626 | 0.0 | 0.4548 | 0.4455 | 0.0 | nan | 0.0 | 0.3826 | 0.0 | 0.0 | 0.9011 | 0.8459 | 0.9673 | 0.0 | 0.0001 | 0.2609 | 0.0 | nan | 0.6556 | 0.8428 | 0.0841 | 0.6664 | 0.2449 | nan | 0.3186 | 0.4486 | 0.0 | 0.7511 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5700 | 0.0 | 0.0 | 0.6577 | 0.0 | 0.3207 | 0.3424 | 0.0 | nan | 0.0 | 0.2569 | 0.0 | 0.0 | 0.8038 | 0.7080 | 0.9087 | 0.0 | 0.0001 | 0.2085 | 0.0 |
| 0.4779 | 46.0 | 4922 | 0.6370 | 0.2721 | 0.3407 | 0.8173 | nan | 0.7184 | 0.9500 | 0.1183 | 0.8408 | 0.2093 | nan | 0.4735 | 0.7918 | 0.0 | 0.9066 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7902 | 0.0 | 0.0 | 0.8807 | 0.0 | 0.5252 | 0.3672 | 0.0 | nan | 0.0 | 0.3848 | 0.0 | 0.0 | 0.8909 | 0.8341 | 0.9619 | 0.0 | 0.0 | 0.2590 | 0.0 | nan | 0.6536 | 0.8352 | 0.0857 | 0.6622 | 0.1713 | nan | 0.2987 | 0.4503 | 0.0 | 0.7568 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5968 | 0.0 | 0.0 | 0.6392 | 0.0 | 0.3366 | 0.3266 | 0.0 | nan | 0.0 | 0.2634 | 0.0 | 0.0 | 0.8086 | 0.7051 | 0.9098 | 0.0 | 0.0 | 0.2081 | 0.0 |
| 0.4473 | 47.0 | 5029 | 0.6425 | 0.2713 | 0.3387 | 0.8133 | nan | 0.6994 | 0.9423 | 0.0690 | 0.8474 | 0.2233 | nan | 0.4301 | 0.7275 | 0.0 | 0.9012 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8387 | 0.0 | 0.0 | 0.8716 | 0.0 | 0.4686 | 0.4532 | 0.0 | nan | 0.0 | 0.4030 | 0.0 | 0.0 | 0.9117 | 0.8222 | 0.9505 | 0.0 | 0.0064 | 0.2731 | 0.0 | nan | 0.6344 | 0.8294 | 0.0586 | 0.5998 | 0.1831 | nan | 0.3108 | 0.4896 | 0.0 | 0.7499 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6183 | 0.0 | 0.0 | 0.6457 | 0.0 | 0.3075 | 0.3404 | 0.0 | nan | 0.0 | 0.2601 | 0.0 | 0.0 | 0.8108 | 0.7115 | 0.9146 | 0.0 | 0.0064 | 0.2109 | 0.0 |
| 0.473 | 48.0 | 5136 | 0.6422 | 0.2697 | 0.3360 | 0.8112 | nan | 0.6854 | 0.9420 | 0.1309 | 0.8273 | 0.2546 | nan | 0.5640 | 0.7386 | 0.0 | 0.9125 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7923 | 0.0 | 0.0 | 0.8816 | 0.0 | 0.4047 | 0.3684 | 0.0 | nan | 0.0 | 0.3528 | 0.0 | 0.0 | 0.9236 | 0.7482 | 0.9633 | 0.0 | 0.0029 | 0.2587 | 0.0 | nan | 0.6186 | 0.8410 | 0.0933 | 0.6505 | 0.2030 | nan | 0.3017 | 0.4866 | 0.0 | 0.7350 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6182 | 0.0 | 0.0 | 0.6326 | 0.0 | 0.2819 | 0.3208 | 0.0 | nan | 0.0 | 0.2567 | 0.0 | 0.0 | 0.7975 | 0.6717 | 0.9122 | 0.0 | 0.0029 | 0.2051 | 0.0 |
| 0.4241 | 49.0 | 5243 | 0.6255 | 0.2737 | 0.3387 | 0.8166 | nan | 0.7375 | 0.9367 | 0.1313 | 0.8636 | 0.2763 | nan | 0.5443 | 0.7194 | 0.0 | 0.8946 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7436 | 0.0 | 0.0 | 0.8931 | 0.0 | 0.4677 | 0.3851 | 0.0 | nan | 0.0 | 0.3614 | 0.0 | 0.0 | 0.8977 | 0.7758 | 0.9657 | 0.0 | 0.0063 | 0.2369 | 0.0 | nan | 0.6546 | 0.8372 | 0.0888 | 0.6319 | 0.2170 | nan | 0.3243 | 0.4674 | 0.0 | 0.7569 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6013 | 0.0 | 0.0 | 0.6424 | 0.0 | 0.3288 | 0.3391 | 0.0 | nan | 0.0 | 0.2665 | 0.0 | 0.0 | 0.8066 | 0.6834 | 0.9051 | 0.0 | 0.0062 | 0.2002 | 0.0 |
| 0.4681 | 50.0 | 5350 | 0.6196 | 0.2712 | 0.3393 | 0.8204 | nan | 0.8128 | 0.9339 | 0.1273 | 0.8327 | 0.2242 | nan | 0.4888 | 0.7883 | 0.0 | 0.9409 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6982 | 0.0 | 0.0 | 0.8247 | 0.0 | 0.5636 | 0.3920 | 0.0 | nan | 0.0 | 0.3897 | 0.0 | 0.0 | 0.9191 | 0.7609 | 0.9612 | 0.0 | 0.0101 | 0.1876 | 0.0 | nan | 0.6913 | 0.8442 | 0.0932 | 0.6740 | 0.1778 | nan | 0.3092 | 0.4430 | 0.0 | 0.6827 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5736 | 0.0 | 0.0 | 0.6605 | 0.0 | 0.3690 | 0.3381 | 0.0 | nan | 0.0 | 0.2675 | 0.0 | 0.0 | 0.7983 | 0.6753 | 0.9113 | 0.0 | 0.0100 | 0.1585 | 0.0 |
| 0.4074 | 51.0 | 5457 | 0.6271 | 0.2775 | 0.3439 | 0.8195 | nan | 0.7294 | 0.9401 | 0.1326 | 0.8529 | 0.2439 | nan | 0.5318 | 0.7294 | 0.0 | 0.9195 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8128 | 0.0 | 0.0 | 0.8700 | 0.0 | 0.4981 | 0.3919 | 0.0 | nan | 0.0 | 0.3983 | 0.0 | 0.0 | 0.9130 | 0.8188 | 0.9577 | 0.0 | 0.0223 | 0.2417 | 0.0 | nan | 0.6655 | 0.8368 | 0.0844 | 0.6580 | 0.1969 | nan | 0.3341 | 0.4974 | 0.0 | 0.7287 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6328 | 0.0 | 0.0 | 0.6460 | 0.0 | 0.3449 | 0.3422 | 0.0 | nan | 0.0 | 0.2711 | 0.0 | 0.0 | 0.8063 | 0.7061 | 0.9139 | 0.0 | 0.0216 | 0.1940 | 0.0 |
| 0.4509 | 52.0 | 5564 | 0.6222 | 0.2796 | 0.3433 | 0.8223 | nan | 0.7406 | 0.9468 | 0.1266 | 0.8702 | 0.2352 | nan | 0.4488 | 0.6973 | 0.0 | 0.9181 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8190 | 0.0 | 0.0 | 0.8629 | 0.0 | 0.5565 | 0.4160 | 0.0 | nan | 0.0 | 0.3726 | 0.0 | 0.0 | 0.9052 | 0.8316 | 0.9539 | 0.0 | 0.0203 | 0.2652 | 0.0 | nan | 0.6646 | 0.8367 | 0.0931 | 0.6549 | 0.1878 | nan | 0.3129 | 0.4997 | 0.0 | 0.7531 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6511 | 0.0 | 0.0 | 0.6638 | 0.0 | 0.3531 | 0.3372 | 0.0 | nan | 0.0 | 0.2675 | 0.0 | 0.0 | 0.8122 | 0.7163 | 0.9154 | 0.0 | 0.0194 | 0.2096 | 0.0 |
| 0.4498 | 53.0 | 5671 | 0.6236 | 0.2758 | 0.3394 | 0.8230 | nan | 0.7541 | 0.9499 | 0.1196 | 0.7979 | 0.2387 | nan | 0.5362 | 0.7165 | 0.0 | 0.9071 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7450 | 0.0 | 0.0 | 0.8996 | 0.0 | 0.5641 | 0.3635 | 0.0 | nan | 0.0 | 0.3507 | 0.0 | 0.0 | 0.8941 | 0.8490 | 0.9589 | 0.0 | 0.0099 | 0.2058 | 0.0 | nan | 0.6657 | 0.8440 | 0.0951 | 0.6654 | 0.1853 | nan | 0.3408 | 0.4485 | 0.0 | 0.7598 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6050 | 0.0 | 0.0 | 0.6495 | 0.0 | 0.3570 | 0.3232 | 0.0 | nan | 0.0 | 0.2632 | 0.0 | 0.0 | 0.8128 | 0.7191 | 0.9078 | 0.0 | 0.0096 | 0.1741 | 0.0 |
| 0.4507 | 54.0 | 5778 | 0.6176 | 0.2770 | 0.3433 | 0.8211 | nan | 0.7416 | 0.9454 | 0.1223 | 0.8588 | 0.2360 | nan | 0.4892 | 0.7601 | 0.0 | 0.9185 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7776 | 0.0 | 0.0 | 0.8650 | 0.0 | 0.5613 | 0.4273 | 0.0 | nan | 0.0 | 0.4079 | 0.0 | 0.0 | 0.9153 | 0.7758 | 0.9553 | 0.0 | 0.0130 | 0.2160 | 0.0 | nan | 0.6602 | 0.8370 | 0.0957 | 0.6295 | 0.1915 | nan | 0.3314 | 0.4815 | 0.0 | 0.7392 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6269 | 0.0 | 0.0 | 0.6720 | 0.0 | 0.3648 | 0.3500 | 0.0 | nan | 0.0 | 0.2645 | 0.0 | 0.0 | 0.8121 | 0.7003 | 0.9127 | 0.0 | 0.0125 | 0.1808 | 0.0 |
| 0.4261 | 55.0 | 5885 | 0.6341 | 0.2781 | 0.3445 | 0.8214 | nan | 0.7109 | 0.9575 | 0.1100 | 0.8762 | 0.2139 | nan | 0.4148 | 0.7341 | 0.0 | 0.9027 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8385 | 0.0 | 0.0 | 0.8455 | 0.0 | 0.6064 | 0.3939 | 0.0 | nan | 0.0 | 0.4239 | 0.0 | 0.0 | 0.9049 | 0.8480 | 0.9505 | 0.0 | 0.0098 | 0.2812 | 0.0 | nan | 0.6436 | 0.8358 | 0.0899 | 0.6665 | 0.1782 | nan | 0.3080 | 0.4988 | 0.0 | 0.7586 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6378 | 0.0 | 0.0 | 0.6712 | 0.0 | 0.3576 | 0.3103 | 0.0 | nan | 0.0 | 0.2761 | 0.0 | 0.0 | 0.8068 | 0.7148 | 0.9155 | 0.0 | 0.0097 | 0.2208 | 0.0 |
| 0.4269 | 56.0 | 5992 | 0.6354 | 0.2800 | 0.3425 | 0.8229 | nan | 0.7174 | 0.9635 | 0.1316 | 0.8449 | 0.1980 | nan | 0.4801 | 0.7019 | 0.0 | 0.9220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7983 | 0.0 | 0.0 | 0.8332 | 0.0 | 0.5805 | 0.3871 | 0.0 | nan | 0.0 | 0.3880 | 0.0 | 0.0 | 0.9070 | 0.8425 | 0.9638 | 0.0 | 0.0057 | 0.2954 | 0.0 | nan | 0.6489 | 0.8346 | 0.0938 | 0.6909 | 0.1622 | nan | 0.3414 | 0.5041 | 0.0 | 0.7278 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6631 | 0.0 | 0.0 | 0.6693 | 0.0 | 0.3671 | 0.3103 | 0.0 | nan | 0.0 | 0.2686 | 0.0 | 0.0 | 0.8133 | 0.7224 | 0.9163 | 0.0 | 0.0055 | 0.2195 | 0.0 |
| 0.4169 | 57.0 | 6099 | 0.6053 | 0.2789 | 0.3414 | 0.8280 | nan | 0.7766 | 0.9558 | 0.1311 | 0.8292 | 0.2873 | nan | 0.4710 | 0.7584 | 0.0 | 0.9194 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7046 | 0.0 | 0.0 | 0.8549 | 0.0 | 0.5641 | 0.3717 | 0.0 | nan | 0.0 | 0.3584 | 0.0 | 0.0 | 0.9088 | 0.8278 | 0.9606 | 0.0 | 0.0052 | 0.2411 | 0.0 | nan | 0.6842 | 0.8509 | 0.0887 | 0.6997 | 0.2152 | nan | 0.3381 | 0.4711 | 0.0 | 0.7428 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5962 | 0.0 | 0.0 | 0.6637 | 0.0 | 0.3486 | 0.3136 | 0.0 | nan | 0.0 | 0.2619 | 0.0 | 0.0 | 0.8097 | 0.7202 | 0.9169 | 0.0 | 0.0051 | 0.1982 | 0.0 |
| 0.4203 | 58.0 | 6206 | 0.6204 | 0.2768 | 0.3469 | 0.8219 | nan | 0.7345 | 0.9382 | 0.1267 | 0.8723 | 0.2670 | nan | 0.5010 | 0.8043 | 0.0 | 0.9108 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7795 | 0.0 | 0.0 | 0.8756 | 0.0 | 0.5060 | 0.3917 | 0.0 | nan | 0.0 | 0.3940 | 0.0 | 0.0 | 0.9022 | 0.8630 | 0.9626 | 0.0 | 0.0147 | 0.2578 | 0.0 | nan | 0.6559 | 0.8447 | 0.0970 | 0.6231 | 0.2100 | nan | 0.3341 | 0.4423 | 0.0 | 0.7649 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6047 | 0.0 | 0.0 | 0.6631 | 0.0 | 0.3387 | 0.3427 | 0.0 | nan | 0.0 | 0.2643 | 0.0 | 0.0 | 0.8105 | 0.7240 | 0.9139 | 0.0 | 0.0143 | 0.2106 | 0.0 |
| 0.4258 | 59.0 | 6313 | 0.6419 | 0.2779 | 0.3458 | 0.8155 | nan | 0.6942 | 0.9363 | 0.1292 | 0.8829 | 0.2064 | nan | 0.5479 | 0.7177 | 0.0 | 0.9146 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8297 | 0.0 | 0.0 | 0.8539 | 0.0 | 0.5605 | 0.4169 | 0.0 | nan | 0.0 | 0.3946 | 0.0 | 0.0 | 0.9033 | 0.8518 | 0.9652 | 0.0 | 0.0151 | 0.2459 | 0.0 | nan | 0.6383 | 0.8254 | 0.0972 | 0.6315 | 0.1650 | nan | 0.3471 | 0.5105 | 0.0 | 0.7528 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6633 | 0.0 | 0.0 | 0.6610 | 0.0 | 0.3331 | 0.3427 | 0.0 | nan | 0.0 | 0.2635 | 0.0 | 0.0 | 0.8102 | 0.7257 | 0.9120 | 0.0 | 0.0143 | 0.1989 | 0.0 |
| 0.3939 | 60.0 | 6420 | 0.6190 | 0.2810 | 0.3480 | 0.8236 | nan | 0.7109 | 0.9550 | 0.1268 | 0.8720 | 0.2427 | nan | 0.5037 | 0.7789 | 0.0 | 0.9088 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7861 | 0.0 | 0.0 | 0.8841 | 0.0 | 0.5206 | 0.4294 | 0.0 | nan | 0.0 | 0.3945 | 0.0 | 0.0 | 0.8896 | 0.8466 | 0.9647 | 0.0 | 0.0142 | 0.3064 | 0.0 | nan | 0.6377 | 0.8446 | 0.0928 | 0.6636 | 0.1958 | nan | 0.3534 | 0.4888 | 0.0 | 0.7695 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6303 | 0.0 | 0.0 | 0.6599 | 0.0 | 0.3350 | 0.3416 | 0.0 | nan | 0.0 | 0.2695 | 0.0 | 0.0 | 0.8187 | 0.7265 | 0.9120 | 0.0 | 0.0139 | 0.2380 | 0.0 |
| 0.3992 | 61.0 | 6527 | 0.6259 | 0.2772 | 0.3441 | 0.8178 | nan | 0.7583 | 0.9240 | 0.1284 | 0.8714 | 0.2340 | nan | 0.5038 | 0.7764 | 0.0 | 0.9091 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7920 | 0.0 | 0.0 | 0.8740 | 0.0 | 0.5316 | 0.3637 | 0.0 | nan | 0.0 | 0.3819 | 0.0 | 0.0 | 0.9060 | 0.8316 | 0.9569 | 0.0 | 0.0077 | 0.2618 | 0.0 | nan | 0.6735 | 0.8256 | 0.0924 | 0.5960 | 0.1875 | nan | 0.3319 | 0.4965 | 0.0 | 0.7582 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6429 | 0.0 | 0.0 | 0.6716 | 0.0 | 0.3335 | 0.3229 | 0.0 | nan | 0.0 | 0.2728 | 0.0 | 0.0 | 0.8130 | 0.7181 | 0.9167 | 0.0 | 0.0076 | 0.2104 | 0.0 |
| 0.4129 | 62.0 | 6634 | 0.6260 | 0.2820 | 0.3511 | 0.8223 | nan | 0.7066 | 0.9535 | 0.1271 | 0.8465 | 0.2200 | nan | 0.5430 | 0.7777 | 0.0 | 0.9011 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8684 | 0.0 | 0.0 | 0.8707 | 0.0 | 0.5766 | 0.3864 | 0.0 | nan | 0.0 | 0.3736 | 0.0 | 0.0 | 0.8824 | 0.8947 | 0.9619 | 0.0 | 0.0059 | 0.3374 | 0.0 | nan | 0.6317 | 0.8436 | 0.0913 | 0.6718 | 0.1800 | nan | 0.3561 | 0.5209 | 0.0 | 0.7695 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6569 | 0.0 | 0.0 | 0.6685 | 0.0 | 0.3421 | 0.3253 | 0.0 | nan | 0.0 | 0.2687 | 0.0 | 0.0 | 0.8108 | 0.7197 | 0.9138 | 0.0 | 0.0058 | 0.2489 | 0.0 |
