modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 18:33:19
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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jhakaran1/bert-essay-concat
|
jhakaran1
| 2022-10-29T00:00:25Z | 156 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-28T02:20:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-essay-concat
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-essay-concat
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0735
- Accuracy: 0.6331
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7024 | 1.0 | 3677 | 0.9159 | 0.6329 |
| 0.6413 | 2.0 | 7354 | 1.0267 | 0.6346 |
| 0.5793 | 3.0 | 11031 | 1.0735 | 0.6331 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
hakurei/bloom-1b1-arb-thesis
|
hakurei
| 2022-10-28T22:35:44Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-14T16:02:00Z |
---
license: bigscience-bloom-rail-1.0
---
|
christyli/vit-base-beans
|
christyli
| 2022-10-28T21:59:17Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-28T21:55:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-beans
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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3930
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0349 | 1.0 | 17 | 0.8167 | 0.9323 |
| 0.7502 | 2.0 | 34 | 0.6188 | 0.9699 |
| 0.5508 | 3.0 | 51 | 0.4856 | 0.9774 |
| 0.4956 | 4.0 | 68 | 0.4109 | 0.9774 |
| 0.4261 | 5.0 | 85 | 0.3930 | 0.9774 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Tokenizers 0.12.1
|
sd-concepts-library/urivoldemort
|
sd-concepts-library
| 2022-10-28T20:58:35Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-28T19:36:37Z |
---
license: mit
---
### Urivoldemort on Stable Diffusion
Create Uriboldemort images using any context. This was taught to Stable Diffusion via Textual Inversion. Use the `<uriboldemort>` placeholder in the text prompt. You can train your Concept using the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. For inference use this copy of the official [notebook](https://colab.research.google.com/drive/11bIGXVkQJ4bJTSQIjlxDg1OhhX7nDZ01?usp=sharing).
Some outputs:
|
sd-concepts-library/anime-background-style-v2
|
sd-concepts-library
| 2022-10-28T19:56:39Z | 0 | 24 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-28T19:45:11Z |
---
license: mit
---
### Anime Background style (v2) on Stable Diffusion
This is the `<anime-background-style-v2>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
Here are images generated with this style:
|
kyle-lucke/autotrain-planes-1918465011
|
kyle-lucke
| 2022-10-28T19:42:45Z | 3 | 0 |
transformers
|
[
"transformers",
"joblib",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:kyle-lucke/autotrain-data-planes",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
tabular-classification
| 2022-10-28T19:42:12Z |
---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- kyle-lucke/autotrain-data-planes
co2_eq_emissions:
emissions: 0.19811345350195664
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1918465011
- CO2 Emissions (in grams): 0.1981
## Validation Metrics
- Loss: 0.011
- Accuracy: 0.997
- Macro F1: 0.916
- Micro F1: 0.997
- Weighted F1: 0.996
- Macro Precision: 0.999
- Micro Precision: 0.997
- Weighted Precision: 0.997
- Macro Recall: 0.867
- Micro Recall: 0.997
- Weighted Recall: 0.997
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
```
|
hsuvaskakoty/bart_def_gen_40k
|
hsuvaskakoty
| 2022-10-28T19:18:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-26T17:53:02Z |
This is a fine-tuned BART model for Definition Generation. It is still in the prototype stage, fine-tuned only with 40k Training Instances of (definition, context) pairs for 3 epochs. The eval_loss is still in 2.30. The beam Size is 4.
|
ViktorDo/SciBERT-POWO_Lifecycle_Finetuned
|
ViktorDo
| 2022-10-28T19:12:38Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-28T18:06:36Z |
---
tags:
- generated_from_trainer
model-index:
- name: SciBERT-POWO_Lifecycle_Finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SciBERT-POWO_Lifecycle_Finetuned
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0812
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0899 | 1.0 | 1704 | 0.0795 |
| 0.0845 | 2.0 | 3408 | 0.0836 |
| 0.0684 | 3.0 | 5112 | 0.0812 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
leslyarun/grammatical-error-correction-quantized
|
leslyarun
| 2022-10-28T17:55:05Z | 14 | 1 |
transformers
|
[
"transformers",
"onnx",
"t5",
"text2text-generation",
"grammar",
"en",
"dataset:leslyarun/c4_200m_gec_train100k_test25k",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-28T13:10:29Z |
---
language: en
tags:
- grammar
- text2text-generation
datasets:
- leslyarun/c4_200m_gec_train100k_test25k
---
# Get Grammatical corrections on your English text, trained on a subset of c4-200m dataset - ONNX Quantized Model
# Use the below code for running the model
``` python
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSeq2SeqLM
from optimum.pipelines import pipeline
tokenizer = AutoTokenizer.from_pretrained("leslyarun/grammatical-error-correction-quantized")
model = ORTModelForSeq2SeqLM.from_pretrained("leslyarun/grammatical-error-correction-quantized",
encoder_file_name="encoder_model_quantized.onnx",
decoder_file_name="decoder_model_quantized.onnx",
decoder_with_past_file_name="decoder_with_past_model_quantized.onnx")
text2text_generator = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
output = text2text_generator("grammar: " + sentence)
print(output[0]["generated_text"])
```
|
ybelkada/switch-base-8-xsum
|
ybelkada
| 2022-10-28T17:54:45Z | 12 | 3 |
transformers
|
[
"transformers",
"pytorch",
"switch_transformers",
"text2text-generation",
"en",
"dataset:c4",
"dataset:xsum",
"arxiv:2101.03961",
"arxiv:2210.11416",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-28T13:29:07Z |
---
language:
- en
tags:
- text2text-generation
widget:
- text: "summarize: Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital. Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well. Therefore, Peter stayed with her at the hospital for 3 days without leaving."
example_title: "Summarization"
datasets:
- c4
- xsum
license: apache-2.0
---
# Model Card for Switch Transformers Base - 8 experts
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Training Details](#training-details)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmental-impact)
8. [Citation](#citation)
9. [Model Card Authors](#model-card-authors)
# TL;DR
Switch Transformers is a Mixture of Experts (MoE) model trained on Masked Language Modeling (MLM) task. The model architecture is similar to the classic T5, but with the Feed Forward layers replaced by the Sparse MLP layers containing "experts" MLP. According to the [original paper](https://arxiv.org/pdf/2101.03961.pdf) the model enables faster training (scaling properties) while being better than T5 on fine-tuned tasks.
As mentioned in the first few lines of the abstract :
> we advance the current scale of language models by pre-training up to trillion parameter models on the “Colossal Clean Crawled Corpus”, and achieve a 4x speedup over the T5-XXL model.
**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [original paper](https://arxiv.org/pdf/2101.03961.pdf).
# Model Details
## Model Description
- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=switch)
- **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#mixture-of-experts-moe-checkpoints)
- **Resources for more information:**
- [Research paper](https://arxiv.org/pdf/2101.03961.pdf)
- [GitHub Repo](https://github.com/google-research/t5x)
- [Hugging Face Switch Transformers Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/switch_transformers)
# Usage
Note that these checkpoints has been trained on Masked-Language Modeling (MLM) task. Therefore the checkpoints are not "ready-to-use" for downstream tasks. You may want to check `FLAN-T5` for running fine-tuned weights or fine-tune your own MoE following [this notebook](https://colab.research.google.com/drive/1aGGVHZmtKmcNBbAwa9hbu58DDpIuB5O4?usp=sharing)
Find below some example scripts on how to use the model in `transformers`:
## Using the Pytorch model
### Running the model on a CPU
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-8")
input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
>>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s>
```
</details>
### Running the model on a GPU
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto")
input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
>>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s>
```
</details>
### Running the model on a GPU using different precisions
#### FP16
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto", torch_dtype=torch.float16)
input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
>>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s>
```
</details>
#### INT8
<details>
<summary> Click to expand </summary>
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto")
input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
>>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s>
```
</details>
# Uses
## Direct Use and Downstream Use
The authors write in [the original paper's model card](https://arxiv.org/pdf/2210.11416.pdf) that:
> The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models
See the [research paper](https://arxiv.org/pdf/2210.11416.pdf) for further details.
## Out-of-Scope Use
More information needed.
# Bias, Risks, and Limitations
More information needed.
## Ethical considerations and risks
More information needed.
## Known Limitations
More information needed.
## Sensitive Use:
> SwitchTransformers should not be applied for any unacceptable use cases, e.g., generation of abusive speech.