| 0.4115 | 63.0 | 6741 | 0.6140 | 0.2792 | 0.3499 | 0.8224 | nan | 0.7478 | 0.9367 | 0.1313 | 0.8520 | 0.2720 | nan | 0.5338 | 0.8185 | 0.0 | 0.9055 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7987 | 0.0 | 0.0 | 0.8555 | 0.0 | 0.5679 | 0.3952 | 0.0 | nan | 0.0 | 0.4042 | 0.0 | 0.0 | 0.9170 | 0.8134 | 0.9675 | 0.0 | 0.0131 | 0.2668 | 0.0 | nan | 0.6611 | 0.8430 | 0.0900 | 0.6619 | 0.2118 | nan | 0.3411 | 0.4575 | 0.0 | 0.7664 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6257 | 0.0 | 0.0 | 0.6674 | 0.0 | 0.3466 | 0.3267 | 0.0 | nan | 0.0 | 0.2734 | 0.0 | 0.0 | 0.8067 | 0.7131 | 0.9151 | 0.0 | 0.0127 | 0.2142 | 0.0 |
| 0.4007 | 64.0 | 6848 | 0.6226 | 0.2799 | 0.3478 | 0.8241 | nan | 0.7270 | 0.9499 | 0.1301 | 0.8634 | 0.2476 | nan | 0.5034 | 0.8017 | 0.0 | 0.9072 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8034 | 0.0 | 0.0 | 0.8629 | 0.0 | 0.5290 | 0.3805 | 0.0 | nan | 0.0 | 0.4671 | 0.0 | 0.0 | 0.9222 | 0.8110 | 0.9629 | 0.0 | 0.0131 | 0.2483 | 0.0 | nan | 0.6596 | 0.8455 | 0.0886 | 0.6685 | 0.1992 | nan | 0.3472 | 0.4826 | 0.0 | 0.7668 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6407 | 0.0 | 0.0 | 0.6656 | 0.0 | 0.3446 | 0.3243 | 0.0 | nan | 0.0 | 0.2835 | 0.0 | 0.0 | 0.8060 | 0.7012 | 0.9171 | 0.0 | 0.0128 | 0.2020 | 0.0 |
| 0.3729 | 65.0 | 6955 | 0.6277 | 0.2805 | 0.3501 | 0.8175 | nan | 0.7535 | 0.9178 | 0.1297 | 0.8744 | 0.2713 | nan | 0.5176 | 0.7654 | 0.0 | 0.9066 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8650 | 0.0 | 0.0 | 0.8636 | 0.0 | 0.5904 | 0.3873 | 0.0 | nan | 0.0 | 0.4093 | 0.0 | 0.0 | 0.9034 | 0.8424 | 0.9667 | 0.0 | 0.0238 | 0.2161 | 0.0 | nan | 0.6692 | 0.8260 | 0.0898 | 0.5885 | 0.2116 | nan | 0.3469 | 0.5326 | 0.0 | 0.7594 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6756 | 0.0 | 0.0 | 0.6697 | 0.0 | 0.3490 | 0.3297 | 0.0 | nan | 0.0 | 0.2702 | 0.0 | 0.0 | 0.8161 | 0.7236 | 0.9122 | 0.0 | 0.0231 | 0.1842 | 0.0 |
| 0.4057 | 66.0 | 7062 | 0.6330 | 0.2800 | 0.3502 | 0.8214 | nan | 0.7551 | 0.9304 | 0.1350 | 0.8568 | 0.2164 | nan | 0.5517 | 0.7512 | 0.0 | 0.9266 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8694 | 0.0 | 0.0 | 0.8600 | 0.0 | 0.5469 | 0.4128 | 0.0 | nan | 0.0 | 0.4283 | 0.0 | 0.0 | 0.9052 | 0.8578 | 0.9652 | 0.0 | 0.0115 | 0.2263 | 0.0 | nan | 0.6654 | 0.8419 | 0.0868 | 0.6542 | 0.1744 | nan | 0.3281 | 0.5290 | 0.0 | 0.7317 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6670 | 0.0 | 0.0 | 0.6642 | 0.0 | 0.3528 | 0.3423 | 0.0 | nan | 0.0 | 0.2696 | 0.0 | 0.0 | 0.8157 | 0.7269 | 0.9145 | 0.0 | 0.0111 | 0.1858 | 0.0 |
| 0.4369 | 67.0 | 7169 | 0.6357 | 0.2824 | 0.3525 | 0.8224 | nan | 0.7476 | 0.9330 | 0.1332 | 0.8828 | 0.2109 | nan | 0.4922 | 0.7664 | 0.0 | 0.8911 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8781 | 0.0 | 0.0 | 0.8555 | 0.0 | 0.5624 | 0.4589 | 0.0 | nan | 0.0 | 0.4195 | 0.0 | 0.0 | 0.9089 | 0.8686 | 0.9586 | 0.0 | 0.0182 | 0.2932 | 0.0 | nan | 0.6625 | 0.8404 | 0.0855 | 0.6255 | 0.1733 | nan | 0.3351 | 0.5265 | 0.0 | 0.7617 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6604 | 0.0 | 0.0 | 0.6802 | 0.0 | 0.3689 | 0.3586 | 0.0 | nan | 0.0 | 0.2648 | 0.0 | 0.0 | 0.8099 | 0.7189 | 0.9187 | 0.0 | 0.0171 | 0.2292 | 0.0 |
| 0.3892 | 68.0 | 7276 | 0.6167 | 0.2833 | 0.3528 | 0.8258 | nan | 0.7860 | 0.9329 | 0.1332 | 0.8555 | 0.2064 | nan | 0.5435 | 0.7913 | 0.0 | 0.9146 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8599 | 0.0 | 0.0 | 0.8568 | 0.0 | 0.5659 | 0.3951 | 0.0 | nan | 0.0 | 0.4415 | 0.0 | 0.0 | 0.9093 | 0.8528 | 0.9608 | 0.0 | 0.0204 | 0.2654 | 0.0 | nan | 0.6838 | 0.8452 | 0.0888 | 0.6523 | 0.1692 | nan | 0.3467 | 0.5246 | 0.0 | 0.7558 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6640 | 0.0 | 0.0 | 0.6786 | 0.0 | 0.3606 | 0.3320 | 0.0 | nan | 0.0 | 0.2839 | 0.0 | 0.0 | 0.8121 | 0.7187 | 0.9186 | 0.0 | 0.0194 | 0.2098 | 0.0 |
| 0.3962 | 69.0 | 7383 | 0.6167 | 0.2808 | 0.3513 | 0.8262 | nan | 0.7777 | 0.9410 | 0.1316 | 0.8411 | 0.2312 | nan | 0.5449 | 0.8178 | 0.0 | 0.9141 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8272 | 0.0 | 0.0 | 0.8759 | 0.0 | 0.5513 | 0.3886 | 0.0 | nan | 0.0 | 0.4216 | 0.0 | 0.0 | 0.8981 | 0.8375 | 0.9663 | 0.0000 | 0.0144 | 0.2604 | 0.0 | nan | 0.6825 | 0.8491 | 0.0916 | 0.6636 | 0.1848 | nan | 0.3580 | 0.4910 | 0.0 | 0.7574 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6304 | 0.0 | 0.0 | 0.6691 | 0.0 | 0.3443 | 0.3262 | 0.0 | nan | 0.0 | 0.2676 | 0.0 | 0.0 | 0.8152 | 0.7171 | 0.9170 | 0.0000 | 0.0138 | 0.2060 | 0.0 |
| 0.3941 | 70.0 | 7490 | 0.6295 | 0.2814 | 0.3482 | 0.8248 | nan | 0.7540 | 0.9446 | 0.1261 | 0.8654 | 0.1788 | nan | 0.5286 | 0.7921 | 0.0 | 0.9087 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8541 | 0.0 | 0.0 | 0.8799 | 0.0 | 0.5621 | 0.3841 | 0.0 | nan | 0.0 | 0.3876 | 0.0 | 0.0 | 0.9026 | 0.8420 | 0.9602 | 0.0000 | 0.0127 | 0.2591 | 0.0 | nan | 0.6649 | 0.8426 | 0.0909 | 0.6614 | 0.1472 | nan | 0.3625 | 0.5344 | 0.0 | 0.7587 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6551 | 0.0 | 0.0 | 0.6738 | 0.0 | 0.3419 | 0.3251 | 0.0 | nan | 0.0 | 0.2699 | 0.0 | 0.0 | 0.8157 | 0.7237 | 0.9189 | 0.0000 | 0.0123 | 0.2063 | 0.0 |
| 0.3723 | 71.0 | 7597 | 0.6136 | 0.2851 | 0.3555 | 0.8245 | nan | 0.7657 | 0.9351 | 0.1292 | 0.8615 | 0.2372 | nan | 0.5278 | 0.7931 | 0.0 | 0.9092 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8857 | 0.0 | 0.0 | 0.8408 | 0.0 | 0.5757 | 0.4549 | 0.0 | nan | 0.0 | 0.4165 | 0.0 | 0.0 | 0.9103 | 0.8393 | 0.9636 | 0.0004 | 0.0229 | 0.3081 | 0.0 | nan | 0.6760 | 0.8398 | 0.0924 | 0.6518 | 0.1897 | nan | 0.3505 | 0.5348 | 0.0 | 0.7571 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6611 | 0.0 | 0.0 | 0.6755 | 0.0 | 0.3646 | 0.3619 | 0.0 | nan | 0.0 | 0.2733 | 0.0 | 0.0 | 0.8090 | 0.7150 | 0.9190 | 0.0004 | 0.0211 | 0.2305 | 0.0 |
| 0.3479 | 72.0 | 7704 | 0.6061 | 0.2884 | 0.3557 | 0.8325 | nan | 0.7936 | 0.9508 | 0.1315 | 0.8549 | 0.2708 | nan | 0.5505 | 0.8127 | 0.0 | 0.9252 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8511 | 0.0 | 0.0 | 0.8386 | 0.0 | 0.4955 | 0.4688 | 0.0 | nan | 0.0 | 0.4257 | 0.0 | 0.0 | 0.9102 | 0.8361 | 0.9647 | 0.0 | 0.0295 | 0.2733 | 0.0 | nan | 0.6928 | 0.8570 | 0.0867 | 0.6919 | 0.2109 | nan | 0.3833 | 0.5276 | 0.0 | 0.7488 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6738 | 0.0 | 0.0 | 0.6799 | 0.0 | 0.3550 | 0.3680 | 0.0 | nan | 0.0 | 0.2631 | 0.0 | 0.0 | 0.8084 | 0.7220 | 0.9201 | 0.0 | 0.0269 | 0.2136 | 0.0 |
| 0.3855 | 73.0 | 7811 | 0.6140 | 0.2844 | 0.3517 | 0.8280 | nan | 0.7850 | 0.9335 | 0.1308 | 0.8746 | 0.2526 | nan | 0.5565 | 0.8057 | 0.0 | 0.9215 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8211 | 0.0 | 0.0 | 0.8850 | 0.0 | 0.4633 | 0.4599 | 0.0 | nan | 0.0 | 0.3996 | 0.0 | 0.0 | 0.9129 | 0.8173 | 0.9627 | 0.0 | 0.0262 | 0.2472 | 0.0 | nan | 0.6876 | 0.8485 | 0.0883 | 0.6613 | 0.2008 | nan | 0.3733 | 0.5156 | 0.0 | 0.7579 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6635 | 0.0 | 0.0 | 0.6722 | 0.0 | 0.3407 | 0.3598 | 0.0 | nan | 0.0 | 0.2652 | 0.0 | 0.0 | 0.8112 | 0.7110 | 0.9183 | 0.0 | 0.0236 | 0.2031 | 0.0 |
| 0.3729 | 74.0 | 7918 | 0.6198 | 0.2842 | 0.3507 | 0.8236 | nan | 0.7829 | 0.9307 | 0.1299 | 0.8662 | 0.2433 | nan | 0.5390 | 0.7845 | 0.0 | 0.9173 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8498 | 0.0 | 0.0 | 0.8569 | 0.0 | 0.5180 | 0.4716 | 0.0 | nan | 0.0 | 0.3838 | 0.0 | 0.0 | 0.9181 | 0.7794 | 0.9605 | 0.0 | 0.0255 | 0.2639 | 0.0 | nan | 0.6945 | 0.8309 | 0.0913 | 0.6524 | 0.1930 | nan | 0.3617 | 0.5286 | 0.0 | 0.7612 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6807 | 0.0 | 0.0 | 0.6696 | 0.0 | 0.3450 | 0.3662 | 0.0 | nan | 0.0 | 0.2663 | 0.0 | 0.0 | 0.8067 | 0.6917 | 0.9190 | 0.0 | 0.0232 | 0.2136 | 0.0 |
| 0.3847 | 75.0 | 8025 | 0.6102 | 0.2872 | 0.3542 | 0.8295 | nan | 0.7547 | 0.9536 | 0.1305 | 0.8593 | 0.2643 | nan | 0.5444 | 0.7866 | 0.0 | 0.9128 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8692 | 0.0 | 0.0 | 0.8641 | 0.0 | 0.5149 | 0.4767 | 0.0 | nan | 0.0 | 0.3996 | 0.0 | 0.0 | 0.9072 | 0.8167 | 0.9652 | 0.0 | 0.0273 | 0.2872 | 0.0 | nan | 0.6746 | 0.8521 | 0.0899 | 0.6848 | 0.2061 | nan | 0.3671 | 0.5234 | 0.0 | 0.7691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6744 | 0.0 | 0.0 | 0.6762 | 0.0 | 0.3446 | 0.3628 | 0.0 | nan | 0.0 | 0.2719 | 0.0 | 0.0 | 0.8109 | 0.7111 | 0.9194 | 0.0 | 0.0241 | 0.2272 | 0.0 |
| 0.3747 | 76.0 | 8132 | 0.6221 | 0.2855 | 0.3530 | 0.8264 | nan | 0.7744 | 0.9357 | 0.1332 | 0.8646 | 0.2509 | nan | 0.5133 | 0.7699 | 0.0 | 0.9257 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8628 | 0.0 | 0.0 | 0.8489 | 0.0 | 0.5518 | 0.4277 | 0.0 | nan | 0.0 | 0.4239 | 0.0 | 0.0 | 0.9115 | 0.8425 | 0.9679 | 0.0 | 0.0170 | 0.2731 | 0.0 | nan | 0.6871 | 0.8423 | 0.0887 | 0.6448 | 0.1990 | nan | 0.3666 | 0.5301 | 0.0 | 0.7518 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6831 | 0.0 | 0.0 | 0.6731 | 0.0 | 0.3554 | 0.3631 | 0.0 | nan | 0.0 | 0.2731 | 0.0 | 0.0 | 0.8106 | 0.7180 | 0.9189 | 0.0 | 0.0155 | 0.2154 | 0.0 |
| 0.393 | 77.0 | 8239 | 0.6188 | 0.2845 | 0.3524 | 0.8246 | nan | 0.7827 | 0.9299 | 0.1315 | 0.8704 | 0.2537 | nan | 0.5172 | 0.7217 | 0.0 | 0.9238 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8737 | 0.0 | 0.0 | 0.8465 | 0.0 | 0.5902 | 0.4841 | 0.0 | nan | 0.0 | 0.3912 | 0.0 | 0.0 | 0.9111 | 0.8047 | 0.9652 | 0.0 | 0.0183 | 0.2607 | 0.0 | nan | 0.6816 | 0.8433 | 0.0911 | 0.6407 | 0.1997 | nan | 0.3598 | 0.5258 | 0.0 | 0.7555 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6891 | 0.0 | 0.0 | 0.6751 | 0.0 | 0.3515 | 0.3614 | 0.0 | nan | 0.0 | 0.2679 | 0.0 | 0.0 | 0.8116 | 0.7075 | 0.9187 | 0.0 | 0.0161 | 0.2081 | 0.0 |
| 0.3503 | 78.0 | 8346 | 0.6216 | 0.2862 | 0.3506 | 0.8283 | nan | 0.7554 | 0.9490 | 0.1308 | 0.8747 | 0.2635 | nan | 0.5136 | 0.7380 | 0.0 | 0.9021 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8697 | 0.0 | 0.0 | 0.8639 | 0.0 | 0.5498 | 0.4507 | 0.0 | nan | 0.0 | 0.3723 | 0.0 | 0.0 | 0.9130 | 0.8144 | 0.9669 | 0.0000 | 0.0202 | 0.2718 | 0.0 | nan | 0.6704 | 0.8499 | 0.0876 | 0.6808 | 0.2080 | nan | 0.3576 | 0.5340 | 0.0 | 0.7722 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6799 | 0.0 | 0.0 | 0.6734 | 0.0 | 0.3529 | 0.3562 | 0.0 | nan | 0.0 | 0.2639 | 0.0 | 0.0 | 0.8091 | 0.7083 | 0.9194 | 0.0000 | 0.0185 | 0.2176 | 0.0 |
| 0.3389 | 79.0 | 8453 | 0.6086 | 0.2873 | 0.3536 | 0.8317 | nan | 0.7849 | 0.9467 | 0.1317 | 0.8604 | 0.2798 | nan | 0.5327 | 0.7801 | 0.0 | 0.9223 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8640 | 0.0 | 0.0 | 0.8674 | 0.0 | 0.5225 | 0.4534 | 0.0 | nan | 0.0 | 0.4022 | 0.0 | 0.0 | 0.9105 | 0.8236 | 0.9628 | 0.0 | 0.0203 | 0.2504 | 0.0 | nan | 0.6862 | 0.8560 | 0.0930 | 0.6832 | 0.2170 | nan | 0.3653 | 0.5196 | 0.0 | 0.7605 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6713 | 0.0 | 0.0 | 0.6743 | 0.0 | 0.3523 | 0.3645 | 0.0 | nan | 0.0 | 0.2715 | 0.0 | 0.0 | 0.8142 | 0.7199 | 0.9201 | 0.0 | 0.0189 | 0.2045 | 0.0 |
| 0.3585 | 80.0 | 8560 | 0.6063 | 0.2864 | 0.3520 | 0.8318 | nan | 0.7764 | 0.9462 | 0.1291 | 0.8645 | 0.2672 | nan | 0.5498 | 0.7815 | 0.0 | 0.9163 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8444 | 0.0 | 0.0 | 0.8734 | 0.0 | 0.5129 | 0.4308 | 0.0 | nan | 0.0 | 0.3917 | 0.0 | 0.0 | 0.9168 | 0.8376 | 0.9623 | 0.0 | 0.0157 | 0.2467 | 0.0 | nan | 0.6799 | 0.8577 | 0.0902 | 0.6946 | 0.2078 | nan | 0.3653 | 0.5092 | 0.0 | 0.7677 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6630 | 0.0 | 0.0 | 0.6704 | 0.0 | 0.3574 | 0.3582 | 0.0 | nan | 0.0 | 0.2689 | 0.0 | 0.0 | 0.8115 | 0.7248 | 0.9214 | 0.0 | 0.0149 | 0.2031 | 0.0 |
| 0.3316 | 81.0 | 8667 | 0.6237 | 0.2846 | 0.3499 | 0.8292 | nan | 0.7771 | 0.9554 | 0.1310 | 0.8561 | 0.2328 | nan | 0.5116 | 0.8077 | 0.0 | 0.9220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8567 | 0.0 | 0.0 | 0.8447 | 0.0 | 0.5184 | 0.4265 | 0.0 | nan | 0.0 | 0.3936 | 0.0 | 0.0 | 0.9163 | 0.7947 | 0.9616 | 0.0 | 0.0194 | 0.2725 | 0.0 | nan | 0.6830 | 0.8478 | 0.0919 | 0.6921 | 0.1856 | nan | 0.3587 | 0.5077 | 0.0 | 0.7572 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6634 | 0.0 | 0.0 | 0.6682 | 0.0 | 0.3565 | 0.3552 | 0.0 | nan | 0.0 | 0.2675 | 0.0 | 0.0 | 0.8096 | 0.7085 | 0.9218 | 0.0 | 0.0182 | 0.2152 | 0.0 |
| 0.37 | 82.0 | 8774 | 0.6196 | 0.2852 | 0.3505 | 0.8287 | nan | 0.7762 | 0.9494 | 0.1284 | 0.8690 | 0.2293 | nan | 0.4902 | 0.7878 | 0.0 | 0.9015 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8731 | 0.0 | 0.0 | 0.8775 | 0.0 | 0.5446 | 0.4245 | 0.0 | nan | 0.0 | 0.3999 | 0.0 | 0.0 | 0.9022 | 0.8158 | 0.9637 | 0.0000 | 0.0177 | 0.2668 | 0.0 | nan | 0.6814 | 0.8447 | 0.0936 | 0.6709 | 0.1840 | nan | 0.3540 | 0.5202 | 0.0 | 0.7798 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6653 | 0.0 | 0.0 | 0.6719 | 0.0 | 0.3483 | 0.3534 | 0.0 | nan | 0.0 | 0.2708 | 0.0 | 0.0 | 0.8169 | 0.7155 | 0.9201 | 0.0000 | 0.0167 | 0.2179 | 0.0 |
| 0.3627 | 83.0 | 8881 | 0.6121 | 0.2864 | 0.3502 | 0.8309 | nan | 0.7826 | 0.9457 | 0.1303 | 0.8636 | 0.2628 | nan | 0.5182 | 0.7617 | 0.0 | 0.9169 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8484 | 0.0 | 0.0 | 0.8981 | 0.0 | 0.5051 | 0.4155 | 0.0 | nan | 0.0 | 0.3909 | 0.0 | 0.0 | 0.9009 | 0.8326 | 0.9629 | 0.0 | 0.0204 | 0.2512 | 0.0 | nan | 0.6893 | 0.8503 | 0.0938 | 0.6786 | 0.2084 | nan | 0.3700 | 0.5230 | 0.0 | 0.7683 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6699 | 0.0 | 0.0 | 0.6627 | 0.0 | 0.3504 | 0.3488 | 0.0 | nan | 0.0 | 0.2671 | 0.0 | 0.0 | 0.8173 | 0.7240 | 0.9195 | 0.0 | 0.0191 | 0.2055 | 0.0 |
| 0.3548 | 84.0 | 8988 | 0.6255 | 0.2871 | 0.3508 | 0.8293 | nan | 0.7457 | 0.9559 | 0.1291 | 0.8691 | 0.2532 | nan | 0.5187 | 0.7411 | 0.0 | 0.9188 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8670 | 0.0 | 0.0 | 0.8565 | 0.0 | 0.5686 | 0.4341 | 0.0 | nan | 0.0 | 0.3870 | 0.0 | 0.0 | 0.9185 | 0.8038 | 0.9562 | 0.0 | 0.0193 | 0.2832 | 0.0 | nan | 0.6752 | 0.8472 | 0.0924 | 0.6852 | 0.2021 | nan | 0.3671 | 0.5311 | 0.0 | 0.7628 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6838 | 0.0 | 0.0 | 0.6761 | 0.0 | 0.3590 | 0.3523 | 0.0 | nan | 0.0 | 0.2738 | 0.0 | 0.0 | 0.8100 | 0.7060 | 0.9238 | 0.0 | 0.0179 | 0.2213 | 0.0 |
| 0.3724 | 85.0 | 9095 | 0.6204 | 0.2872 | 0.3517 | 0.8294 | nan | 0.7477 | 0.9552 | 0.1310 | 0.8738 | 0.2764 | nan | 0.5233 | 0.7399 | 0.0 | 0.9121 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8842 | 0.0 | 0.0 | 0.8752 | 0.0 | 0.5314 | 0.4354 | 0.0 | nan | 0.0 | 0.3917 | 0.0 | 0.0 | 0.9046 | 0.8095 | 0.9608 | 0.0 | 0.0245 | 0.2773 | 0.0 | nan | 0.6719 | 0.8498 | 0.0929 | 0.6746 | 0.2173 | nan | 0.3705 | 0.5351 | 0.0 | 0.7717 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6752 | 0.0 | 0.0 | 0.6721 | 0.0 | 0.3498 | 0.3548 | 0.0 | nan | 0.0 | 0.2703 | 0.0 | 0.0 | 0.8144 | 0.7084 | 0.9218 | 0.0 | 0.0218 | 0.2181 | 0.0 |
| 0.3412 | 86.0 | 9202 | 0.6206 | 0.2865 | 0.3530 | 0.8286 | nan | 0.7376 | 0.9578 | 0.1302 | 0.8737 | 0.2390 | nan | 0.5286 | 0.7728 | 0.0 | 0.9213 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8872 | 0.0 | 0.0 | 0.8562 | 0.0 | 0.5362 | 0.4489 | 0.0 | nan | 0.0 | 0.4215 | 0.0 | 0.0 | 0.9120 | 0.8058 | 0.9629 | 0.0003 | 0.0238 | 0.2789 | 0.0 | nan | 0.6623 | 0.8475 | 0.0928 | 0.6955 | 0.1920 | nan | 0.3685 | 0.5297 | 0.0 | 0.7654 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6686 | 0.0 | 0.0 | 0.6750 | 0.0 | 0.3522 | 0.3603 | 0.0 | nan | 0.0 | 0.2744 | 0.0 | 0.0 | 0.8128 | 0.7070 | 0.9232 | 0.0003 | 0.0215 | 0.2182 | 0.0 |
| 0.3816 | 87.0 | 9309 | 0.6182 | 0.2885 | 0.3576 | 0.8305 | nan | 0.7658 | 0.9495 | 0.1319 | 0.8699 | 0.2584 | nan | 0.5379 | 0.7819 | 0.0 | 0.9261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8793 | 0.0 | 0.0 | 0.8375 | 0.0 | 0.5918 | 0.4508 | 0.0 | nan | 0.0 | 0.4501 | 0.0 | 0.0 | 0.9060 | 0.8429 | 0.9638 | 0.0 | 0.0206 | 0.2781 | 0.0 | nan | 0.6797 | 0.8510 | 0.0925 | 0.6860 | 0.2044 | nan | 0.3727 | 0.5308 | 0.0 | 0.7534 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6776 | 0.0 | 0.0 | 0.6817 | 0.0 | 0.3627 | 0.3565 | 0.0 | nan | 0.0 | 0.2872 | 0.0 | 0.0 | 0.8167 | 0.7217 | 0.9234 | 0.0 | 0.0186 | 0.2152 | 0.0 |
| 0.3451 | 88.0 | 9416 | 0.6192 | 0.2869 | 0.3519 | 0.8302 | nan | 0.7577 | 0.9534 | 0.1308 | 0.8708 | 0.2657 | nan | 0.5258 | 0.7680 | 0.0 | 0.9224 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8636 | 0.0 | 0.0 | 0.8602 | 0.0 | 0.5584 | 0.4366 | 0.0 | nan | 0.0 | 0.3964 | 0.0 | 0.0 | 0.9124 | 0.8146 | 0.9591 | 0.0 | 0.0151 | 0.2492 | 0.0 | nan | 0.6754 | 0.8507 | 0.0921 | 0.6914 | 0.2094 | nan | 0.3695 | 0.5297 | 0.0 | 0.7594 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6818 | 0.0 | 0.0 | 0.6744 | 0.0 | 0.3527 | 0.3531 | 0.0 | nan | 0.0 | 0.2722 | 0.0 | 0.0 | 0.8145 | 0.7132 | 0.9232 | 0.0 | 0.0141 | 0.2027 | 0.0 |
| 0.3549 | 89.0 | 9523 | 0.6111 | 0.2875 | 0.3527 | 0.8318 | nan | 0.7690 | 0.9509 | 0.1307 | 0.8692 | 0.2542 | nan | 0.5263 | 0.7781 | 0.0 | 0.9164 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8690 | 0.0 | 0.0 | 0.8728 | 0.0 | 0.5455 | 0.4461 | 0.0 | nan | 0.0 | 0.3868 | 0.0 | 0.0 | 0.9071 | 0.8496 | 0.9605 | 0.0 | 0.0186 | 0.2354 | 0.0 | nan | 0.6819 | 0.8525 | 0.0922 | 0.6967 | 0.2025 | nan | 0.3693 | 0.5314 | 0.0 | 0.7691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6787 | 0.0 | 0.0 | 0.6703 | 0.0 | 0.3520 | 0.3585 | 0.0 | nan | 0.0 | 0.2679 | 0.0 | 0.0 | 0.8177 | 0.7221 | 0.9224 | 0.0 | 0.0174 | 0.1972 | 0.0 |
| 0.3675 | 90.0 | 9630 | 0.6085 | 0.2871 | 0.3525 | 0.8312 | nan | 0.7783 | 0.9479 | 0.1331 | 0.8670 | 0.2621 | nan | 0.5314 | 0.7927 | 0.0 | 0.9177 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8640 | 0.0 | 0.0 | 0.8641 | 0.0 | 0.5337 | 0.4436 | 0.0 | nan | 0.0 | 0.3959 | 0.0 | 0.0 | 0.9199 | 0.8105 | 0.9577 | 0.0 | 0.0193 | 0.2418 | 0.0 | nan | 0.6875 | 0.8524 | 0.0913 | 0.6871 | 0.2078 | nan | 0.3731 | 0.5255 | 0.0 | 0.7704 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6773 | 0.0 | 0.0 | 0.6731 | 0.0 | 0.3493 | 0.3616 | 0.0 | nan | 0.0 | 0.2691 | 0.0 | 0.0 | 0.8117 | 0.7092 | 0.9232 | 0.0 | 0.0179 | 0.2013 | 0.0 |
| 0.3346 | 91.0 | 9737 | 0.6175 | 0.2861 | 0.3523 | 0.8294 | nan | 0.7571 | 0.9528 | 0.1329 | 0.8727 | 0.2238 | nan | 0.5224 | 0.7853 | 0.0 | 0.9101 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8653 | 0.0 | 0.0 | 0.8636 | 0.0 | 0.5742 | 0.4281 | 0.0 | nan | 0.0 | 0.4133 | 0.0 | 0.0 | 0.9096 | 0.8216 | 0.9655 | 0.0001 | 0.0190 | 0.2571 | 0.0 | nan | 0.6775 | 0.8469 | 0.0898 | 0.6763 | 0.1828 | nan | 0.3720 | 0.5250 | 0.0 | 0.7756 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6748 | 0.0 | 0.0 | 0.6781 | 0.0 | 0.3545 | 0.3506 | 0.0 | nan | 0.0 | 0.2722 | 0.0 | 0.0 | 0.8162 | 0.7140 | 0.9213 | 0.0001 | 0.0176 | 0.2114 | 0.0 |
| 0.3679 | 92.0 | 9844 | 0.6140 | 0.2870 | 0.3545 | 0.8308 | nan | 0.7757 | 0.9425 | 0.1333 | 0.8753 | 0.2634 | nan | 0.5339 | 0.7934 | 0.0 | 0.9171 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8685 | 0.0 | 0.0 | 0.8648 | 0.0 | 0.5513 | 0.4414 | 0.0 | nan | 0.0 | 0.4012 | 0.0 | 0.0 | 0.9139 | 0.8384 | 0.9640 | 0.0001 | 0.0205 | 0.2449 | 0.0 | nan | 0.6849 | 0.8538 | 0.0900 | 0.6714 | 0.2093 | nan | 0.3755 | 0.5232 | 0.0 | 0.7691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6736 | 0.0 | 0.0 | 0.6739 | 0.0 | 0.3498 | 0.3594 | 0.0 | nan | 0.0 | 0.2714 | 0.0 | 0.0 | 0.8155 | 0.7201 | 0.9216 | 0.0001 | 0.0190 | 0.2031 | 0.0 |
| 0.3564 | 93.0 | 9951 | 0.6306 | 0.2873 | 0.3540 | 0.8295 | nan | 0.7487 | 0.9498 | 0.1348 | 0.8731 | 0.2800 | nan | 0.5271 | 0.7866 | 0.0 | 0.9001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8696 | 0.0 | 0.0 | 0.8795 | 0.0 | 0.5637 | 0.4355 | 0.0 | nan | 0.0 | 0.3995 | 0.0 | 0.0 | 0.9070 | 0.8256 | 0.9621 | 0.0004 | 0.0191 | 0.2643 | 0.0 | nan | 0.6768 | 0.8482 | 0.0894 | 0.6763 | 0.2202 | nan | 0.3755 | 0.5260 | 0.0 | 0.7800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6703 | 0.0 | 0.0 | 0.6708 | 0.0 | 0.3502 | 0.3531 | 0.0 | nan | 0.0 | 0.2703 | 0.0 | 0.0 | 0.8171 | 0.7166 | 0.9212 | 0.0004 | 0.0177 | 0.2146 | 0.0 |
| 0.3657 | 93.4579 | 10000 | 0.6095 | 0.2881 | 0.3546 | 0.8313 | nan | 0.7705 | 0.9498 | 0.1340 | 0.8681 | 0.2555 | nan | 0.5391 | 0.7858 | 0.0 | 0.9234 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8710 | 0.0 | 0.0 | 0.8511 | 0.0 | 0.5804 | 0.4355 | 0.0 | nan | 0.0 | 0.3931 | 0.0 | 0.0 | 0.9126 | 0.8313 | 0.9633 | 0.0 | 0.0209 | 0.2608 | 0.0 | nan | 0.6844 | 0.8516 | 0.0905 | 0.6851 | 0.2043 | nan | 0.3766 | 0.5327 | 0.0 | 0.7630 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6814 | 0.0 | 0.0 | 0.6784 | 0.0 | 0.3576 | 0.3553 | 0.0 | nan | 0.0 | 0.2718 | 0.0 | 0.0 | 0.8160 | 0.7180 | 0.9227 | 0.0 | 0.0191 | 0.2111 | 0.0 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
shkna1368/khanaiqobadi
|
shkna1368
| 2024-06-07T15:26:35Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-06-07T15:15:03Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Antoinegg1/llama-2-7b_truthful_0.75
|
Antoinegg1
| 2024-06-07T15:22:30Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:57:56Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Antoinegg1/llama-2-7b_safe_0.5to0.25_1
|
Antoinegg1
| 2024-06-07T15:22:30Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:57:06Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Antoinegg1/llama-2-7b_safe_0.25to0.5_1
|
Antoinegg1
| 2024-06-07T15:22:30Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:56:53Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Antoinegg1/llama-2-7b_truthful_0.5to0.75_1
|
Antoinegg1
| 2024-06-07T15:22:25Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:57:20Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Antoinegg1/llama-2-7b_safe_0.75to0.25_1
|
Antoinegg1
| 2024-06-07T15:22:24Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T13:56:51Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
shkna1368/khaniqobadi
|
shkna1368
| 2024-06-07T15:17:00Z | 161 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-06-07T15:15:06Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
AgentPublic/llama3-instruct-8b
|
AgentPublic
| 2024-06-07T15:14:03Z | 298 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-08T00:06:35Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
license_link: LICENSE
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the
Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3
distributed by Meta at https://llama.meta.com/get-started/.
"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into
this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or
regulations to provide legal consent and that has legal authority to bind your employer or such other
person or entity if you are entering in this Agreement on their behalf.
"Meta Llama 3" means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://llama.meta.com/llama-downloads.
"Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any
portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your
principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located
outside of the EEA or Switzerland).
1. License Rights and Redistribution.
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free
limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama
Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the
Llama Materials.
b. Redistribution and Use.
i. If you distribute or make available the Llama Materials (or any derivative works
thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide
a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta
Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you
use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is
distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model
name.
ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part
of an integrated end user product, then Section 2 of this Agreement will not apply to you.
iii. You must retain in all copies of the Llama Materials that you distribute the following
attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is
licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights
Reserved.”
iv. Your use of the Llama Materials must comply with applicable laws and regulations
(including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama
Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by
reference into this Agreement.
v. You will not use the Llama Materials or any output or results of the Llama Materials to
improve any other large language model (excluding Meta Llama 3 or derivative works thereof).
2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users
of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700
million monthly active users in the preceding calendar month, you must request a license from Meta,
which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the
rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF
ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED,
INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,
MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR
DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND
ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND
RESULTS.
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING
OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,
INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED
OF THE POSSIBILITY OF ANY OF THE FOREGOING.
5. Intellectual Property.
a. No trademark licenses are granted under this Agreement, and in connection with the Llama
Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other
or any of its affiliates, except as required for reasonable and customary use in describing and
redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to
use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will
comply with Meta’s brand guidelines (currently accessible at
https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use
of the Mark will inure to the benefit of Meta.
b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with
respect to any derivative works and modifications of the Llama Materials that are made by you, as
between you and Meta, you are and will be the owner of such derivative works and modifications.
c. If you institute litigation or other proceedings against Meta or any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or
results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other
rights owned or licensable by you, then any licenses granted to you under this Agreement shall
terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold
harmless Meta from and against any claim by any third party arising out of or related to your use or
distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your acceptance of this
Agreement or access to the Llama Materials and will continue in full force and effect until terminated in
accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in
breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete
and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this
Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of
the State of California without regard to choice of law principles, and the UN Convention on Contracts
for the International Sale of Goods does not apply to this Agreement. The courts of California shall have
exclusive jurisdiction of any dispute arising out of this Agreement.
### Meta Llama 3 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
#### Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
others to use, Meta Llama 3 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Meta Llama 3 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
widget:
- example_title: Hello
messages:
- role: user
content: Hey my name is Julien! How are you?
- example_title: Winter holidays
messages:
- role: system
content: You are a helpful and honest assistant. Please, respond concisely and truthfully.
- role: user
content: Can you recommend a good destination for Winter holidays?
- example_title: Programming assistant
messages:
- role: system
content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully.
- role: user
content: Write a function that computes the nth fibonacci number.
inference:
parameters:
max_new_tokens: 300
stop:
- <|end_of_text|>
- <|eot_id|>
---
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
backyardai/Llama-3-70b-Uncensored-Lumi-Tess-gradient-GGUF
|
backyardai
| 2024-06-07T15:12:45Z | 2,203 | 3 | null |
[
"gguf",
"uncencored",
"llama-3",
"tess",
"lumimaid",
"Lumi-tess",
"base_model:ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient",
"base_model:quantized:ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-06-07T12:38:01Z |
---
tags:
- uncencored
- llama-3
- tess
- lumimaid
- Lumi-tess
base_model: ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient
model_name: Llama-3-70b-Uncensored-Lumi-Tess-gradient-GGUF
quantized_by: brooketh
parameter_count: 70553706496
---
<img src="BackyardAI_Banner.png" alt="Backyard.ai" style="height: 90px; min-width: 32px; display: block; margin: auto;">
**<p style="text-align: center;">The official library of GGUF format models for use in the local AI chat app, Backyard AI.</p>**
<p style="text-align: center;"><a href="https://backyard.ai/">Download Backyard AI here to get started.</a></p>
<p style="text-align: center;"><a href="https://www.reddit.com/r/LLM_Quants/">Request Additional models at r/LLM_Quants.</a></p>
***
# Llama 3 Uncensored Lumi Tess Gradient 70B
- **Creator:** [ryzen88](https://huggingface.co/ryzen88/)
- **Original:** [Llama 3 Uncensored Lumi Tess Gradient 70B](https://huggingface.co/ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient)
- **Date Created:** 2024-05-10
- **Trained Context:** 262144 tokens
- **Description:** Good Llama3 uncensored model with a long context, made using a breadcrumb ties merger of Instruct-gradient, Lumimaid, and Tess models. Works with a very wide range of sampler settings.
***
## What is a GGUF?
GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.
GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.
***
<img src="BackyardAI_Logo.png" alt="Backyard.ai" style="height: 75px; min-width: 32px; display: block; horizontal align: left;">
## Backyard AI
- Free, local AI chat application.
- One-click installation on Mac and PC.
- Automatically use GPU for maximum speed.
- Built-in model manager.
- High-quality character hub.
- Zero-config desktop-to-mobile tethering.
Backyard AI makes it easy to start chatting with AI using your own characters or one of the many found in the built-in character hub. The model manager helps you find the latest and greatest models without worrying about whether it's the correct format. Backyard AI supports advanced features such as lorebooks, author's note, text formatting, custom context size, sampler settings, grammars, local TTS, cloud inference, and tethering, all implemented in a way that is straightforward and reliable.
**Join us on [Discord](https://discord.gg/SyNN2vC9tQ)**
***
|
mlx-community/omost-phi-3-mini-128k-mlx-8bit
|
mlx-community
| 2024-06-07T15:07:56Z | 9 | 0 |
mlx
|
[
"mlx",
"safetensors",
"phi3",
"pytorch",
"trl",
"sft",
"custom_code",
"region:us"
] | null | 2024-06-07T14:22:50Z |
---
tags:
- pytorch
- trl
- sft
- mlx
inference: false
---
# mlx-community/omost-phi-3-mini-128k-mlx-8bit
The Model [mlx-community/omost-phi-3-mini-128k-mlx-8bit](https://huggingface.co/mlx-community/omost-phi-3-mini-128k-mlx-8bit) was converted to MLX format from [lllyasviel/omost-phi-3-mini-128k](https://huggingface.co/lllyasviel/omost-phi-3-mini-128k) using mlx-lm version **0.14.3**.
## Intended Usage
Model finetuned specifically for lllyasviel's [Omost project](https://github.com/lllyasviel/Omost)
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/omost-phi-3-mini-128k-mlx-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
xMaulana/QLoRA-Psychika
|
xMaulana
| 2024-06-07T15:07:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T15:07:11Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
meyandrei/bankchat
|
meyandrei
| 2024-06-07T15:07:20Z | 150 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T10:40:45Z |
---
base_model: output_model_gpt2_20epoch
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output_model_gpt2_50epoch
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. -->
# output_model_gpt2_50epoch
This model is a fine-tuned version of [output_model_gpt2_20epoch](https://huggingface.co/output_model_gpt2_20epoch) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7376
- Accuracy: 0.5284
## 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 18
- total_train_batch_size: 54
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
ntkhoi/bart-vi-news-summarization
|
ntkhoi
| 2024-06-07T15:07:14Z | 184 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"summarization",
"vi",
"dataset:vietgpt/news_summarization_vi",
"base_model:PhucMap/BARTphoVi-VSL",
"base_model:finetune:PhucMap/BARTphoVi-VSL",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2024-06-01T16:51:23Z |
---
license: mit
base_model: PhucMap/BARTphoVi-VSL
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mBART-vi-news-summarization
results: []
datasets:
- vietgpt/news_summarization_vi
language:
- vi
pipeline_tag: summarization
---
<!-- 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-vi-news-summarization
This model is a fine-tuned version of [PhucMap/BARTphoVi-VSL](https://huggingface.co/PhucMap/BARTphoVi-VSL) on vietgpt/news_summarization_vi.