# Training Details
## Training Data
The model was trained on a Masked Language Modeling task, on Colossal Clean Crawled Corpus (C4) dataset, following the same procedure as `T5`.
## Training Procedure
According to the model card from the [original paper](https://arxiv.org/pdf/2210.11416.pdf):
> These models are based on pretrained SwitchTransformers and are not fine-tuned. It is normal if they perform well on zero-shot tasks.
The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax).
# Evaluation
## Testing Data, Factors & Metrics
The authors evaluated the model on various tasks and compared the results against T5. See the table below for some quantitative evaluation:
For full details, please check the [research paper](https://arxiv.org/pdf/2101.03961.pdf).
## Results
For full results for Switch Transformers, see the [research paper](https://arxiv.org/pdf/2101.03961.pdf), Table 5.
# Environmental Impact
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:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4.
- **Hours used:** More information needed
- **Cloud Provider:** GCP
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Citation
**BibTeX:**
```bibtex
@misc{https://doi.org/10.48550/arxiv.2101.03961,
doi = {10.48550/ARXIV.2101.03961},
url = {https://arxiv.org/abs/2101.03961},
author = {Fedus, William and Zoph, Barret and Shazeer, Noam},
keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
ivanzidov/setfit-occupation
|
ivanzidov
| 2022-10-28T17:48:19Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-28T11:39:19Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 125000 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 125000,
"warmup_steps": 12500,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
leo93/all-15-bert-finetuned-ner
|
leo93
| 2022-10-28T17:47:37Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-28T01:42:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: all-15-bert-finetuned-ner
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-15-bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0081
- Precision: 0.9630
- Recall: 0.9661
- F1: 0.9646
- Accuracy: 0.9987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.014 | 1.0 | 6693 | 0.0080 | 0.9048 | 0.9363 | 0.9203 | 0.9976 |
| 0.007 | 2.0 | 13386 | 0.0070 | 0.9116 | 0.9459 | 0.9284 | 0.9976 |
| 0.0034 | 3.0 | 20079 | 0.0050 | 0.9514 | 0.9529 | 0.9522 | 0.9985 |
| 0.0027 | 4.0 | 26772 | 0.0065 | 0.9360 | 0.9618 | 0.9487 | 0.9982 |
| 0.002 | 5.0 | 33465 | 0.0062 | 0.9485 | 0.9555 | 0.9520 | 0.9984 |
| 0.0008 | 6.0 | 40158 | 0.0069 | 0.9498 | 0.9468 | 0.9483 | 0.9983 |
| 0.0013 | 7.0 | 46851 | 0.0059 | 0.9591 | 0.9618 | 0.9605 | 0.9987 |
| 0.0007 | 8.0 | 53544 | 0.0072 | 0.9635 | 0.9594 | 0.9614 | 0.9986 |
| 0.0003 | 9.0 | 60237 | 0.0076 | 0.9656 | 0.9621 | 0.9638 | 0.9987 |
| 0.0006 | 10.0 | 66930 | 0.0080 | 0.9598 | 0.9625 | 0.9611 | 0.9986 |
| 0.0007 | 11.0 | 73623 | 0.0072 | 0.9584 | 0.9651 | 0.9618 | 0.9986 |
| 0.0 | 12.0 | 80316 | 0.0073 | 0.9606 | 0.9658 | 0.9632 | 0.9987 |
| 0.0001 | 13.0 | 87009 | 0.0072 | 0.9649 | 0.9636 | 0.9642 | 0.9987 |
| 0.0 | 14.0 | 93702 | 0.0078 | 0.9629 | 0.9665 | 0.9647 | 0.9987 |
| 0.0 | 15.0 | 100395 | 0.0081 | 0.9630 | 0.9661 | 0.9646 | 0.9987 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
yubol/all-15-bert-finetuned-ner
|
yubol
| 2022-10-28T17:47:37Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-28T01:42:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: all-15-bert-finetuned-ner
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-15-bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0081
- Precision: 0.9630
- Recall: 0.9661
- F1: 0.9646
- Accuracy: 0.9987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.014 | 1.0 | 6693 | 0.0080 | 0.9048 | 0.9363 | 0.9203 | 0.9976 |
| 0.007 | 2.0 | 13386 | 0.0070 | 0.9116 | 0.9459 | 0.9284 | 0.9976 |
| 0.0034 | 3.0 | 20079 | 0.0050 | 0.9514 | 0.9529 | 0.9522 | 0.9985 |
| 0.0027 | 4.0 | 26772 | 0.0065 | 0.9360 | 0.9618 | 0.9487 | 0.9982 |
| 0.002 | 5.0 | 33465 | 0.0062 | 0.9485 | 0.9555 | 0.9520 | 0.9984 |
| 0.0008 | 6.0 | 40158 | 0.0069 | 0.9498 | 0.9468 | 0.9483 | 0.9983 |
| 0.0013 | 7.0 | 46851 | 0.0059 | 0.9591 | 0.9618 | 0.9605 | 0.9987 |
| 0.0007 | 8.0 | 53544 | 0.0072 | 0.9635 | 0.9594 | 0.9614 | 0.9986 |
| 0.0003 | 9.0 | 60237 | 0.0076 | 0.9656 | 0.9621 | 0.9638 | 0.9987 |
| 0.0006 | 10.0 | 66930 | 0.0080 | 0.9598 | 0.9625 | 0.9611 | 0.9986 |
| 0.0007 | 11.0 | 73623 | 0.0072 | 0.9584 | 0.9651 | 0.9618 | 0.9986 |
| 0.0 | 12.0 | 80316 | 0.0073 | 0.9606 | 0.9658 | 0.9632 | 0.9987 |
| 0.0001 | 13.0 | 87009 | 0.0072 | 0.9649 | 0.9636 | 0.9642 | 0.9987 |
| 0.0 | 14.0 | 93702 | 0.0078 | 0.9629 | 0.9665 | 0.9647 | 0.9987 |
| 0.0 | 15.0 | 100395 | 0.0081 | 0.9630 | 0.9661 | 0.9646 | 0.9987 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Arklyn/fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-test
|
Arklyn
| 2022-10-28T16:22:36Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-10-07T04:10:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_10_0
model-index:
- name: fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-test
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. -->
# fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-test
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3076
- Wer: 0.2971
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 7
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9436 | 9.99 | 570 | 2.7467 | 1.0 |
| 1.0498 | 19.99 | 1140 | 0.3630 | 0.3965 |
| 0.6789 | 29.99 | 1710 | 0.3396 | 0.3712 |
| 0.5259 | 39.99 | 2280 | 0.3204 | 0.3241 |
| 0.4701 | 49.99 | 2850 | 0.3118 | 0.3005 |
| 0.4248 | 59.99 | 3420 | 0.3076 | 0.2971 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
tlttl/tluo_xml_roberta_base_amazon_review_sentiment
|
tlttl
| 2022-10-28T15:51:48Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-28T07:26:12Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tluo_xml_roberta_base_amazon_review_sentiment
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. -->
# tluo_xml_roberta_base_amazon_review_sentiment
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: 0.9552
- Accuracy: 0.6003
## 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: 123
- 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5664 | 0.33 | 5000 | 1.3816 | 0.5688 |
| 0.9494 | 0.67 | 10000 | 0.9702 | 0.5852 |
| 0.9613 | 1.0 | 15000 | 0.9545 | 0.5917 |
| 0.8611 | 1.33 | 20000 | 0.9689 | 0.5953 |
| 0.8636 | 1.67 | 25000 | 0.9556 | 0.5943 |
| 0.8582 | 2.0 | 30000 | 0.9552 | 0.6003 |
| 0.7555 | 2.33 | 35000 | 1.0001 | 0.5928 |
| 0.7374 | 2.67 | 40000 | 1.0037 | 0.594 |
| 0.733 | 3.0 | 45000 | 0.9976 | 0.5983 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
PabloZubeldia/distilbert-base-uncased-finetuned-tweets
|
PabloZubeldia
| 2022-10-28T15:33:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T21:15:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-tweets
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. -->
# distilbert-base-uncased-finetuned-tweets
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2703
- Accuracy: 0.9068
- F1: 0.9081
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3212 | 1.0 | 143 | 0.2487 | 0.8989 | 0.8991 |
| 0.2031 | 2.0 | 286 | 0.2268 | 0.9077 | 0.9074 |
| 0.1474 | 3.0 | 429 | 0.2385 | 0.9094 | 0.9107 |
| 0.1061 | 4.0 | 572 | 0.2516 | 0.9103 | 0.9111 |
| 0.0804 | 5.0 | 715 | 0.2703 | 0.9068 | 0.9081 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
ajankelo/pklot_small_model
|
ajankelo
| 2022-10-28T14:32:23Z | 0 | 0 | null |
[
"PyTorch",
"vfnet",
"icevision",
"en",
"license:mit",
"region:us"
] | null | 2022-10-27T21:11:41Z |
---
language: en
license: mit
tags:
- PyTorch
- vfnet
- icevision
---
# Small PKLot
This model is trained on a subset of the PKLot dataset ( first introduced in this paper [here](https://www.inf.ufpr.br/lesoliveira/download/ESWA2015.pdf)). The subset is comprised of 50 fully annotated images for training.