It achieves the following results on the evaluation set:
- Loss: 0.8166
- Rouge1: 70.8598
- Rouge2: 51.2255
- Rougel: 51.8606
- Rougelsum: 65.5856
- Gen Len: 152.869
## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
KalidasVijayBhak/output
|
KalidasVijayBhak
| 2024-06-07T15:06:30Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2024-06-07T15:06:28Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: PY007/TinyLlama-1.1B-Chat-v0.3
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
narraticlabs/MiniLM-L6-european-union
|
narraticlabs
| 2024-06-07T15:06:26Z | 9 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"de",
"fr",
"es",
"it",
"nl",
"pl",
"pt",
"hu",
"ro",
"sv",
"da",
"fi",
"cs",
"el",
"bg",
"hr",
"et",
"lv",
"lt",
"mt",
"sk",
"sl",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-05-31T04:13:04Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- en
- de
- fr
- es
- it
- nl
- pl
- pt
- hu
- ro
- sv
- da
- fi
- cs
- el
- bg
- hr
- et
- lv
- lt
- mt
- sk
- sl
license: apache-2.0
---
# narraticlabs/MiniLM-L6-european-union
Distillation of paraphrase-MiniLM-L12 to 6 layers with all EU languages
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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('narraticlabs/MiniLM-L6-european-union')
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('narraticlabs/MiniLM-L6-european-union')
model = AutoModel.from_pretrained('narraticlabs/MiniLM-L6-european-union')
# 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=narraticlabs/MiniLM-L6-european-union)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 143002 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
NaomiH/coc-desolation
|
NaomiH
| 2024-06-07T15:02:11Z | 2 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2024-06-07T15:01:46Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/ComfyUI_00062_.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: apache-2.0
---
# Cult of clown
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/NaomiH/coc-desolation/tree/main) them in the Files & versions tab.
|
cascadenite/q-Taxi-v3-3rd_train
|
cascadenite
| 2024-06-07T14:59:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-07T14:41:29Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-3rd_train
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="cascadenite/q-Taxi-v3-3rd_train", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Kingpeach/Reinforce-CartPole-v1
|
Kingpeach
| 2024-06-07T14:54:22Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-07T14:54:12Z |
---
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
|
ttangmo24/vit-base-classification-Eye-Diseases
|
ttangmo24
| 2024-06-07T14:53:43Z | 216 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-05-31T16:02:41Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer12_16_20-def_layer12-wikitext-41
|
PhillipGuo
| 2024-06-07T14:45:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:45:14Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
Jjzzzz/whisper-tiny-hi2
|
Jjzzzz
| 2024-06-07T14:45:21Z | 138 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-06-03T09:59:34Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-tiny-hi2
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. -->
# whisper-tiny-hi2
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6992
- Wer: 141.0704
## 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: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer12_16_20-def_layer12-wikitext-41
|
PhillipGuo
| 2024-06-07T14:43:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:43:40Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
hmandsager/detr-finetuned-balloon-v2
|
hmandsager
| 2024-06-07T14:43:25Z | 191 | 0 |
transformers
|
[
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2024-06-07T14:43:17Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer12_16_20-def_layer12-wikitext-39
|
PhillipGuo
| 2024-06-07T14:32:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:32:22Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer12_16_20-def_layer12-wikitext-40
|
PhillipGuo
| 2024-06-07T14:32:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:32:05Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer12_16_20-def_layer12-wikitext-41
|
PhillipGuo
| 2024-06-07T14:31:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:31:31Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer12_16_20-def_layer12-wikitext-40
|
PhillipGuo
| 2024-06-07T14:31:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:31:15Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer12_16_20-def_layer12-wikitext-39
|
PhillipGuo
| 2024-06-07T14:31:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:31:11Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
temorubun/callai
|
temorubun
| 2024-06-07T14:30:30Z | 0 | 0 | null |
[
"code",
"legal",
"text-to-speech",
"id",
"en",
"dataset:Locutusque/function-calling-chatml",
"dataset:openbmb/RLAIF-V-Dataset",
"license:apache-2.0",
"region:us"
] |
text-to-speech
| 2024-06-07T14:13:07Z |
---
license: apache-2.0
datasets:
- Locutusque/function-calling-chatml
- openbmb/RLAIF-V-Dataset
language:
- id
- en
metrics:
- accuracy
- character
pipeline_tag: text-to-speech
tags:
- code
- legal
---
|
kajamo/model_14
|
kajamo
| 2024-06-07T14:23:05Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:adapter:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2024-06-07T12:32:42Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: distilbert-base-uncased
model-index:
- name: model_14
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_14
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:
- eval_loss: 0.6205
- eval_accuracy: 0.7676
- eval_precision: 0.7674
- eval_recall: 0.7676
- eval_f1: 0.7670
- eval_runtime: 61.0973
- eval_samples_per_second: 200.418
- eval_steps_per_second: 25.058
- epoch: 9.0
- step: 55107
## 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
melissoep/roberta_finetuning-sentiment-hpReddit
|
melissoep
| 2024-06-07T14:22:26Z | 108 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment-latest",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-07T13:10:27Z |
---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
model-index:
- name: roberta_finetuning-sentiment-hpReddit
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_finetuning-sentiment-hpReddit
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6539
- eval_accuracy: 0.6974
- eval_f1: 0.6931
- eval_Precision: 0.7627
- eval_runtime: 4.2935
- eval_samples_per_second: 17.701
- eval_steps_per_second: 1.165
- step: 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: 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: 2
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
camilomj/youngbloodguitar
|
camilomj
| 2024-06-07T14:19:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-06-07T14:18:38Z |
---
license: apache-2.0
---
|
mradermacher/Solus-103B-L2-GGUF
|
mradermacher
| 2024-06-07T14:18:52Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Sao10K/Solus-103B-L2",
"base_model:quantized:Sao10K/Solus-103B-L2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-05T04:49:54Z |
---
base_model: Sao10K/Solus-103B-L2
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Sao10K/Solus-103B-L2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Solus-103B-L2-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q2_K.gguf) | Q2_K | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.IQ3_XS.gguf) | IQ3_XS | 42.3 | |
| [GGUF](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q3_K_S.gguf) | Q3_K_S | 44.6 | |
| [GGUF](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.IQ3_S.gguf) | IQ3_S | 44.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.IQ3_M.gguf) | IQ3_M | 46.2 | |
| [GGUF](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q3_K_M.gguf) | Q3_K_M | 49.7 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q3_K_L.gguf.part2of2) | Q3_K_L | 54.2 | |
| [PART 1](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.IQ4_XS.gguf.part2of2) | IQ4_XS | 55.7 | |
| [PART 1](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q4_K_S.gguf.part2of2) | Q4_K_S | 58.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q4_K_M.gguf.part2of2) | Q4_K_M | 62.0 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q5_K_S.gguf.part2of2) | Q5_K_S | 71.1 | |
| [PART 1](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q5_K_M.gguf.part2of2) | Q5_K_M | 73.0 | |
| [PART 1](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q6_K.gguf.part2of2) | Q6_K | 84.8 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Solus-103B-L2-GGUF/resolve/main/Solus-103B-L2.Q8_0.gguf.part3of3) | Q8_0 | 109.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
cgihlstorf/llama2-13b_32_1_0.0003_sequential_RANDOM_75pct
|
cgihlstorf
| 2024-06-07T14:18:47Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:adapter:meta-llama/Llama-2-13b-hf",
"region:us"
] | null | 2024-06-07T14:17:55Z |
---
library_name: peft
base_model: meta-llama/Llama-2-13b-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]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
### Framework versions
- PEFT 0.10.0
|
timtimtimshen/gemma-2b-flock-1717769757
|
timtimtimshen
| 2024-06-07T14:18:03Z | 150 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-07T14:15:58Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
baf2b252097d46299a/disease_diagnosis_5f550955d8dd4c39a97a17877c71685c
|
baf2b252097d46299a
| 2024-06-07T14:16:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:15:56Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
yirifiai1/BERT_Regulatory_Text_Classification_01
|
yirifiai1
| 2024-06-07T14:15:31Z | 135 | 2 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"multilabel",
"classification",
"finetune",
"finance",
"regulatory",
"text",
"risk",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-06T12:44:51Z |
---
language:
- en
library_name: transformers
tags:
- bert
- multilabel
- classification
- finetune
- finance
- regulatory
- text
- risk
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: >-
Where an FI employs a technological solution provided by an external party
to conduct screening of virtual asset transactions and the associated wallet
addresses, the FI remains responsible for discharging its AML/CFT
obligations. The FI should conduct due diligence on the solution before
deploying it, taking into account relevant factors such as:
---
This model is a fine-tuned version of the BERT language model, specifically adapted for multi-label classification tasks in the
financial regulatory domain. It is built upon the pre-trained ProsusAI/finbert model, which has been further fine-tuned using a diverse
dataset of financial regulatory texts. This allows the model to accurately classify text into multiple relevant categories simultaneously.
# Model Architecture
- **Base Model**: BERT
- **Pre-trained Model**: ProsusAI/finbert
- **Task**: Multi-label classification
## Performance
Performance metrics on the validation set:
- F1 Score: 0.8637
- ROC AUC: 0.9044
- Accuracy: 0.6155
## Limitations and Ethical Considerations
- This model's performance may vary depending on the specific nature of the text data and label distribution.
- Class imbalance in the dataset.
## Dataset Information
- **Training Dataset**: Number of samples: 6562
- **Validation Dataset**: Number of samples: 929
- **Test Dataset**: Number of samples: 1884
## Training Details
- **Training Strategy**: Fine-tuning BERT with a randomly initialized classification head.
- **Optimizer**: Adam
- **Learning Rate**: 1e-4
- **Batch Size**: 16
- **Number of Epochs**: 2
- **Evaluation Strategy**: Epoch
- **Weight Decay**: 0.01
- **Metric for Best Model**: F1 Score
|
Labira/LabiraEdu-v1.0x
|
Labira
| 2024-06-07T14:15:04Z | 21 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:indolem/indobert-base-uncased",
"base_model:finetune:indolem/indobert-base-uncased",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-06-07T13:14:53Z |
---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Labira/LabiraEdu-v1.0x
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. -->
# Labira/LabiraEdu-v1.0x
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0206
- Validation Loss: 4.5266
- Epoch: 98
## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1100, '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 | Epoch |
|:----------:|:---------------:|:-----:|
| 5.0565 | 3.9761 | 0 |
| 3.6621 | 3.2932 | 1 |
| 3.0961 | 3.2587 | 2 |
| 2.7357 | 3.2031 | 3 |
| 2.3059 | 3.2519 | 4 |
| 1.8933 | 3.4772 | 5 |
| 1.9076 | 3.1664 | 6 |
| 1.5492 | 3.4201 | 7 |
| 1.2578 | 3.5190 | 8 |
| 1.0478 | 3.4076 | 9 |
| 1.0130 | 3.5961 | 10 |
| 0.9073 | 3.4919 | 11 |
| 0.7071 | 3.5013 | 12 |
| 0.5616 | 4.0259 | 13 |
| 0.4798 | 3.9766 | 14 |
| 0.5938 | 3.8146 | 15 |
| 0.6476 | 3.7065 | 16 |
| 0.4264 | 4.1631 | 17 |
| 0.5290 | 3.7455 | 18 |
| 0.4637 | 3.6362 | 19 |
| 0.3826 | 3.8389 | 20 |
| 0.2876 | 3.7611 | 21 |
| 0.2221 | 4.0540 | 22 |
| 0.1752 | 4.0683 | 23 |
| 0.1544 | 4.0452 | 24 |
| 0.1600 | 4.0417 | 25 |
| 0.1390 | 4.0668 | 26 |
| 0.1134 | 4.0659 | 27 |
| 0.0965 | 4.0700 | 28 |
| 0.0820 | 4.2026 | 29 |
| 0.0810 | 4.3008 | 30 |
| 0.1166 | 4.0835 | 31 |
| 0.0776 | 4.0886 | 32 |
| 0.1033 | 4.1303 | 33 |
| 0.0512 | 4.1014 | 34 |
| 0.0484 | 4.1462 | 35 |
| 0.0565 | 4.2404 | 36 |
| 0.0652 | 4.2064 | 37 |
| 0.0538 | 4.1032 | 38 |
| 0.0516 | 4.0948 | 39 |
| 0.0611 | 4.2563 | 40 |
| 0.0523 | 4.3629 | 41 |
| 0.0571 | 4.3032 | 42 |
| 0.0479 | 4.3147 | 43 |
| 0.0308 | 4.3639 | 44 |
| 0.0370 | 4.3490 | 45 |
| 0.0406 | 4.3471 | 46 |
| 0.0300 | 4.4078 | 47 |
| 0.0270 | 4.4253 | 48 |
| 0.0283 | 4.4177 | 49 |
| 0.0228 | 4.4394 | 50 |
| 0.0538 | 4.4019 | 51 |
| 0.0342 | 4.3553 | 52 |
| 0.0249 | 4.3161 | 53 |
| 0.0657 | 4.4426 | 54 |
| 0.0309 | 4.5678 | 55 |
| 0.0467 | 4.4247 | 56 |
| 0.0356 | 4.5058 | 57 |
| 0.0431 | 4.4563 | 58 |
| 0.0366 | 4.5242 | 59 |
| 0.0624 | 4.3149 | 60 |
| 0.0471 | 4.3177 | 61 |
| 0.0248 | 4.3159 | 62 |
| 0.0388 | 4.3554 | 63 |
| 0.0262 | 4.3888 | 64 |
| 0.0360 | 4.4544 | 65 |
| 0.0319 | 4.4608 | 66 |
| 0.0269 | 4.4676 | 67 |
| 0.0373 | 4.3847 | 68 |
| 0.0205 | 4.3560 | 69 |
| 0.0223 | 4.3715 | 70 |
| 0.0306 | 4.3894 | 71 |
| 0.0235 | 4.4409 | 72 |
| 0.0189 | 4.4767 | 73 |
| 0.0280 | 4.5137 | 74 |
| 0.0165 | 4.5471 | 75 |
| 0.0098 | 4.5553 | 76 |
| 0.0173 | 4.5465 | 77 |
| 0.0234 | 4.5461 | 78 |
| 0.0231 | 4.5485 | 79 |
| 0.0237 | 4.5326 | 80 |
| 0.0158 | 4.5293 | 81 |
| 0.0178 | 4.5309 | 82 |
| 0.0225 | 4.5306 | 83 |
| 0.0191 | 4.5213 | 84 |
| 0.0213 | 4.5231 | 85 |
| 0.0144 | 4.5332 | 86 |
| 0.0191 | 4.5365 | 87 |
| 0.0188 | 4.5487 | 88 |
| 0.0272 | 4.5426 | 89 |
| 0.0126 | 4.5390 | 90 |
| 0.0224 | 4.5384 | 91 |
| 0.0218 | 4.5389 | 92 |
| 0.0083 | 4.5394 | 93 |
| 0.0246 | 4.5326 | 94 |
| 0.0199 | 4.5284 | 95 |
| 0.0174 | 4.5264 | 96 |
| 0.0130 | 4.5259 | 97 |
| 0.0206 | 4.5266 | 98 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.19.2
- Tokenizers 0.19.1
|
baf2b252097d46299a/medical_summarizer_0f9315fa1b204234aaedc8de3c98fbeb
|
baf2b252097d46299a
| 2024-06-07T14:12:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:11:50Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
vishal324/hb_info2
|
vishal324
| 2024-06-07T14:11:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-07T14:11:14Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** vishal324
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
hyunjae/kogpt2_reward_model
|
hyunjae
| 2024-06-07T14:08:39Z | 162 | 1 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"feature-extraction",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-06-07T13:49:57Z |
---
license: apache-2.0
---
|
huyhuy123/paraphrase-vietnamese-law-ALQAC
|
huyhuy123
| 2024-06-07T14:07:06Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:7696",
"loss:TripletLoss",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:minhquan6203/paraphrase-vietnamese-law",
"base_model:finetune:minhquan6203/paraphrase-vietnamese-law",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-05T14:57:56Z |
---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7696
- loss:TripletLoss
base_model: minhquan6203/paraphrase-vietnamese-law
datasets: []
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
widget:
- source_sentence: 'Bộ Giáo dục và Đào tạo chịu trách nhiệm về quy định việc dạy và
học bằng tiếng nước ngoài trong cơ sở giáo dục.
'
sentences:
- 'Giáo dục hòa nhập
1. Giáo dục hòa nhập là phương thức giáo dục nhằm đáp ứng nhu cầu và khả năng
khác nhau của người học; bảo đảm quyền học tập bình đẳng, chất lượng giáo dục,
phù hợp với nhu cầu, đặc điểm và khả năng của người học; tôn trọng sự đa dạng,
khác biệt của người học và không phân biệt đối xử.
2. Nhà nước có chính sách hỗ trợ thực hiện giáo dục hòa nhập cho người học là
trẻ em có hoàn cảnh đặc biệt theo quy định của Luật Trẻ em, người học là người
khuyết tật theo quy định của Luật Người khuyết tật và quy định khác của pháp luật
có liên quan.'
- 'Ngôn ngữ, chữ viết dùng trong cơ sở giáo dục
1. Tiếng Việt là ngôn ngữ chính thức dùng trong cơ sở giáo dục. Căn cứ vào mục
tiêu giáo dục và yêu cầu cụ thể về nội dung giáo dục, Chính phủ quy định việc
dạy và học bằng tiếng nước ngoài trong cơ sở giáo dục.
2. Nhà nước khuyến khích, tạo điều kiện để người dân tộc thiểu số được học tiếng
nói, chữ viết của dân tộc mình theo quy định của Chính phủ; người khuyết tật nghe,
nói được học bằng ngôn ngữ ký hiệu, người khuyết tật nhìn được học bằng chữ nổi
Braille theo quy định của Luật Người khuyết tật.
3. Ngoại ngữ quy định trong chương trình giáo dục là ngôn ngữ được sử dụng phổ
biến trong giao dịch quốc tế. Việc tổ chức dạy ngoại ngữ trong cơ sở giáo dục
phải bảo đảm để người học được học liên tục, hiệu quả.'