## Citation for original dataset
Almeida, P., Oliveira, L. S., Silva Jr, E., Britto Jr, A., Koerich, A., PKLot – A robust dataset for parking lot classification, Expert Systems with Applications, 42(11):4937-4949, 2015.
|
alefarasin/ppo-CartPole-v1
|
alefarasin
| 2022-10-28T13:06:55Z | 0 | 0 | null |
[
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-28T13:06:49Z |
---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 155.80 +/- 45.55
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8
# Hyperparameters
```python
{'exp_name': 'dummy_name'
'f': '/root/.local/share/jupyter/runtime/kernel-e1e9a3a5-8345-4438-b691-f71df9c2a28b.json'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'alefarasin/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
gokul-g-menon/xls-r_fine_tuned
|
gokul-g-menon
| 2022-10-28T13:01:13Z | 74 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-10-26T16:47:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: xls-r_fine_tuned
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. -->
# xls-r_fine_tuned
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Rocketknight1/temp_upload_test
|
Rocketknight1
| 2022-10-28T12:29:16Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-28T12:28:55Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Rocketknight1/temp_upload_test
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. -->
# Rocketknight1/temp_upload_test
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6858
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.6858 | 0 |
### Framework versions
- Transformers 4.24.0.dev0
- TensorFlow 2.10.0
- Datasets 2.6.1
- Tokenizers 0.11.0
|
ayushtiwari/bert-finetuned-ner
|
ayushtiwari
| 2022-10-28T12:28:11Z | 11 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-27T20:58:57Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ayushtiwari/bert-finetuned-ner
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. -->
# ayushtiwari/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0271
- Validation Loss: 0.0549
- Epoch: 2
## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1723 | 0.0643 | 0 |
| 0.0465 | 0.0564 | 1 |
| 0.0271 | 0.0549 | 2 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.10.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
teacookies/autotrain-28102022-cert2-1916264970
|
teacookies
| 2022-10-28T12:26:46Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"token-classification",
"unk",
"dataset:teacookies/autotrain-data-28102022-cert2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-28T12:15:55Z |
---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-28102022-cert2
co2_eq_emissions:
emissions: 17.982023070008026
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1916264970
- CO2 Emissions (in grams): 17.9820
## Validation Metrics
- Loss: 0.002
- Accuracy: 1.000
- Precision: 0.980
- Recall: 0.986
- F1: 0.983
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-28102022-cert2-1916264970
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-28102022-cert2-1916264970", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-28102022-cert2-1916264970", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
ashish23993/t5-small-finetuned-xsum-ashish
|
ashish23993
| 2022-10-28T11:53:09Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-28T11:49:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-xsum-ashish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-ashish
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 8 | 2.2555 | 21.098 | 9.1425 | 17.7091 | 19.9721 | 19.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
ivanzidov/my-awesome-setfit-model
|
ivanzidov
| 2022-10-28T10:31:46Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-28T10:25:42Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
roa7n/DNABert_K6_G_quad_3
|
roa7n
| 2022-10-28T10:04:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-28T08:21:20Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DNABert_K6_G_quad_3
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. -->
# DNABert_K6_G_quad_3
This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0722
- Accuracy: 0.9761
## 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: 32
- 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0912 | 1.0 | 9375 | 0.0883 | 0.9707 |
| 0.0668 | 2.0 | 18750 | 0.0723 | 0.9757 |
| 0.0598 | 3.0 | 28125 | 0.0722 | 0.9761 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
caskcsg/cotmae_base_msmarco_retriever
|
caskcsg
| 2022-10-28T08:30:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"arxiv:2208.07670",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-28T08:01:25Z |
---
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- transformers
---
# CoT-MAE MS-Marco Passage Retriever
CoT-MAE is a transformers based Mask Auto-Encoder pretraining architecture designed for Dense Passage Retrieval.
**CoT-MAE MS-Marco Passage Retriever** is a retriever trained with BM25 hard negatives and CoT-MAE retriever mined MS-Marco hard negatives using [Tevatron](github.com/texttron/tevatron) toolkit. Specifically, we trained a stage-one retriever using BM25 HN, using stage-one retriever to mine HN, then trained a stage-two retriever using both BM25 HN & stage-one retriever mined hn. The release is the stage-two retriever.
Details can be found in our paper and codes.
Paper: [ConTextual Mask Auto-Encoder for Dense Passage Retrieval](https://arxiv.org/abs/2208.07670).
Code: [caskcsg/ir/cotmae](https://github.com/caskcsg/ir/tree/main/cotmae)
## Scores
### MS-Marco Passage full-ranking
| MRR @10 | recall@1 | recall@50 | recall@1k | QueriesRanked |
|----------|----------|-----------|-----------|----------------|
| 0.394431 | 0.265903 | 0.870344 | 0.986676 | 6980 |
## Citations
If you find our work useful, please cite our paper.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2208.07670,
doi = {10.48550/ARXIV.2208.07670},
url = {https://arxiv.org/abs/2208.07670},
author = {Wu, Xing and Ma, Guangyuan and Lin, Meng and Lin, Zijia and Wang, Zhongyuan and Hu, Songlin},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {ConTextual Mask Auto-Encoder for Dense Passage Retrieval},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
XaviXva/distilbert-base-uncased-finetuned-paws
|
XaviXva
| 2022-10-28T08:14:21Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:pawsx",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-26T09:59:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pawsx
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-paws
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: pawsx
type: pawsx
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.8355
- name: F1
type: f1
value: 0.8361579553422098
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-paws
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pawsx dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3850
- Accuracy: 0.8355
- F1: 0.8362
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6715 | 1.0 | 772 | 0.5982 | 0.6785 | 0.6799 |
| 0.4278 | 2.0 | 1544 | 0.3850 | 0.8355 | 0.8362 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
teacookies/autotrain-28102022-1914864930
|
teacookies
| 2022-10-28T07:41:13Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"token-classification",
"unk",
"dataset:teacookies/autotrain-data-28102022",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-28T07:30:27Z |
---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-28102022
co2_eq_emissions:
emissions: 19.19485186697524
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1914864930
- CO2 Emissions (in grams): 19.1949
## Validation Metrics
- Loss: 0.002
- Accuracy: 1.000
- Precision: 0.982
- Recall: 0.984
- F1: 0.983
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-28102022-1914864930
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-28102022-1914864930", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-28102022-1914864930", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
ComCom/gpt2-small
|
ComCom
| 2022-10-28T05:53:14Z | 273 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-28T05:43:05Z |
---
language: en
tags:
- exbert
license: mit
---
This repository has been forked from https://huggingface.co/gpt2
---
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The White man worked as a mannequin for'},
{'generated_text': 'The White man worked as a maniser of the'},
{'generated_text': 'The White man worked as a bus conductor by day'},
{'generated_text': 'The White man worked as a plumber at the'},
{'generated_text': 'The White man worked as a journalist. He had'}]
>>> set_seed(42)
>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The Black man worked as a man at a restaurant'},
{'generated_text': 'The Black man worked as a car salesman in a'},
{'generated_text': 'The Black man worked as a police sergeant at the'},
{'generated_text': 'The Black man worked as a man-eating monster'},
{'generated_text': 'The Black man worked as a slave, and was'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
## Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
### BibTeX entry and citation info
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
<a href="https://huggingface.co/exbert/?model=gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
bpatwa-shi/bert-finetuned-ner
|
bpatwa-shi
| 2022-10-28T05:22:16Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-28T03:37:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9333113238692637
- name: Recall
type: recall
value: 0.9515314708852238
- name: F1
type: f1
value: 0.9423333333333334
- name: Accuracy
type: accuracy
value: 0.9870636368988049
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0587
- Precision: 0.9333
- Recall: 0.9515
- F1: 0.9423
- Accuracy: 0.9871
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.086 | 1.0 | 1756 | 0.0634 | 0.9186 | 0.9364 | 0.9274 | 0.9829 |
| 0.0372 | 2.0 | 3512 | 0.0598 | 0.9328 | 0.9478 | 0.9402 | 0.9860 |
| 0.0217 | 3.0 | 5268 | 0.0587 | 0.9333 | 0.9515 | 0.9423 | 0.9871 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
doodlevelyn/roberta-base
|
doodlevelyn
| 2022-10-28T04:44:44Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-28T00:00:46Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base
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-base
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3953
- Precision: 0.5295
- Recall: 0.2861
- F1: 0.3715
- Accuracy: 0.9648
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0001 | 1.0 | 7365 | 0.4150 | 0.4892 | 0.1876 | 0.2712 | 0.9603 |
| 0.0 | 2.0 | 14730 | 0.4248 | 0.6005 | 0.2399 | 0.3428 | 0.9638 |
| 0.0 | 3.0 | 22095 | 0.3953 | 0.5295 | 0.2861 | 0.3715 | 0.9648 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/shinononetu
|
huggingtweets
| 2022-10-28T04:43:17Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-28T04:42:41Z |
---
language: en
thumbnail: http://www.huggingtweets.com/shinononetu/1666932192965/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1381323487499980806/i2qeW2Qi_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Netu</div>
<div style="text-align: center; font-size: 14px;">@shinononetu</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Netu.
| Data | Netu |
| --- | --- |
| Tweets downloaded | 1912 |
| Retweets | 627 |
| Short tweets | 453 |
| Tweets kept | 832 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38lbhqc9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @shinononetu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1tj5n1bk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1tj5n1bk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/shinononetu')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
lixiangchun/imagenet-swav-resnet50w2
|
lixiangchun
| 2022-10-28T04:13:37Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"onnx",
"region:us"
] | null | 2022-10-20T04:06:01Z |
```python
import trace_layer2 as models
import torch
x=torch.randn(1, 3, 224, 224)
state_dict = torch.load('swav_imagenet_layer2.pt', map_location='cpu')
model = models.resnet50w2()
model.load_state_dict(state_dict)
model.eval()
feature = model(x)
traced_model = torch.jit.load('traced_swav_imagenet_layer2.pt', map_location='cpu')
traced_model.eval()
feature = traced_model(x)
```
|
huggingtweets/missalykatt
|
huggingtweets
| 2022-10-28T02:37:20Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-28T02:34:18Z |
---
language: en
thumbnail: http://www.huggingtweets.com/missalykatt/1666924619450/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1556386443752222720/Fzb-hZ4Q_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">MissAlyKatt 🏳️🌈♀️</div>
<div style="text-align: center; font-size: 14px;">@missalykatt</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from MissAlyKatt 🏳️🌈♀️.
| Data | MissAlyKatt 🏳️🌈♀️ |
| --- | --- |
| Tweets downloaded | 3217 |
| Retweets | 361 |
| Short tweets | 757 |
| Tweets kept | 2099 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yaoalt1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @missalykatt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2uetdofk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2uetdofk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/missalykatt')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
helloway/simple
|
helloway
| 2022-10-28T02:00:19Z | 0 | 0 | null |
[
"audio-classification",
"license:apache-2.0",
"region:us"
] |
audio-classification
| 2022-10-28T01:51:37Z |
---
license: apache-2.0
tags:
- audio-classification
---
|
Sunny5353/distilbert-base-uncased-finetuned-imdb
|
Sunny5353
| 2022-10-28T01:40:18Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-28T01:29:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.76 | 1.0 | 157 | 0.6640 |
| 0.688 | 2.0 | 314 | 0.6581 |
| 0.6768 | 3.0 | 471 | 0.6604 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Gub/model_gub_v2
|
Gub
| 2022-10-28T00:58:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-10-20T05:17:16Z |
---
license: creativeml-openrail-m
---
|
OpenMatch/cocodr-large-msmarco-idro-only
|
OpenMatch
| 2022-10-28T00:45:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-28T00:42:33Z |
---
license: mit
---
This model has been pretrained on MS MARCO corpus and then finetuned on MS MARCO training data with implicit distributionally robust optimization (iDRO), following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-large as the backbone with 335M hyperparameters.
|
TingChenChang/t5-end2end-questions-generation
|
TingChenChang
| 2022-10-28T00:36:02Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-27T14:37:17Z |
---
tags:
- generated_from_trainer
model-index:
- name: t5-end2end-questions-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [TingChenChang/t5-end2end-questions-generation](https://huggingface.co/TingChenChang/t5-end2end-questions-generation) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5291
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5711 | 0.4 | 100 | 1.6119 |
| 1.5353 | 0.8 | 200 | 1.6052 |
| 1.502 | 1.2 | 300 | 1.6082 |
| 1.4525 | 1.6 | 400 | 1.5918 |
| 1.4463 | 2.0 | 500 | 1.5847 |
| 1.3885 | 2.4 | 600 | 1.6236 |
| 1.4029 | 2.8 | 700 | 1.5962 |
| 1.3947 | 3.2 | 800 | 1.5932 |
| 1.3685 | 3.6 | 900 | 1.5898 |
| 1.3926 | 4.0 | 1000 | 1.5624 |
| 1.4666 | 4.4 | 1100 | 1.5535 |
| 1.4573 | 4.8 | 1200 | 1.5483 |
| 1.4342 | 5.2 | 1300 | 1.5449 |
| 1.4281 | 5.6 | 1400 | 1.5347 |
| 1.4031 | 6.0 | 1500 | 1.5456 |
| 1.375 | 6.4 | 1600 | 1.5375 |
| 1.3867 | 6.8 | 1700 | 1.5393 |
| 1.3763 | 7.2 | 1800 | 1.5401 |
| 1.357 | 7.6 | 1900 | 1.5361 |
| 1.3568 | 8.0 | 2000 | 1.5295 |
| 1.3503 | 8.4 | 2100 | 1.5377 |
| 1.3335 | 8.8 | 2200 | 1.5353 |
| 1.3416 | 9.2 | 2300 | 1.5288 |
| 1.3179 | 9.6 | 2400 | 1.5324 |
| 1.3276 | 10.0 | 2500 | 1.5291 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.12.1
|
caffsean/bert-base-cased-deep-ritmo
|
caffsean
| 2022-10-28T00:17:00Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-27T03:19:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-deep-ritmo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-deep-ritmo
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0463 | 1.0 | 1875 | 3.7428 |
| 3.3393 | 2.0 | 3750 | 3.0259 |
| 2.7435 | 3.0 | 5625 | 2.5837 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
allenai/scirepeval_adapters_qry
|
allenai
| 2022-10-28T00:06:24Z | 12 | 1 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:scirepeval/adhoc_search",
"bert",
"dataset:allenai/scirepeval",
"region:us"
] | null | 2022-10-28T00:06:13Z |
---
tags:
- adapterhub:scirepeval/adhoc_search
- adapter-transformers
- bert
datasets:
- allenai/scirepeval
---
# Adapter `allenai/scirepeval_adapters_qry` for malteos/scincl
An [adapter](https://adapterhub.ml) for the `malteos/scincl` model that was trained on the [scirepeval/adhoc_search](https://adapterhub.ml/explore/scirepeval/adhoc_search/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("malteos/scincl")
adapter_name = model.load_adapter("allenai/scirepeval_adapters_qry", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
OpenMatch/co-condenser-large
|
OpenMatch
| 2022-10-28T00:03:42Z | 33 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-27T23:56:37Z |
---
license: mit
---
This model has been pretrained on MS MARCO following the approach described in the paper **Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval**. The model can be used to reproduce the experimental results within the GitHub repository https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-large as the backbone with 335M hyperparameters.
|
allenai/scirepeval_adapters_clf
|
allenai
| 2022-10-28T00:03:35Z | 14 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:scirepeval/classification",
"bert",
"dataset:allenai/scirepeval",
"region:us"
] | null | 2022-10-28T00:03:26Z |
---
tags:
- adapterhub:scirepeval/classification
- adapter-transformers
- bert
datasets:
- allenai/scirepeval
---
# Adapter `allenai/scirepeval_adapters_clf` for malteos/scincl
An [adapter](https://adapterhub.ml) for the `malteos/scincl` model that was trained on the [scirepeval/classification](https://adapterhub.ml/explore/scirepeval/classification/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("malteos/scincl")
adapter_name = model.load_adapter("allenai/scirepeval_adapters_clf", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
huggingtweets/f1_nn0
|
huggingtweets
| 2022-10-27T23:52:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-27T23:51:36Z |
---
language: en
thumbnail: http://www.huggingtweets.com/f1_nn0/1666914758812/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1207307756610445315/5rbKIvN6_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">chilgar</div>
<div style="text-align: center; font-size: 14px;">@f1_nn0</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from chilgar.