- 'Việc thực hiện quyền tự do cư trú của công dân
1. Công dân thực hiện quyền tự do cư trú theo quy định của Luật này và quy định
khác của pháp luật có liên quan.
2. Việc thực hiện quyền tự do cư trú của công dân chỉ bị hạn chế trong các trường
hợp sau đây:
a) Người bị cơ quan, người có thẩm quyền tiến hành tố tụng áp dụng biện pháp cấm
đi khỏi nơi cư trú, biện pháp tạm giữ, tạm giam; người bị kết án phạt tù nhưng
chưa có quyết định thi hành án hoặc đã có quyết định thi hành án nhưng đang tại
ngoại hoặc được hoãn chấp hành án, tạm đình chỉ chấp hành án; người bị kết án
phạt tù được hưởng án treo đang trong thời gian thử thách; người đang chấp hành
án phạt tù, cấm cư trú, quản chế hoặc cải tạo không giam giữ; người được tha tù
trước thời hạn có điều kiện đang trong thời gian thử thách;
b) Người đang chấp hành biện pháp giáo dục tại xã, phường, thị trấn, biện pháp
đưa vào cơ sở giáo dục bắt buộc, đưa vào cơ sở cai nghiện bắt buộc, đưa vào trường
giáo dưỡng; người phải chấp hành biện pháp đưa vào cơ sở giáo dục bắt buộc,
đưa vào cơ sở cai nghiện bắt buộc, đưa vào trường giáo dưỡng nhưng đang được hoãn
chấp hành hoặc tạm đình chỉ chấp hành; người bị quản lý trong thời gian làm thủ
tục xem xét, quyết định áp dụng biện pháp đưa vào cơ sở giáo dục bắt buộc, đưa
vào cơ sở cai nghiện bắt buộc, đưa vào trường giáo dưỡng;
c) Người bị cách ly do có nguy cơ lây lan dịch bệnh cho cộng đồng;
d) Địa điểm, khu vực cách ly vì lý do phòng, chống dịch bệnh theo quyết định của
cơ quan, người có thẩm quyền; địa bàn có tình trạng khẩn cấp theo quy định của
pháp luật; địa điểm không được đăng ký thường trú mới, đăng ký tạm trú mới, tách
hộ theo quy định của Luật này;
đ) Các trường hợp khác theo quy định của luật.
3. Việc thực hiện quyền tự do cư trú của công dân chỉ bị hạn chế theo quy định
của luật. Nội dung, thời gian hạn chế quyền tự do cư trú thực hiện theo bản án,
quyết định có hiệu lực pháp luật của Tòa án, quyết định của cơ quan nhà nước khác
có thẩm quyền hoặc theo quy định của luật có liên quan.'
- source_sentence: Bộ trưởng Bộ Giáo dục và Đào tạo không được quy định ngưỡng đầu
vào trình độ cao đẳng, trình độ đại học thuộc ngành đào tạo giáo viên, đúng hay
sai?
sentences:
- 'Hệ thống giáo dục quốc dân
1. Hệ thống giáo dục quốc dân là hệ thống giáo dục mở, liên thông gồm giáo dục
chính quy và giáo dục thường xuyên.
2. Cấp học, trình độ đào tạo của hệ thống giáo dục quốc dân bao gồm:
a) Giáo dục mầm non gồm giáo dục nhà trẻ và giáo dục mẫu giáo;
b) Giáo dục phổ thông gồm giáo dục tiểu học, giáo dục trung học cơ sở và giáo
dục trung học phổ thông;
c) Giáo dục nghề nghiệp đào tạo trình độ sơ cấp, trình độ trung cấp, trình độ
cao đẳng và các chương trình đào tạo nghề nghiệp khác;
d) Giáo dục đại học đào tạo trình độ đại học, trình độ thạc sĩ và trình độ tiến
sĩ.
3. Thủ tướng Chính phủ quyết định phê duyệt Khung cơ cấu hệ thống giáo dục quốc
dân và Khung trình độ quốc gia Việt Nam; quy định thời gian đào tạo, tiêu chuẩn
cho từng trình độ đào tạo, khối lượng học tập tối thiểu đối với trình độ của giáo
dục nghề nghiệp, giáo dục đại học.
4. Bộ trưởng Bộ Giáo dục và Đào tạo, Bộ trưởng Bộ Lao động - Thương binh và Xã
hội, trong phạm vi nhiệm vụ, quyền hạn của mình, quy định ngưỡng đầu vào trình
độ cao đẳng, trình độ đại học thuộc ngành đào tạo giáo viên và ngành thuộc lĩnh
vực sức khỏe.'
- 'Người học
Người học là người đang học tập tại cơ sở giáo dục trong hệ thống giáo dục quốc
dân bao gồm:
1. Trẻ em của cơ sở giáo dục mầm non;
2. Học sinh của cơ sở giáo dục phổ thông, lớp đào tạo nghề, trung tâm giáo dục
nghề nghiệp - giáo dục thường xuyên, trung tâm giáo dục nghề nghiệp, trường trung
cấp, trường dự bị đại học;
3. Sinh viên của trường cao đẳng, trường đại học;
4. Học viên của cơ sở đào tạo thạc sĩ;
5. Nghiên cứu sinh của cơ sở đào tạo tiến sĩ;
6. Học viên theo học chương trình giáo dục thường xuyên.'
- 'Thẩm quyền giải quyết các tranh chấp của Trọng tài
1. Tranh chấp giữa các bên phát sinh từ hoạt động thương mại.
2. Tranh chấp phát sinh giữa các bên trong đó ít nhất một bên có hoạt động thương
mại.
3. Tranh chấp khác giữa các bên mà pháp luật quy định được giải quyết bằng Trọng
tài.'
- source_sentence: Việc sinh con bằng kỹ thuật hỗ trợ sinh sản là hành vi trong đó
con người sử dụng phương pháp nhân tạo thụ tinh hoặc thụ tinh trong ống nghiệm
để sinh con.
sentences:
- 'Quyền, nghĩa vụ về học tập, làm việc, tham gia hoạt động chính trị, kinh tế,
văn hóa, xã hội
Vợ, chồng có quyền, nghĩa vụ tạo điều kiện, giúp đỡ nhau chọn nghề nghiệp; học
tập, nâng cao trình độ văn hóa, chuyên môn, nghiệp vụ; tham gia hoạt động chính
trị, kinh tế, văn hóa, xã hội.'
- 'Giải thích từ ngữ
Trong Luật này, các từ ngữ dưới đây được hiểu như sau:
1. Hôn nhân là quan hệ giữa vợ và chồng sau khi kết hôn.
2. Gia đình là tập hợp những người gắn bó với nhau do hôn nhân, quan hệ huyết
thống hoặc quan hệ nuôi dưỡng, làm phát sinh các quyền và nghĩa vụ giữa họ với
nhau theo quy định của Luật này.
3. Chế độ hôn nhân và gia đình là toàn bộ những quy định của pháp luật về kết
hôn, ly hôn; quyền và nghĩa vụ giữa vợ và chồng, giữa cha mẹ và con, giữa các
thành viên khác trong gia đình; cấp dưỡng; xác định cha, mẹ, con; quan hệ hôn
nhân và gia đình có yếu tố nước ngoài và những vấn đề khác liên quan đến hôn nhân
và gia đình.
4. Tập quán về hôn nhân và gia đình là quy tắc xử sự có nội dung rõ ràng về quyền,
nghĩa vụ của các bên trong quan hệ hôn nhân và gia đình, được lặp đi, lặp lại
trong một thời gian dài và được thừa nhận rộng rãi trong một vùng, miền hoặc cộng
đồng.
5. Kết hôn là việc nam và nữ xác lập quan hệ vợ chồng với nhau theo quy định của
Luật này về điều kiện kết hôn và đăng ký kết hôn.
6. Kết hôn trái pháp luật là việc nam, nữ đã đăng ký kết hôn tại cơ quan nhà nước
có thẩm quyền nhưng một bên hoặc cả hai bên vi phạm điều kiện kết hôn theo quy
định tại Điều 8 của Luật này.
7. Chung sống như vợ chồng là việc nam, nữ tổ chức cuộc sống chung và coi nhau
là vợ chồng.
8. Tảo hôn là việc lấy vợ, lấy chồng khi một bên hoặc cả hai bên chưa đủ tuổi
kết hôn theo quy định tại điểm a khoản 1 Điều 8 của Luật này.
9. Cưỡng ép kết hôn, ly hôn là việc đe dọa, uy hiếp tinh thần, hành hạ, ngược
đãi, yêu sách của cải hoặc hành vi khác để buộc người khác phải kết hôn hoặc ly
hôn trái với ý muốn của họ.
10. Cản trở kết hôn, ly hôn là việc đe dọa, uy hiếp tinh thần, hành hạ, ngược
đãi, yêu sách của cải hoặc hành vi khác để ngăn cản việc kết hôn của người có
đủ điều kiện kết hôn theo quy định của Luật này hoặc buộc người khác phải duy
trì quan hệ hôn nhân trái với ý muốn của họ.
11. Kết hôn giả tạo là việc lợi dụng kết hôn để xuất cảnh, nhập cảnh, cư trú,
nhập quốc tịch Việt Nam, quốc tịch nước ngoài; hưởng chế độ ưu đãi của Nhà nước
hoặc để đạt được mục đích khác mà không nhằm mục đích xây dựng gia đình.
12. Yêu sách của cải trong kết hôn là việc đòi hỏi về vật chất một cách quá đáng
và coi đó là điều kiện để kết hôn nhằm cản trở việc kết hôn tự nguyện của nam,
nữ.
13. Thời kỳ hôn nhân là khoảng thời gian tồn tại quan hệ vợ chồng, được tính từ
ngày đăng ký kết hôn đến ngày chấm dứt hôn nhân.
14. Ly hôn là việc chấm dứt quan hệ vợ chồng theo bản án, quyết định có hiệu lực
pháp luật của Tòa án.
15. Ly hôn giả tạo là việc lợi dụng ly hôn để trốn tránh nghĩa vụ tài sản, vi
phạm chính sách, pháp luật về dân số hoặc để đạt được mục đích khác mà không nhằm
mục đích chấm dứt hôn nhân.
16. Thành viên gia đình bao gồm vợ, chồng; cha mẹ đẻ, cha mẹ nuôi, cha dượng,
mẹ kế, cha mẹ vợ, cha mẹ chồng; con đẻ, con nuôi, con riêng của vợ hoặc chồng,
con dâu, con rể; anh, chị, em cùng cha mẹ, anh, chị, em cùng cha khác mẹ, anh,
chị, em cùng mẹ khác cha, anh rể, em rể, chị dâu, em dâu của người cùng cha mẹ
hoặc cùng cha khác mẹ, cùng mẹ khác cha; ông bà nội, ông bà ngoại; cháu nội, cháu
ngoại; cô, dì, chú, cậu, bác ruột và cháu ruột.
17. Những người cùng dòng máu về trực hệ là những người có quan hệ huyết thống,
trong đó, người này sinh ra người kia kế tiếp nhau.
18. Những người có họ trong phạm vi ba đời là những người cùng một gốc sinh ra
gồm cha mẹ là đời thứ nhất; anh, chị, em cùng cha mẹ, cùng cha khác mẹ, cùng mẹ
khác cha là đời thứ hai; anh, chị, em con chú, con bác, con cô, con cậu, con dì
là đời thứ ba.
19. Người thân thích là người có quan hệ hôn nhân, nuôi dưỡng, người có cùng dòng
máu về trực hệ và người có họ trong phạm vi ba đời.
20. Nhu cầu thiết yếu là nhu cầu sinh hoạt thông thường về ăn, mặc, ở, học tập,
khám bệnh, chữa bệnh và nhu cầu sinh hoạt thông thường khác không thể thiếu cho
cuộc sống của mỗi người, mỗi gia đình.
21. Sinh con bằng kỹ thuật hỗ trợ sinh sản là việc sinh con bằng kỹ thuật thụ
tinh nhân tạo hoặc thụ tinh trong ống nghiệm.
22. Mang thai hộ vì mục đích nhân đạo là việc một người phụ nữ tự nguyện, không
vì mục đích thương mại giúp mang thai cho cặp vợ chồng mà người vợ không thể mang
thai và sinh con ngay cả khi áp dụng kỹ thuật hỗ trợ sinh sản, bằng việc lấy noãn
của người vợ và tinh trùng của người chồng để thụ tinh trong ống nghiệm, sau đó
cấy vào tử cung của người phụ nữ tự nguyện mang thai để người này mang thai và
sinh con.
23. Mang thai hộ vì mục đích thương mại là việc một người phụ nữ mang thai cho
người khác bằng việc áp dụng kỹ thuật hỗ trợ sinh sản để được hưởng lợi về kinh
tế hoặc lợi ích khác.
24. Cấp dưỡng là việc một người có nghĩa vụ đóng góp tiền hoặc tài sản khác để
đáp ứng nhu cầu thiết yếu của người không sống chung với mình mà có quan hệ hôn
nhân, huyết thống hoặc nuôi dưỡng trong trường hợp người đó là người chưa thành
niên, người đã thành niên mà không có khả năng lao động và không có tài sản để
tự nuôi mình hoặc người gặp khó khăn, túng thiếu theo quy định của Luật này.
25. Quan hệ hôn nhân và gia đình có yếu tố nước ngoài là quan hệ hôn nhân và gia
đình mà ít nhất một bên tham gia là người nước ngoài, người Việt Nam định cư ở
nước ngoài; quan hệ hôn nhân và gia đình giữa các bên tham gia là công dân Việt
Nam nhưng căn cứ để xác lập, thay đổi, chấm dứt quan hệ đó theo pháp luật nước
ngoài, phát sinh tại nước ngoài hoặc tài sản liên quan đến quan hệ đó ở nước ngoài.'
- 'Giải thích từ ngữ
Trong Luật này, các từ ngữ dưới đây được hiểu như sau:
1. Hôn nhân là quan hệ giữa vợ và chồng sau khi kết hôn.
2. Gia đình là tập hợp những người gắn bó với nhau do hôn nhân, quan hệ huyết
thống hoặc quan hệ nuôi dưỡng, làm phát sinh các quyền và nghĩa vụ giữa họ với
nhau theo quy định của Luật này.
3. Chế độ hôn nhân và gia đình là toàn bộ những quy định của pháp luật về kết
hôn, ly hôn; quyền và nghĩa vụ giữa vợ và chồng, giữa cha mẹ và con, giữa các
thành viên khác trong gia đình; cấp dưỡng; xác định cha, mẹ, con; quan hệ hôn
nhân và gia đình có yếu tố nước ngoài và những vấn đề khác liên quan đến hôn nhân
và gia đình.
4. Tập quán về hôn nhân và gia đình là quy tắc xử sự có nội dung rõ ràng về quyền,
nghĩa vụ của các bên trong quan hệ hôn nhân và gia đình, được lặp đi, lặp lại
trong một thời gian dài và được thừa nhận rộng rãi trong một vùng, miền hoặc cộng
đồng.
5. Kết hôn là việc nam và nữ xác lập quan hệ vợ chồng với nhau theo quy định của
Luật này về điều kiện kết hôn và đăng ký kết hôn.
6. Kết hôn trái pháp luật là việc nam, nữ đã đăng ký kết hôn tại cơ quan nhà nước
có thẩm quyền nhưng một bên hoặc cả hai bên vi phạm điều kiện kết hôn theo quy
định tại Điều 8 của Luật này.
7. Chung sống như vợ chồng là việc nam, nữ tổ chức cuộc sống chung và coi nhau
là vợ chồng.
8. Tảo hôn là việc lấy vợ, lấy chồng khi một bên hoặc cả hai bên chưa đủ tuổi
kết hôn theo quy định tại điểm a khoản 1 Điều 8 của Luật này.