| Data | chilgar |
| --- | --- |
| Tweets downloaded | 1284 |
| Retweets | 56 |
| Short tweets | 384 |
| Tweets kept | 844 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6g2hbq09/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @f1_nn0's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34clozqp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34clozqp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/f1_nn0')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
wavymulder/zelda-diffusion-HN
|
wavymulder
| 2022-10-27T21:32:27Z | 0 | 18 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-10-25T01:06:42Z |
---
license: creativeml-openrail-m
---
**Zelda Diffusion - Hypernet**
[*DOWNLOAD LINK*](https://huggingface.co/wavymulder/zelda-diffusion-HN/resolve/main/zeldaBOTW.pt) - This is a hypernet trained on screenshots of Princess Zelda from BOTW
Here's a random batch of 9 images to show the hypernet uncherrypicked. The prompt is "anime princess zelda volumetric lighting" and the negative prompt is "cel render 3d animation"
and [a link to more](https://i.imgur.com/NixQGid.jpg)
---
Tips:
You'll want to adjust the hypernetwork strength depending on what style you're trying to put Zelda into. I usually keep it at 80% strength and go from there.
This hypernetwork helps make Zelda look more like the BOTW Zelda. You still have to prompt for what you want. Extra weight might sometimes need to be applied to get her to wear costumes. You may also have luck putting her name closer to the end of the prompt than you normally would.
Since the hypernetwork is trained on screenshots from the videogame, it imparts a heavy Cel Shading effect [(Example here)](https://huggingface.co/wavymulder/zelda-diffusion-HN/resolve/main/00108-920950.png). You can minimize this by negative prompting "cel". I believe every example posted here uses this.
The hypernet can be used either with very simple prompting, as shown above, or a prompt of your favourite artists.
You can put this hypernet on top of different models to create some really cool Zeldas, such as this one made with [Nitrosocke](https://huggingface.co/nitrosocke)'s [Modern Disney Model](https://huggingface.co/nitrosocke/modern-disney-diffusion).
|
Aadarsh/bert-finetuned-ner
|
Aadarsh
| 2022-10-27T21:31:02Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-26T22:08:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1429
- Precision: 0.4954
- Recall: 0.6136
- F1: 0.5482
- Accuracy: 0.9642
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 141 | 0.2894 | 0.4649 | 0.3258 | 0.3831 | 0.9219 |
| No log | 2.0 | 282 | 0.1767 | 0.4706 | 0.4545 | 0.4624 | 0.9487 |
| No log | 3.0 | 423 | 0.1429 | 0.4954 | 0.6136 | 0.5482 | 0.9642 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
OpenMatch/cocodr-base-msmarco-idro-only
|
OpenMatch
| 2022-10-27T21:26:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-10-27T21:21:56Z |
---
license: mit
---
This model has been pretrained on MS MARCO corpus and then finetuned on MS MARCO training data with implicit distributionally robust optimization (iDRO), following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-base as the backbone with 110M hyperparameters.
|
ViktorDo/SciBERT-POWO_Epiphyte_Finetuned
|
ViktorDo
| 2022-10-27T21:10:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T19:53:27Z |
---
tags:
- generated_from_trainer
model-index:
- name: SciBERT-POWO_Epiphyte_Finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SciBERT-POWO_Epiphyte_Finetuned
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0909 | 1.0 | 2063 | 0.0860 |
| 0.0763 | 2.0 | 4126 | 0.1000 |
| 0.0627 | 3.0 | 6189 | 0.0898 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Phantasion/phaninc
|
Phantasion
| 2022-10-27T21:03:33Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-10-27T20:18:49Z |
Phaninc is a model based on my cyberpunk tumblr blog phantasyinc. One thing that has frustrated me with AI art is the generic quality of prompting for cyberpunk imagery, so I went through my blog and curated a dataset for 25 new keywords to get the results I desire. I have been heavily inspired by the work of nousr on robodiffusion whose model gave me a lot of results I love.
I have utilised the new FAST dreambooth method, and run it at 20000 steps on 684 images (around 800 steps per concept). At the time of writing the model is still training but I thought I would use my training time to summarise my intent with each keyword. I expect there to be problems and some of my experiments to not pan out so well, but I thought I would share.
*Post training update: the entire model is contaminated, most prompts are gonna churn out cyberpunk work, but the keywords are still good against one another and work as desired, and the base model has had some interesting lessons taught to it.*
**phanborg**
This set was the first to be tested, it is a combination of portraits of cyborgs much like phancyborg and phandroid. The difference between the three is that phanborg uses a combination of images with the face covered and uncovered by machinery, while phancyborg uses only uncovered cyborgs and phandroid only covered cyborgs. The images used in all three are entirely different so that I can play with a diversity of trained features with my keywords.
**phanbrutal**
Images I consider a combination of cyberpunk and brutalism.
**phanbw**
This one is one of my more experimental keywords, utilising monochrome cyberpunk images I find quite striking in black and white. However apart from sticking to a cyberpunk theme, there is no consistent subject matter and may just end up being a generic monochrome keyword.
**phancircle**
another experimental keyword, this keyword utilises a selection of architectural, textural and 3d design images with circles and spheres as a recurring motif. My hope is this keyword will help provide a cyberpunk texture to other prompts with a circular motif.
**phancity**
Bleak futuristic cityscapes, but like phanbw this experiment may fail due to being too varied subject matter.
**phanconcrete**
concrete, images of architecture with mostly concrete finishes, might be overkill with phanbrutal above, but I like that there will still be nuanced differences to play with.
**phanconsole**
A command centre needs buttons to beep and switches to boop, this keyword is all about screens and buttons.
**phancorridor**
images of spaceship corridors and facilities to provide a more futuristic interior design.
**phancyborg**
phancyborg is an image selection of cyborgs with some or all of a human face uncovered.
**phandraw**
a selection focused on drawn cyberpunk artwork with bright neon colors and defined linework
**phandroid**
this is where I pay most homage to nousrs robodiffusion, using only cyborgs with their faces concealed or just plain humanoid robots
**phandustrial**
futuristic ndustrial imagery of pipes wires and messes of cables.
**phanfashion**
trying to get that urbanwear hoodie look but with some variations.
**phanfem**
a series of cyberpunk women
**phanglitch**
Glitch art I had reblogged on the blog with a cyberpunk feel. Quite colorful.
**phangrunge**
Dilapidated dens for the scum of the city. Hopefully will add a good dose of urban decay to your prompt.
**phanlogo**
Sleek graphic design, typography and logos.
**phanmachine**
Built with unclear subject matter, phanmachine focuses on the details of futuristic shiny machinery in hopes of it coming out as a style or texture that can be applied in prompts.
**phanmecha**
The three cyborg keywords are sleek and humanoid, phanmecha focuses more on creating unique robot bodytypes.
**phanmilitary**
Future soldiers, man and machine. Likely to attach a gun to your prompt's character.
**phanneon**
Bright neon lights taking over the scene, this feature is what annoyed me with a lot of cyberpunk prompts in ai models. Overall I have it pretty isolated to this keyword, if you want those futuristic glowies.
**phanrooms**
Totally seperate to the rest of the theming, phanrooms is trained on backrooms and liminal space imagery. Which like cyberpunk is of high visual interest to me, and something the base model can sometimes struggle with.
**phansterile**
This is like cyberpunk cleancore, lots of white, very clean, clinical theming.
**phantex**
I don't know why latex outfits are cyberpunk but they just are, these images were selected for the accessorising on top of just the latex outfits.
**phanture**
Abstract textures that were cyberpunk enough for me to put on my blog.
|
YurtsAI/yurts-python-code-gen-30-sparse
|
YurtsAI
| 2022-10-27T20:39:18Z | 560 | 19 |
transformers
|
[
"transformers",
"pytorch",
"codegen",
"text-generation",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-24T22:22:16Z |
---
license: bsd-3-clause
---
# Maverick (Yurt's Python Code Generation Model)
## Model description
This code generation model was fine-tuned on Python code from a generic multi-language code generation model. This model was then pushed to 30% sparsity using Yurts' in-house technology without performance loss. In this specific instance, the class representation for the network is still dense. This particular model has 350M trainable parameters.
## Training data
This model was tuned on a subset of the Python data available in the BigQuery open-source [Github dataset](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code).
## How to use
The model is great at autocompleting based off of partially generated function signatures and class signatures. It is also decent at generating code base based off of natural language prompts with a comment. If you find something cool you can do with the model, be sure to share it with us!
Check out our [colab notebook](https://colab.research.google.com/drive/1NDO4X418HuPJzF8mFc6_ySknQlGIZMDU?usp=sharing) to see how to invoke the model and try it out.
## Feedback and Questions
Have any questions or feedback? Find us on [Discord](https://discord.gg/2x4rmSGER9).
|
andrewzhang505/sf2-lunar-lander
|
andrewzhang505
| 2022-10-27T19:51:07Z | 2 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-27T19:50:47Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- metrics:
- type: mean_reward
value: 126.58 +/- 137.36
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLanderContinuous-v2
type: LunarLanderContinuous-v2
---
A(n) **APPO** model trained on the **LunarLanderContinuous-v2** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
orlcast/layoutxlm-finetuned-xfund-it-re
|
orlcast
| 2022-10-27T19:29:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"generated_from_trainer",
"dataset:xfun",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-10-20T13:37:37Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
model-index:
- name: layoutxlm-finetuned-xfund-it-re
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. -->
# layoutxlm-finetuned-xfund-it-re
This model is a fine-tuned version of [orlcast/layoutxlm-finetuned-xfund-it-re](https://huggingface.co/orlcast/layoutxlm-finetuned-xfund-it-re) on the xfun dataset.
It achieves the following results on the evaluation set:
- Precision: 0.5092
- Recall: 0.7450
- F1: 0.6050
- Loss: 0.0020
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 4000
### Training results
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.6.1
- Tokenizers 0.12.1
|
sam34738/roberta-nisha
|
sam34738
| 2022-10-27T19:29:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T19:03:16Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-nisha
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-nisha
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5375
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3254 | 1.0 | 460 | 0.7247 |
| 0.5791 | 2.0 | 920 | 0.5375 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Tokenizers 0.13.1
|
hoodhahmed/dhivehi_corpus
|
hoodhahmed
| 2022-10-27T18:59:43Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2022-10-27T18:59:43Z |
---
license: bigscience-openrail-m
---
|
sam34738/roberta-kabita
|
sam34738
| 2022-10-27T18:33:31Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T18:13:13Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-kabita
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-kabita
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4709
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2327 | 1.0 | 460 | 0.6935 |
| 0.4793 | 2.0 | 920 | 0.4709 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Tokenizers 0.13.1
|
vict0rsch/climateGAN
|
vict0rsch
| 2022-10-27T17:49:52Z | 0 | 2 | null |
[
"Climate Change",
"GAN",
"Domain Adaptation",
"en",
"license:gpl-3.0",
"region:us"
] | null | 2022-10-24T13:17:28Z |
---
language:
- en
tags:
- Climate Change
- GAN
- Domain Adaptation
license: gpl-3.0
title: ClimateGAN
emoji: 🌎
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 3.6
app_file: app.py
inference: true
pinned: true
---
# ClimateGAN: Raising Awareness about Climate Change by Generating Images of Floods
This repository contains the code used to train the model presented in our **[paper](https://openreview.net/forum?id=EZNOb_uNpJk)**.
It is not simply a presentation repository but the code we have used over the past 30 months to come to our final architecture. As such, you will find many scripts, classes, blocks and options which we actively use for our own development purposes but are not directly relevant to reproduce results or use pretrained weights.
If you use this code, data or pre-trained weights, please cite our ICLR 2022 paper:
```
@inproceedings{schmidt2022climategan,
title = {Climate{GAN}: Raising Climate Change Awareness by Generating Images of Floods},
author = {Victor Schmidt and Alexandra Luccioni and M{\'e}lisande Teng and Tianyu Zhang and Alexia Reynaud and Sunand Raghupathi and Gautier Cosne and Adrien Juraver and Vahe Vardanyan and Alex Hern{\'a}ndez-Garc{\'\i}a and Yoshua Bengio},
booktitle = {International Conference on Learning Representations},
year = {2022},
url = {https://openreview.net/forum?id=EZNOb_uNpJk}
}
```
## Using pre-trained weights from this Huggingface Space and Stable Diffusion In-painting
<p align="center">
<strong>Huggingface ClimateGAN Space:</strong>
<a href="https://huggingface.co/spaces/vict0rsch/climateGAN" target="_blank">
<img src="https://huggingface.co/vict0rsch/climateGAN/resolve/main/images/hf-cg.png">
</a>
</p>
1. Download code and model
```bash
git lfs install
git clone https://huggingface.co/vict0rsch/climateGAN
git lfs pull # optional if you don't have the weights
```
2. Install requirements
```
pip install requirements.txt
```
3. **Enable Stable Diffusion Inpainting** by visiting the model's card: https://huggingface.co/runwayml/stable-diffusion-inpainting **and** running `$ huggingface-cli login`
4. Run `$ python climategan_wrapper.py help` for usage instructions on how to infer on a folder's images.
5. Run `$ python app.py` to see the Gradio app.
1. To use Google Street View you'll need an API key and set the `GMAPS_API_KEY` environment variable.
2. To use Stable Diffusion if you can't run `$ huggingface-cli login` (on a Huggingface Space for instance) set the `HF_AUTH_TOKEN` env variable to a [Huggingface authorization token](https://huggingface.co/settings/tokens)
3. To change the UI without model overhead, set the `CG_DEV_MODE` environment variable to `true`.
For a more fine-grained control on ClimateGAN's inferences, refer to `apply_events.py` (does not support Stable Diffusion painter)
**Note:** you don't have control on the prompt by design because I disabled the safety checker. Fork this space/repo and do it yourself if you really need to change the prompt. At least [open a discussion](https://huggingface.co/spaces/vict0rsch/climateGAN/discussions).
## Using pre-trained weights from source
In the paper, we present ClimateGAN as a solution to produce images of floods. It can actually do **more**:
* reusing the segmentation map, we are able to isolate the sky, turn it red and in a few more steps create an image resembling the consequences of a wildfire on a neighboring area, similarly to the [California wildfires](https://www.google.com/search?q=california+wildfires+red+sky&source=lnms&tbm=isch&sa=X&ved=2ahUKEwisws-hx7zxAhXxyYUKHQyKBUwQ_AUoAXoECAEQBA&biw=1680&bih=917&dpr=2).
* reusing the depth map, we can simulate the consequences of a smog event on an image, scaling the intensity of the filter by the distance of an object to the camera, as per [HazeRD](http://www2.ece.rochester.edu/~gsharma/papers/Zhang_ICIP2017_HazeRD.pdf)
In this section we'll explain how to produce the `Painted Input` along with the Smog and Wildfire outputs of a pre-trained ClimateGAN model.
### Installation
This repository and associated model have been developed using Python 3.8.2 and **Pytorch 1.7.0**.
```bash
$ git clone git@github.com:cc-ai/climategan.git
$ cd climategan
$ pip install -r requirements-3.8.2.txt # or `requirements-any.txt` for other Python versions (not tested but expected to be fine)
```
Our pipeline uses [comet.ml](https://comet.ml) to log images. You don't *have* to use their services but we recommend you do as images can be uploaded on your workspace instead of being written to disk.
If you want to use Comet, make sure you have the [appropriate configuration in place (API key and workspace at least)](https://www.comet.ml/docs/python-sdk/advanced/#non-interactive-setup)
### Inference
1. Download and unzip the weights [from this link](https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K&export=download) (checkout [`gdown`](https://github.com/wkentaro/gdown) for a commandline interface) and put them in `config/`
```
$ pip install gdown
$ mkdir config
$ cd config
$ gdown https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K
$ unzip release-github-v1.zip
$ cd ..
```
2. Run from the repo's root:
1. With `comet`:
```bash
python apply_events.py --batch_size 4 --half --images_paths path/to/a/folder --resume_path config/model/masker --upload
```
2. Without `comet` (and shortened args compared to the previous example):
```bash
python apply_events.py -b 4 --half -i path/to/a/folder -r config/model/masker --output_path path/to/a/folder
```
The `apply_events.py` script has many options, for instance to use a different output size than the default systematic `640 x 640` pixels, look at the code or `python apply_events.py --help`.