9. Cưỡng ép kết hôn, ly hôn là việc đe dọa, uy hiếp tinh thần, hành hạ, ngược
đãi, yêu sách của cải hoặc hành vi khác để buộc người khác phải kết hôn hoặc ly
hôn trái với ý muốn của họ.
10. Cản trở kết hôn, ly hôn là việc đe dọa, uy hiếp tinh thần, hành hạ, ngược
đãi, yêu sách của cải hoặc hành vi khác để ngăn cản việc kết hôn của người có
đủ điều kiện kết hôn theo quy định của Luật này hoặc buộc người khác phải duy
trì quan hệ hôn nhân trái với ý muốn của họ.
11. Kết hôn giả tạo là việc lợi dụng kết hôn để xuất cảnh, nhập cảnh, cư trú,
nhập quốc tịch Việt Nam, quốc tịch nước ngoài; hưởng chế độ ưu đãi của Nhà nước
hoặc để đạt được mục đích khác mà không nhằm mục đích xây dựng gia đình.
12. Yêu sách của cải trong kết hôn là việc đòi hỏi về vật chất một cách quá đáng
và coi đó là điều kiện để kết hôn nhằm cản trở việc kết hôn tự nguyện của nam,
nữ.
13. Thời kỳ hôn nhân là khoảng thời gian tồn tại quan hệ vợ chồng, được tính từ
ngày đăng ký kết hôn đến ngày chấm dứt hôn nhân.
14. Ly hôn là việc chấm dứt quan hệ vợ chồng theo bản án, quyết định có hiệu lực
pháp luật của Tòa án.
15. Ly hôn giả tạo là việc lợi dụng ly hôn để trốn tránh nghĩa vụ tài sản, vi
phạm chính sách, pháp luật về dân số hoặc để đạt được mục đích khác mà không nhằm
mục đích chấm dứt hôn nhân.
16. Thành viên gia đình bao gồm vợ, chồng; cha mẹ đẻ, cha mẹ nuôi, cha dượng,
mẹ kế, cha mẹ vợ, cha mẹ chồng; con đẻ, con nuôi, con riêng của vợ hoặc chồng,
con dâu, con rể; anh, chị, em cùng cha mẹ, anh, chị, em cùng cha khác mẹ, anh,
chị, em cùng mẹ khác cha, anh rể, em rể, chị dâu, em dâu của người cùng cha mẹ
hoặc cùng cha khác mẹ, cùng mẹ khác cha; ông bà nội, ông bà ngoại; cháu nội, cháu
ngoại; cô, dì, chú, cậu, bác ruột và cháu ruột.
17. Những người cùng dòng máu về trực hệ là những người có quan hệ huyết thống,
trong đó, người này sinh ra người kia kế tiếp nhau.
18. Những người có họ trong phạm vi ba đời là những người cùng một gốc sinh ra
gồm cha mẹ là đời thứ nhất; anh, chị, em cùng cha mẹ, cùng cha khác mẹ, cùng mẹ
khác cha là đời thứ hai; anh, chị, em con chú, con bác, con cô, con cậu, con dì
là đời thứ ba.
19. Người thân thích là người có quan hệ hôn nhân, nuôi dưỡng, người có cùng dòng
máu về trực hệ và người có họ trong phạm vi ba đời.
20. Nhu cầu thiết yếu là nhu cầu sinh hoạt thông thường về ăn, mặc, ở, học tập,
khám bệnh, chữa bệnh và nhu cầu sinh hoạt thông thường khác không thể thiếu cho
cuộc sống của mỗi người, mỗi gia đình.
21. Sinh con bằng kỹ thuật hỗ trợ sinh sản là việc sinh con bằng kỹ thuật thụ
tinh nhân tạo hoặc thụ tinh trong ống nghiệm.
22. Mang thai hộ vì mục đích nhân đạo là việc một người phụ nữ tự nguyện, không
vì mục đích thương mại giúp mang thai cho cặp vợ chồng mà người vợ không thể mang
thai và sinh con ngay cả khi áp dụng kỹ thuật hỗ trợ sinh sản, bằng việc lấy noãn
của người vợ và tinh trùng của người chồng để thụ tinh trong ống nghiệm, sau đó
cấy vào tử cung của người phụ nữ tự nguyện mang thai để người này mang thai và
sinh con.
23. Mang thai hộ vì mục đích thương mại là việc một người phụ nữ mang thai cho
người khác bằng việc áp dụng kỹ thuật hỗ trợ sinh sản để được hưởng lợi về kinh
tế hoặc lợi ích khác.
24. Cấp dưỡng là việc một người có nghĩa vụ đóng góp tiền hoặc tài sản khác để
đáp ứng nhu cầu thiết yếu của người không sống chung với mình mà có quan hệ hôn
nhân, huyết thống hoặc nuôi dưỡng trong trường hợp người đó là người chưa thành
niên, người đã thành niên mà không có khả năng lao động và không có tài sản để
tự nuôi mình hoặc người gặp khó khăn, túng thiếu theo quy định của Luật này.
25. Quan hệ hôn nhân và gia đình có yếu tố nước ngoài là quan hệ hôn nhân và gia
đình mà ít nhất một bên tham gia là người nước ngoài, người Việt Nam định cư ở
nước ngoài; quan hệ hôn nhân và gia đình giữa các bên tham gia là công dân Việt
Nam nhưng căn cứ để xác lập, thay đổi, chấm dứt quan hệ đó theo pháp luật nước
ngoài, phát sinh tại nước ngoài hoặc tài sản liên quan đến quan hệ đó ở nước ngoài.'
- source_sentence: Hồ sơ đăng ký thường trú của công dân có chỗ ở hợp pháp thuộc quyền
sở hữu của mình bao gồm những gì?
sentences:
- 'Nơi cư trú của người học tập, công tác, làm việc trong lực lượng vũ trang
1. Nơi cư trú của sĩ quan, hạ sĩ quan, binh sĩ, quân nhân chuyên nghiệp, công
nhân, viên chức quốc phòng, sinh viên, học viên các trường Quân đội nhân dân là
nơi đơn vị của người đó đóng quân, trừ trường hợp sĩ quan, hạ sĩ quan, binh sĩ,
quân nhân chuyên nghiệp, công nhân, viên chức quốc phòng có nơi cư trú khác theo
quy định của Luật này.
2. Nơi cư trú của sĩ quan nghiệp vụ, hạ sĩ quan nghiệp vụ, sĩ quan chuyên môn
kỹ thuật, hạ sĩ quan chuyên môn kỹ thuật, học sinh, sinh viên, học viên các trường
Công an nhân dân, hạ sĩ quan nghĩa vụ, chiến sĩ nghĩa vụ, công nhân công an là
nơi đơn vị của người đó đóng quân, trừ trường hợp sĩ quan nghiệp vụ, hạ sĩ quan
nghiệp vụ, sĩ quan chuyên môn kỹ thuật, hạ sĩ quan chuyên môn kỹ thuật, công nhân
công an có nơi cư trú khác theo quy định của Luật này.
3. Điều kiện, hồ sơ, thủ tục đăng ký thường trú, đăng ký tạm trú tại nơi đơn vị
đóng quân đối với người quy định tại khoản 1 Điều này do Bộ trưởng Bộ Quốc phòng
quy định, đối với người quy định tại khoản 2 Điều này do Bộ trưởng Bộ Công an
quy định.'
- 'Hồ sơ đăng ký thường trú
1. Hồ sơ đăng ký thường trú đối với người quy định tại khoản 1 Điều 20 của Luật
này bao gồm:
a) Tờ khai thay đổi thông tin cư trú;
b) Giấy tờ, tài liệu chứng minh việc sở hữu chỗ ở hợp pháp.
2. Hồ sơ đăng ký thường trú đối với người quy định tại khoản 2 Điều 20 của Luật
này bao gồm:
a) Tờ khai thay đổi thông tin cư trú, trong đó ghi rõ ý kiến đồng ý cho đăng ký
thường trú của chủ hộ, chủ sở hữu chỗ ở hợp pháp hoặc người được ủy quyền, trừ
trường hợp đã có ý kiến đồng ý bằng văn bản;
b) Giấy tờ, tài liệu chứng minh quan hệ nhân thân với chủ hộ, thành viên hộ gia
đình, trừ trường hợp đã có thông tin thể hiện quan hệ này trong Cơ sở dữ liệu
quốc gia về dân cư, Cơ sở dữ liệu về cư trú;
c) Giấy tờ, tài liệu chứng minh các điều kiện khác quy định tại điểm b, điểm
c khoản 2 Điều 20 của Luật này.
3. Hồ sơ đăng ký thường trú đối với người quy định tại khoản 3 Điều 20 của Luật
này bao gồm:
a) Tờ khai thay đổi thông tin cư trú, trong đó ghi rõ ý kiến đồng ý cho đăng ký
thường trú của chủ hộ, chủ sở hữu chỗ ở hợp pháp được cho thuê, cho mượn, cho
ở nhờ hoặc người được ủy quyền, trừ trường hợp đã có ý kiến đồng ý bằng văn bản;
b) Hợp đồng cho thuê, cho mượn, cho ở nhờ hoặc văn bản về việc cho mượn, cho ở
nhờ chỗ ở hợp pháp đã được công chứng hoặc chứng thực theo quy định của pháp luật;
c) Giấy tờ, tài liệu chứng minh đủ diện tích nhà ở để đăng ký thường trú theo
quy định.
4. Hồ sơ đăng ký thường trú đối với người quy định tại các điểm a, b và c khoản
4 Điều 20 của Luật này bao gồm:
a) Tờ khai thay đổi thông tin cư trú; đối với người quy định tại điểm c khoản
4 Điều 20 của Luật này thì trong tờ khai phải ghi rõ ý kiến đồng ý cho đăng ký
thường trú của người đại diện hoặc ban quản lý cơ sở tín ngưỡng, trừ trường hợp
đã có ý kiến đồng ý bằng văn bản;
b) Giấy tờ, tài liệu chứng minh là nhà tu hành, chức sắc, chức việc hoặc người
khác hoạt động tôn giáo và được hoạt động tại cơ sở tôn giáo đó theo quy định
của pháp luật về tín ngưỡng, tôn giáo đối với người quy định tại điểm a khoản
4 Điều 20 của Luật này; giấy tờ, tài liệu chứng minh là người đại diện cơ sở tín
ngưỡng đối với người quy định tại điểm b khoản 4 Điều 20 của Luật này;
c) Văn bản xác nhận của Ủy ban nhân dân cấp xã về việc trong cơ sở tín ngưỡng,
cơ sở tôn giáo có công trình phụ trợ là nhà ở.
5. Hồ sơ đăng ký thường trú đối với người quy định tại điểm d khoản 4 Điều 20
của Luật này bao gồm:
a) Tờ khai thay đổi thông tin cư trú, trong đó ghi rõ ý kiến đồng ý cho đăng ký
thường trú của người đại diện hoặc ban quản lý cơ sở tín ngưỡng hay người đứng
đầu hoặc người đại diện cơ sở tôn giáo, trừ trường hợp đã có ý kiến đồng ý bằng
văn bản;
b) Văn bản xác nhận của Ủy ban nhân dân cấp xã về việc người đăng ký thường trú
thuộc đối tượng quy định tại khoản 2 Điều 17 của Luật này và việc trong cơ sở
tín ngưỡng, cơ sở tôn giáo có công trình phụ trợ là nhà ở.
6. Hồ sơ đăng ký thường trú đối với người quy định tại khoản 5 Điều 20 của Luật
này bao gồm:
a) Tờ khai thay đổi thông tin cư trú; đối với người được cá nhân, hộ gia đình
nhận chăm sóc, nuôi dưỡng thì trong tờ khai phải ghi rõ ý kiến đồng ý cho đăng
ký thường trú của chủ hộ nhận chăm sóc, nuôi dưỡng, chủ sở hữu chỗ ở hợp pháp
của cá nhân, hộ gia đình nhận chăm sóc, nuôi dưỡng hoặc người được ủy quyền, trừ
trường hợp đã có ý kiến đồng ý bằng văn bản;
b) Văn bản đề nghị của người đứng đầu cơ sở trợ giúp xã hội đối với người được
cơ sở trợ giúp xã hội nhận chăm sóc, nuôi dưỡng, trợ giúp;
c) Giấy tờ, tài liệu xác nhận về việc chăm sóc, nuôi dưỡng, trợ giúp.
7. Hồ sơ đăng ký thường trú đối với người quy định tại khoản 6 Điều 20 của Luật
này bao gồm:
a) Tờ khai thay đổi thông tin cư trú; đối với người đăng ký thường trú không phải
là chủ phương tiện thì trong tờ khai phải ghi rõ ý kiến đồng ý cho đăng ký thường
trú của chủ phương tiện hoặc người được ủy quyền, trừ trường hợp đã có ý kiến
đồng ý bằng văn bản;
b) Giấy chứng nhận đăng ký phương tiện và giấy chứng nhận an toàn kỹ thuật và
bảo vệ môi trường của phương tiện hoặc văn bản xác nhận của Ủy ban nhân dân cấp
xã về việc sử dụng phương tiện đó vào mục đích để ở đối với phương tiện không
thuộc đối tượng phải đăng ký, đăng kiểm;
c) Văn bản xác nhận của Ủy ban nhân dân cấp xã về địa điểm phương tiện đăng ký
đậu, đỗ thường xuyên trong trường hợp phương tiện không phải đăng ký hoặc nơi
đăng ký phương tiện không trùng với nơi thường xuyên đậu, đỗ.
8. Trường hợp người đăng ký thường trú quy định tại các khoản 1, 2, 3, 4, 5, 6
và 7 Điều này là người chưa thành niên thì trong tờ khai thay đổi thông tin cư
trú phải ghi rõ ý kiến đồng ý của cha, mẹ hoặc người giám hộ, trừ trường hợp đã
có ý kiến đồng ý bằng văn bản.
9. Trường hợp người đăng ký thường trú quy định tại các khoản 1, 2, 3, 4, 5, 6
và 7 Điều này là người Việt Nam định cư ở nước ngoài còn quốc tịch Việt Nam thì
trong hồ sơ đăng ký thường trú phải có hộ chiếu Việt Nam còn giá trị sử dụng;
trường hợp không có hộ chiếu Việt Nam còn giá trị sử dụng thì phải có giấy tờ,
tài liệu khác chứng minh có quốc tịch Việt Nam và văn bản đồng ý cho giải quyết
thường trú của cơ quan quản lý xuất, nhập cảnh của Bộ Công an.
10. Chính phủ quy định chi tiết về các loại giấy tờ, tài liệu chứng minh chỗ ở
hợp pháp và giấy tờ, tài liệu chứng minh quan hệ nhân thân quy định tại Điều này.'
- 'Luật áp dụng giải quyết tranh chấp
1. Đối với tranh chấp không có yếu tố nước ngoài, Hội đồng trọng tài áp dụng pháp
luật Việt Nam để giải quyết tranh chấp.
2. Đối với tranh chấp có yếu tố nước ngoài, Hội đồng trọng tài áp dụng pháp luật
do các bên lựa chọn; nếu các bên không có thỏa thuận về luật áp dụng thì Hội đồng
trọng tài quyết định áp dụng pháp luật mà Hội đồng trọng tài cho là phù hợp nhất.
3. Trường hợp pháp luật Việt Nam, pháp luật do các bên lựa chọn không có quy định
cụ thể liên quan đến nội dung tranh chấp thì Hội đồng trọng tài được áp dụng tập
quán quốc tế để giải quyết tranh chấp nếu việc áp dụng hoặc hậu quả của việc áp
dụng đó không trái với các nguyên tắc cơ bản của pháp luật Việt Nam.'
- source_sentence: Đối với tài sản đang xảy ra tranh chấp, nếu không có căn cứ chứng
minh được đó là tài sản riêng của vợ hay chồng, thì tài sản đó coi là tài sản
chung, đúng hay sai?
sentences:
- 'Xác định cha, mẹ trong trường hợp sinh con bằng kỹ thuật hỗ trợ sinh sản
1. Trong trường hợp người vợ sinh con bằng kỹ thuật hỗ trợ sinh sản thì việc xác
định cha, mẹ được áp dụng theo quy định tại Điều 88 của Luật này.
2. Trong trường hợp người phụ nữ sống độc thân sinh con bằng kỹ thuật hỗ trợ sinh
sản thì người phụ nữ đó là mẹ của con được sinh ra.
3. Việc sinh con bằng kỹ thuật hỗ trợ sinh sản không làm phát sinh quan hệ cha,
mẹ và con giữa người cho tinh trùng, cho noãn, cho phôi với người con được sinh
ra.
4. Việc xác định cha, mẹ trong trường hợp mang thai hộ vì mục đích nhân đạo được
áp dụng theo quy định tại Điều 94 của Luật này.'
- 'Hệ thống giáo dục quốc dân
1. Hệ thống giáo dục quốc dân là hệ thống giáo dục mở, liên thông gồm giáo dục
chính quy và giáo dục thường xuyên.
2. Cấp học, trình độ đào tạo của hệ thống giáo dục quốc dân bao gồm:
a) Giáo dục mầm non gồm giáo dục nhà trẻ và giáo dục mẫu giáo;
b) Giáo dục phổ thông gồm giáo dục tiểu học, giáo dục trung học cơ sở và giáo
dục trung học phổ thông;
c) Giáo dục nghề nghiệp đào tạo trình độ sơ cấp, trình độ trung cấp, trình độ
cao đẳng và các chương trình đào tạo nghề nghiệp khác;
d) Giáo dục đại học đào tạo trình độ đại học, trình độ thạc sĩ và trình độ tiến
sĩ.
3. Thủ tướng Chính phủ quyết định phê duyệt Khung cơ cấu hệ thống giáo dục quốc
dân và Khung trình độ quốc gia Việt Nam; quy định thời gian đào tạo, tiêu chuẩn
cho từng trình độ đào tạo, khối lượng học tập tối thiểu đối với trình độ của giáo
dục nghề nghiệp, giáo dục đại học.
4. Bộ trưởng Bộ Giáo dục và Đào tạo, Bộ trưởng Bộ Lao động - Thương binh và Xã
hội, trong phạm vi nhiệm vụ, quyền hạn của mình, quy định ngưỡng đầu vào trình
độ cao đẳng, trình độ đại học thuộc ngành đào tạo giáo viên và ngành thuộc lĩnh
vực sức khỏe.'
- 'Tài sản chung của vợ chồng
1. Tài sản chung của vợ chồng gồm tài sản do vợ, chồng tạo ra, thu nhập do lao
động, hoạt động sản xuất, kinh doanh, hoa lợi, lợi tức phát sinh từ tài sản riêng
và thu nhập hợp pháp khác trong thời kỳ hôn nhân, trừ trường hợp được quy định
tại khoản 1 Điều 40 của Luật này; tài sản mà vợ chồng được thừa kế chung hoặc
được tặng cho chung và tài sản khác mà vợ chồng thỏa thuận là tài sản chung.
Quyền sử dụng đất mà vợ, chồng có được sau khi kết hôn là tài sản chung của vợ
chồng, trừ trường hợp vợ hoặc chồng được thừa kế riêng, được tặng cho riêng hoặc
có được thông qua giao dịch bằng tài sản riêng.
2. Tài sản chung của vợ chồng thuộc sở hữu chung hợp nhất, được dùng để bảo đảm
nhu cầu của gia đình, thực hiện nghĩa vụ chung của vợ chồng.
3. Trong trường hợp không có căn cứ để chứng minh tài sản mà vợ, chồng đang có
tranh chấp là tài sản riêng của mỗi bên thì tài sản đó được coi là tài sản chung.'
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on minhquan6203/paraphrase-vietnamese-law
results:
- task:
type: triplet
name: Triplet
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
- type: dot_accuracy
value: 0.0
name: Dot Accuracy
- type: manhattan_accuracy
value: 1.0
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 1.0
name: Euclidean Accuracy
- type: max_accuracy
value: 1.0
name: Max Accuracy
---
# SentenceTransformer based on minhquan6203/paraphrase-vietnamese-law
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [minhquan6203/paraphrase-vietnamese-law](https://huggingface.co/minhquan6203/paraphrase-vietnamese-law). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [minhquan6203/paraphrase-vietnamese-law](https://huggingface.co/minhquan6203/paraphrase-vietnamese-law) <!-- at revision 5bb1e531c07495496d463e67b3c0dacadd738d02 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("huyhuy123/paraphrase-vietnamese-law-ALQAC")
# Run inference
sentences = [
'Đối với tài sản đang xảy ra tranh chấp, nếu không có căn cứ chứng minh được đó là tài sản riêng của vợ hay chồng, thì tài sản đó coi là tài sản chung, đúng hay sai?',
'Tài sản chung của vợ chồng\n1. Tài sản chung của vợ chồng gồm tài sản do vợ, chồng tạo ra, thu nhập do lao động, hoạt động sản xuất, kinh doanh, hoa lợi, lợi tức phát sinh từ tài sản riêng và thu nhập hợp pháp khác trong thời kỳ hôn nhân, trừ trường hợp được quy định tại khoản 1 Điều 40 của Luật này; tài sản mà vợ chồng được thừa kế chung hoặc được tặng cho chung và tài sản khác mà vợ chồng thỏa thuận là tài sản chung.\n\nQuyền sử dụng đất mà vợ, chồng có được sau khi kết hôn là tài sản chung của vợ chồng, trừ trường hợp vợ hoặc chồng được thừa kế riêng, được tặng cho riêng hoặc có được thông qua giao dịch bằng tài sản riêng.\n\n2. Tài sản chung của vợ chồng thuộc sở hữu chung hợp nhất, được dùng để bảo đảm nhu cầu của gia đình, thực hiện nghĩa vụ chung của vợ chồng.\n\n3. Trong trường hợp không có căn cứ để chứng minh tài sản mà vợ, chồng đang có tranh chấp là tài sản riêng của mỗi bên thì tài sản đó được coi là tài sản chung.',
'Xác định cha, mẹ trong trường hợp sinh con bằng kỹ thuật hỗ trợ sinh sản\n1. Trong trường hợp người vợ sinh con bằng kỹ thuật hỗ trợ sinh sản thì việc xác định cha, mẹ được áp dụng theo quy định tại Điều 88 của Luật này.\n\n2. Trong trường hợp người phụ nữ sống độc thân sinh con bằng kỹ thuật hỗ trợ sinh sản thì người phụ nữ đó là mẹ của con được sinh ra.\n\n3. Việc sinh con bằng kỹ thuật hỗ trợ sinh sản không làm phát sinh quan hệ cha, mẹ và con giữa người cho tinh trùng, cho noãn, cho phôi với người con được sinh ra.\n\n4. Việc xác định cha, mẹ trong trường hợp mang thai hộ vì mục đích nhân đạo được áp dụng theo quy định tại Điều 94 của Luật này.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:--------|
| cosine_accuracy | 1.0 |
| dot_accuracy | 0.0 |
| manhattan_accuracy | 1.0 |
| euclidean_accuracy | 1.0 |
| **max_accuracy** | **1.0** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 7,696 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 30.0 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 182.51 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 163.05 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Trường hợp thông tin trong Sổ hộ khẩu còn hiệu lực có thông tin khác với thông tin trong Cơ sở dữ liệu về cư trú thì sử dụng thông tin trong Cơ sở dữ liệu về cư trú, đúng hay sai?</code> | <code>Điều khoản thi hành<br><br>1. Luật này có hiệu lực thi hành từ ngày 01 tháng 7 năm 2021.<br><br>2. Luật Cư trú số 81/2006/QH11 đã được sửa đổi, bổ sung một số điều theo Luật số 36/2013/QH13 hết hiệu lực kể từ ngày Luật này có hiệu lực thi hành.<br><br>3. Kể từ ngày Luật này có hiệu lực thi hành, Sổ hộ khẩu, Sổ tạm trú đã được cấp vẫn được sử dụng và có giá trị như giấy tờ, tài liệu xác nhận về cư trú theo quy định của Luật này cho đến hết ngày 31 tháng 12 năm 2022.<br><br>Trường hợp thông tin trong Sổ hộ khẩu, Sổ tạm trú khác với thông tin trong Cơ sở dữ liệu về cư trú thì sử dụng thông tin trong Cơ sở dữ liệu về cư trú.<br><br>Khi công dân thực hiện các thủ tục đăng ký cư trú dẫn đến thay đổi thông tin trong Sổ hộ khẩu, Sổ tạm trú thì cơ quan đăng ký cư trú có trách nhiệm thu hồi Sổ hộ khẩu, Sổ tạm trú đã cấp, thực hiện điều chỉnh, cập nhật thông tin trong Cơ sở dữ liệu về cư trú theo quy định của Luật này và không cấp mới, cấp lại Sổ hộ khẩu, Sổ tạm trú.<br><br>4. Chính phủ, Bộ, cơ quan ngang Bộ và cơ quan khác có liên quan rà soát các văn bản quy phạm pháp luật thuộc thẩm quyền ban hành có nội dung quy định liên quan đến Sổ hộ khẩu, Sổ tạm trú hoặc có yêu cầu xuất trình giấy tờ, tài liệu xác nhận về cư trú để sửa đổi, bổ sung cho phù hợp với quy định của Luật này, hạn chế việc sử dụng thông tin về nơi cư trú là điều kiện để thực hiện các thủ tục hành chính.<br><br>Luật này được Quốc hội nước Cộng hòa xã hội chủ nghĩa Việt Nam khóa XIV, kỳ họp thứ 10 thông qua ngày 13 tháng 11 năm 2020.<br><br>NỮ CHỦ TỊCH QUỐC HỘI<br><br>(Đã ký)<br><br><br>Nguyễn Thị Kim Ngân</code> | <code>Thủ tục đăng ký thường trú<br><br>1. Người đăng ký thường trú nộp hồ sơ đăng ký thường trú đến cơ quan đăng ký cư trú nơi mình cư trú.<br><br>2. Khi tiếp nhận hồ sơ đăng ký thường trú, cơ quan đăng ký cư trú kiểm tra và cấp phiếu tiếp nhận hồ sơ cho người đăng ký; trường hợp hồ sơ chưa đầy đủ thì hướng dẫn người đăng ký bổ sung hồ sơ.<br><br>3. Trong thời hạn 07 ngày làm việc kể từ ngày nhận được hồ sơ đầy đủ và hợp lệ, cơ quan đăng ký cư trú có trách nhiệm thẩm định, cập nhật thông tin về nơi thường trú mới của người đăng ký vào Cơ sở dữ liệu về cư trú và thông báo cho người đăng ký về việc đã cập nhật thông tin đăng ký thường trú; trường hợp từ chối đăng ký thì phải trả lời bằng văn bản và nêu rõ lý do.<br><br>4. Người đã đăng ký thường trú mà chuyển đến chỗ ở hợp pháp khác và đủ điều kiện đăng ký thường trú thì có trách nhiệm đăng ký thường trú tại nơi ở mới theo quy định của Luật này trong thời hạn 12 tháng kể từ ngày đủ điều kiện đăng ký.</code> |
| <code>Cha mẹ KHÔNG có nghĩa vụ cùng nhau chăm sóc, nuôi dưỡng con khi nào </code> | <code>Nghĩa vụ và quyền chăm sóc, nuôi dưỡng<br>1. Cha, mẹ có nghĩa vụ và quyền ngang nhau, cùng nhau chăm sóc, nuôi dưỡng con chưa thành niên, con đã thành niên mất năng lực hành vi dân sự hoặc không có khả năng lao động và không có tài sản để tự nuôi mình.<br><br>2. Con có nghĩa vụ và quyền chăm sóc, nuôi dưỡng cha mẹ, đặc biệt khi cha mẹ mất năng lực hành vi dân sự, ốm đau, già yếu, khuyết tật; trường hợp gia đình có nhiều con thì các con phải cùng nhau chăm sóc, nuôi dưỡng cha mẹ.</code> | <code>Áp dụng pháp luật đối với quan hệ hôn nhân và gia đình có yếu tố nước ngoài<br>1. Các quy định của pháp luật về hôn nhân và gia đình của nước Cộng hòa xã hội chủ nghĩa Việt Nam được áp dụng đối với quan hệ hôn nhân và gia đình có yếu tố nước ngoài, trừ trường hợp Luật này có quy định khác.<br><br>Trong trường hợp điều ước quốc tế mà Cộng hòa xã hội chủ nghĩa Việt Nam là thành viên có quy định khác với quy định của Luật này thì áp dụng quy định của điều ước quốc tế đó.<br><br>2. Trong trường hợp Luật này, các văn bản pháp luật khác của Việt Nam có dẫn chiếu về việc áp dụng pháp luật nước ngoài thì pháp luật nước ngoài được áp dụng, nếu việc áp dụng đó không trái với các nguyên tắc cơ bản được quy định tại Điều 2 của Luật này.<br><br>Trong trường hợp pháp luật nước ngoài dẫn chiếu trở lại pháp luật Việt Nam thì áp dụng pháp luật về hôn nhân và gia đình Việt Nam.<br><br>3. Trong trường hợp điều ước quốc tế mà Cộng hòa xã hội chủ nghĩa Việt Nam là thành viên có dẫn chiếu về việc áp dụng pháp luật nước ngoài thì pháp luật nước ngoài được áp dụng.</code> |
| <code>Ai là người giữ vai trò quan trọng trong việc quản lý, tổ chức, và điều hành các hoạt động giáo dục?</code> | <code>Vai trò và trách nhiệm của cán bộ quản lý giáo dục<br><br>1. Cán bộ quản lý giáo dục giữ vai trò quan trọng trong việc tổ chức, quản lý, điều hành các hoạt động giáo dục.<br><br>2. Cán bộ quản lý giáo dục có trách nhiệm học tập, rèn luyện, nâng cao phẩm chất đạo đức, trình độ chuyên môn, năng lực quản lý và thực hiện các chuẩn, quy chuẩn theo quy định của pháp luật.<br><br>3. Nhà nước có kế hoạch xây dựng và nâng cao chất lượng đội ngũ cán bộ quản lý giáo dục.</code> | <code>Phong tặng danh hiệu Nhà giáo nhân dân, Nhà giáo ưu tú<br><br>Nhà giáo, cán bộ quản lý giáo dục, cán bộ nghiên cứu giáo dục đủ tiêu chuẩn theo quy định của pháp luật thì được Nhà nước phong tặng danh hiệu Nhà giáo nhân dân, Nhà giáo ưu tú.</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 2
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | max_accuracy |
|:------:|:----:|:-------------:|:------------:|
| 1.0 | 481 | - | 0.9994 |
| 1.0395 | 500 | 0.3398 | 0.9994 |
| 2.0 | 962 | - | 1.0 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.2
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
Dhahlan2000/Simple_Translation-model-for-GPT-v6
|
Dhahlan2000
| 2024-06-07T14:03:02Z | 113 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Dhahlan2000/Simple_Translation-model-for-GPT-v5",
"base_model:finetune:Dhahlan2000/Simple_Translation-model-for-GPT-v5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-06-07T12:05:59Z |
---
base_model: Dhahlan2000/Simple_Translation-model-for-GPT-v5
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Simple_Translation-model-for-GPT-v6
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. -->
# Simple_Translation-model-for-GPT-v6
This model is a fine-tuned version of [Dhahlan2000/Simple_Translation-model-for-GPT-v5](https://huggingface.co/Dhahlan2000/Simple_Translation-model-for-GPT-v5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7266
- Bleu: 8.8015
- Gen Len: 18.5845
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 1.0639 | 1.0 | 6188 | 0.7879 | 8.263 | 18.5995 |
| 0.9675 | 2.0 | 12376 | 0.7266 | 8.8015 | 18.5845 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.