## Training from scratch
ClimateGAN is split in two main components: the Masker producing a binary mask of where water should go and the Painter generating water within this mask given an initial image's context.
### Configuration
The code is structured to use `shared/trainer/defaults.yaml` as default configuration. There are 2 ways of overriding those for your purposes (without altering that file):
1. By providing an alternative configuration as command line argument `config=path/to/config.yaml`
1. The code will first load `shared/trainer/defaults.yaml`
2. *then* update the resulting dictionary with values read in the provided `config` argument.
3. The folder `config/` is NOT tracked by git so you would typically put them there
2. By overwriting specific arguments from the command-line like `python train.py data.loaders.batch_size=8`
### Data
#### Masker
##### Real Images
Because of copyrights issues we are not able to share the real images scrapped from the internet. You would have to do that yourself. In the `yaml` config file, the code expects a key pointing to a `json` file like `data.files.<train or val>.r: <path/to/a/json/file>`. This `json` file should be a list of dictionaries with tasks as keys and files as values. Example:
```json
[
{
"x": "path/to/a/real/image",
"s": "path/to/a/segmentation_map",
"d": "path/to/a/depth_map"
},
...
]
```
Following the [ADVENT](https://github.com/valeoai/ADVENT) procedure, only `x` should be required. We use `s` and `d` inferred from pre-trained models (DeepLab v3+ and MiDAS) to use those pseudo-labels in the first epochs of training (see `pseudo:` in the config file)
##### Simulated Images
We share snapshots of the Virtual World we created in the [Mila-Simulated-Flood dataset](). You can download and unzip one water-level and then produce json files similar to that of the real data, with an additional key `"m": "path/to/a/ground_truth_sim_mask"`. Lastly, edit the config file: `data.files.<train or val>.s: <path/to/a/json/file>`
#### Painter
The painter expects input images and binary masks to train using the [GauGAN](https://github.com/NVlabs/SPADE) training procedure. Unfortunately we cannot share openly the collected data, but similarly as for the Masker's real data you would point to the data using a `json` file as:
```json
[
{
"x": "path/to/a/real/image",
"m": "path/to/a/water_mask",
},
...
]
```
And put those files as values to `data.files.<train or val>.rf: <path/to/a/json/file>` in the configuration.
## Coding conventions
* Tasks
* `x` is an input image, in [-1, 1]
* `s` is a segmentation target with `long` classes
* `d` is a depth map target in R, may be actually `log(depth)` or `1/depth`
* `m` is a binary mask with 1s where water is/should be
* Domains
* `r` is the *real* domain for the masker. Input images are real pictures of urban/suburban/rural areas
* `s` is the *simulated* domain for the masker. Input images are taken from our Unity world
* `rf` is the *real flooded* domain for the painter. Training images are pairs `(x, m)` of flooded scenes for which the water should be reconstructed, in the validation data input images are not flooded and we provide a manually labeled mask `m`
* `kitti` is a special `s` domain to pre-train the masker on [Virtual Kitti 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/)
* it alters the `trainer.loaders` dict to select relevant data sources from `trainer.all_loaders` in `trainer.switch_data()`. The rest of the code is identical.
* Flow
* This describes the call stack for the trainers standard training procedure
* `train()`
* `run_epoch()`
* `update_G()`
* `zero_grad(G)`
* `get_G_loss()`
* `get_masker_loss()`
* `masker_m_loss()` -> masking loss
* `masker_s_loss()` -> segmentation loss
* `masker_d_loss()` -> depth estimation loss
* `get_painter_loss()` -> painter's loss
* `g_loss.backward()`
* `g_opt_step()`
* `update_D()`
* `zero_grad(D)`
* `get_D_loss()`
* painter's disc losses
* `masker_m_loss()` -> masking AdvEnt disc loss
* `masker_s_loss()` -> segmentation AdvEnt disc loss
* `d_loss.backward()`
* `d_opt_step()`
* `update_learning_rates()` -> update learning rates according to schedules defined in `opts.gen.opt` and `opts.dis.opt`
* `run_validation()`
* compute val losses
* `eval_images()` -> compute metrics
* `log_comet_images()` -> compute and upload inferences
* `save()`
|
OpenMatch/cocodr-base
|
OpenMatch
| 2022-10-27T16:20:16Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-26T05:51:29Z |
This model has been pretrained on BEIR corpus without relevance-level supervision following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-base as the backbone with 110M hyperparameters.
license: mit
---
|
mgb-dx-meetup/xlm-roberta-finetuned-sentiment
|
mgb-dx-meetup
| 2022-10-27T15:37:04Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"text-classification",
"unk",
"dataset:lewtun/autotrain-data-mgb-product-reviews-xlm-r",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T15:17:01Z |
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lewtun/autotrain-data-mgb-product-reviews-xlm-r
co2_eq_emissions:
emissions: 19.116414139555882
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1904264758
- CO2 Emissions (in grams): 19.1164
## Validation Metrics
- Loss: 1.021
- Accuracy: 0.563
- Macro F1: 0.555
- Micro F1: 0.563
- Weighted F1: 0.556
- Macro Precision: 0.555
- Micro Precision: 0.563
- Weighted Precision: 0.556
- Macro Recall: 0.562
- Micro Recall: 0.563
- Weighted Recall: 0.563
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/lewtun/autotrain-mgb-product-reviews-xlm-r-1904264758
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-mgb-product-reviews-xlm-r-1904264758", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-mgb-product-reviews-xlm-r-1904264758", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
Plaban81/vegetable-classifier
|
Plaban81
| 2022-10-27T15:35:01Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-27T15:34:48Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: vegetable-classifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8571428656578064
---
# vegetable-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Brinjal
#### Cabbage
#### Cauliflower
#### Raddish
#### Tomato
|
Sennodipoi/LayoutLMv3-FUNSD-ft
|
Sennodipoi
| 2022-10-27T15:29:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-23T08:14:07Z |
LayoutLMv3 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained using seqeval in strict mode:
| | Precision | Recall | F1-score | Variance (F1) |
|--------------|-----------|--------|----------|---------------|
| Answer | 0.90 | 0.91 | 0.90 | 3e-5 |
| Header | 0.61 | 0.66 | 0.63 | 4e-4 |
| Question | 0.88 | 0.87 | 0.88 | 1e-4 |
| Micro avg | 0.87 | 0.88 | 0.87 | 3e-5 |
| Macro avg | 0.79 | 0.82 | 0.80 | 3e-5 |
| Weighted avg | 0.87 | 0.88 | 0.87 | 3e-5 |
|
JoAmps/trialz
|
JoAmps
| 2022-10-27T15:28:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-27T15:04:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: trialz
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. -->
# trialz
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0043
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 282 | 2.1342 |
| 2.308 | 2.0 | 564 | 2.0320 |
| 2.308 | 3.0 | 846 | 2.0148 |
| 2.1411 | 4.0 | 1128 | 2.0076 |
| 2.1411 | 5.0 | 1410 | 2.0043 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Sennodipoi/LayoutLMv3-kleisterNDA
|
Sennodipoi
| 2022-10-27T15:26:00Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-24T15:26:45Z |
LayoutLMv3 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained with seqeval in strict mode:
| | Precision | Recall | F1-score | Variance (F1) |
|----------------|-----------|--------|----------|---------------|
| EFFECTIVE_DATE | 0.92 | 0.99 | 0.95 | 5e-5 |
| JURISDICTION | 0.87 | 0.88 | 0.88 | 8e-6 |
| PARTY | 0.92 | 0.99 | 0.95 | 5e-5 |
| TERM | 1 | 1 | 1 | 0 |
| Micro avg | 0.91 | 0.96 | 0.94 | 2e-5 |
| Macro avg | 0.92 | 0.96 | 0.94 | 3e-7 |
| Weighted avg | 0.91 | 0.96 | 0.94 | 2e-5 |
Since I used the same segmentation strategy of the original paper i.e. using the labels to create segments, the scores are not directly comparable with the other LayoutLM versions.
|
pig4431/sst2_bert_3epoch
|
pig4431
| 2022-10-27T15:01:53Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T14:55:30Z |
---
tags:
- generated_from_trainer
model-index:
- name: sst2_bert_3epoch
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. -->
# sst2_bert_3epoch
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Shri3/q-Taxi-v3
|
Shri3
| 2022-10-27T14:36:11Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-27T14:36:05Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
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 playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Shri3/q-Taxi-v3", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Shri3/q-FrozenLake-v1-4x4-noSlippery
|
Shri3
| 2022-10-27T14:33:14Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-27T14:07:26Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Shri3/q-FrozenLake-v1-4x4-noSlippery", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
yeahrmek/arxiv-math-lean
|
yeahrmek
| 2022-10-27T14:05:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-10-27T12:23:41Z |
This is a BPE tokenizer based on "Salesforce/codegen-350M-mono".
The tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece)
so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not.
We used ArXiv subset of The Pile dataset and proof steps from [lean-step-public](https://github.com/jesse-michael-han/lean-step-public) datasets to train the tokenizer.
|
JoAmps/GhPoliticsBERT
|
JoAmps
| 2022-10-27T13:15:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-27T10:55:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: GhPoliticsBERT
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. -->
# GhPoliticsBERT
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0 | 1.0 | 9188 | 0.0000 |
| 0.0 | 2.0 | 18376 | 0.0000 |
| 0.0 | 3.0 | 27564 | 0.0000 |
| 0.0 | 4.0 | 36752 | 0.0000 |
| 0.0 | 5.0 | 45940 | 0.0000 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
kevinbror/bertbaseuncasedny
|
kevinbror
| 2022-10-27T12:13:45Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-27T12:13:00Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bertbaseuncasedny
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. -->
# bertbaseuncasedny
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3901
- Train End Logits Accuracy: 0.8823
- Train Start Logits Accuracy: 0.8513
- Validation Loss: 1.2123
- Validation End Logits Accuracy: 0.7291
- Validation Start Logits Accuracy: 0.6977
- Epoch: 3
## 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 29508, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.2597 | 0.6683 | 0.6277 | 1.0151 | 0.7214 | 0.6860 | 0 |
| 0.7699 | 0.7820 | 0.7427 | 1.0062 | 0.7342 | 0.6996 | 1 |
| 0.5343 | 0.8425 | 0.8064 | 1.1162 | 0.7321 | 0.7010 | 2 |
| 0.3901 | 0.8823 | 0.8513 | 1.2123 | 0.7291 | 0.6977 | 3 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Rijgersberg/whisper-small-fy-NL
|
Rijgersberg
| 2022-10-27T08:50:21Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-10-25T22:17:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: whisper-small-fy-NL
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-small-fy-NL
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [CommonVoice 11 `fy-NL` (West-Frisian)](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/fy-NL/train) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5276
- Wer: 0.2919
The Wer before finetuning was 1.0622.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| | 0 | 0 | | 1.0622|
| 0.9177 | 1.0 | 211 | 0.8145 | 0.3450 |
| 0.5807 | 2.0 | 422 | 0.7113 | 0.3640 |
| 0.2884 | 3.0 | 633 | 0.5276 | 0.2919 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
arshiya20/epochs-finetuned-squad
|
arshiya20
| 2022-10-27T07:44:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-27T05:38:23Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: epochs-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. -->
# epochs-finetuned-squad
This model was trained from scratch on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2609
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7553 | 1.0 | 5533 | 1.2460 |
| 0.739 | 2.0 | 11066 | 1.2609 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
sd-concepts-library/pintu
|
sd-concepts-library
| 2022-10-27T06:49:30Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-27T06:49:13Z |
---
license: mit
---
### pintu on Stable Diffusion
This is the `<pintu-dog>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
teacookies/autotrain-27102022-cert1-1899464570
|
teacookies
| 2022-10-27T06:29:42Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"token-classification",
"unk",
"dataset:teacookies/autotrain-data-27102022-cert1",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-27T06:19:22Z |
---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-27102022-cert1
co2_eq_emissions:
emissions: 16.254745105263574
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1899464570
- CO2 Emissions (in grams): 16.2547
## Validation Metrics
- Loss: 0.004
- Accuracy: 0.999
- Precision: 0.972
- Recall: 0.979
- F1: 0.975
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-27102022-cert1-1899464570
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-27102022-cert1-1899464570", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-27102022-cert1-1899464570", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
huggingtweets/ferret_gf
|
huggingtweets
| 2022-10-27T06:27:00Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-27T06:26:17Z |
---
language: en
thumbnail: http://www.huggingtweets.com/ferret_gf/1666852015981/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1583569492789153799/vJ1FEmHw_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">alex</div>
<div style="text-align: center; font-size: 14px;">@ferret_gf</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from alex.
| Data | alex |
| --- | --- |
| Tweets downloaded | 703 |
| Retweets | 163 |
| Short tweets | 183 |
| Tweets kept | 357 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/95pl7wzb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ferret_gf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2k6rhew5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2k6rhew5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ferret_gf')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/schizo_freq
|
huggingtweets
| 2022-10-27T03:52:41Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-09T17:50:33Z |
---
language: en
thumbnail: http://www.huggingtweets.com/schizo_freq/1666842754202/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1582126821025382400/PZjx83du_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Lukas (computer)</div>
<div style="text-align: center; font-size: 14px;">@schizo_freq</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Lukas (computer).
| Data | Lukas (computer) |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 481 |
| Short tweets | 324 |
| Tweets kept | 2429 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/11autkzl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @schizo_freq's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2km4y95n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2km4y95n/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/schizo_freq')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
PKR/swin-tiny-patch4-window7-224-finetuned-eurosat
|
PKR
| 2022-10-27T03:21:42Z | 61 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-27T02:53:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
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: 0.9814814814814815
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0593
- Accuracy: 0.9815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2731 | 1.0 | 190 | 0.1128 | 0.9637 |
| 0.1862 | 2.0 | 380 | 0.0759 | 0.9759 |
| 0.1409 | 3.0 | 570 | 0.0593 | 0.9815 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
roborovski/ddpm-butterflies-128
|
roborovski
| 2022-10-27T02:31:44Z | 8 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-10-26T22:44:56Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/roborovski/ddpm-butterflies-128/tensorboard?#scalars)
|
CharlieP/t5-small-nlpfinalproject-xsum
|
CharlieP
| 2022-10-27T00:12:48Z | 9 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-26T15:42:09Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: CharlieP/t5-small-nlpfinalproject-xsum
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. -->
# CharlieP/t5-small-nlpfinalproject-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.2391
- Validation Loss: 3.0511
- Train Rouge1: 21.2434
- Train Rouge2: 4.0808
- Train Rougel: 16.6836
- Train Rougelsum: 16.6460
- Train Gen Len: 18.42
- Epoch: 4
## 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.8204 | 3.2757 | 18.2829 | 2.7616 | 14.7101 | 14.7047 | 18.59 | 0 |
| 3.4646 | 3.1560 | 20.4371 | 3.6903 | 16.0587 | 16.0790 | 18.35 | 1 |
| 3.3630 | 3.1028 | 20.7907 | 3.9282 | 15.9696 | 15.8916 | 18.42 | 2 |
| 3.2904 | 3.0713 | 21.6980 | 4.3218 | 16.7261 | 16.6776 | 18.42 | 3 |
| 3.2391 | 3.0511 | 21.2434 | 4.0808 | 16.6836 | 16.6460 | 18.42 | 4 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
sd-concepts-library/anime-background-style
|
sd-concepts-library
| 2022-10-26T23:48:27Z | 0 | 7 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-26T23:39:03Z |
---
license: mit
---
### Anime Background Style on Stable Diffusion
This is the `<anime-background-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
Here are images generated with this style:
This style does not produce good results as most of the training images were too small. I'll likely train it again with bigger ones.
|
musika/musika_misc
|
musika
| 2022-10-26T22:48:07Z | 0 | 1 | null |
[
"audio",
"music",
"generation",
"tensorflow",
"arxiv:2208.08706",
"license:mit",
"region:us"
] | null | 2022-10-26T22:46:21Z |
---
license: mit
tags:
- audio
- music
- generation
- tensorflow
---
# Musika Model: musika_misc
## Model provided by: marcop
Pretrained musika_misc model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
Introduced in [this paper](https://arxiv.org/abs/2208.08706).
## How to use
You can generate music from this pretrained musika_misc model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r).
### Model description
This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
The generator has a context window of about 12 seconds of audio.
|
sd-concepts-library/kentaro-miura
|
sd-concepts-library
| 2022-10-26T22:24:04Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-26T22:23:57Z |
---
license: mit
---
### Kentaro Miura on Stable Diffusion
This is the `<kentaro-miura>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
huggingtweets/the_boolaidman
|
huggingtweets
| 2022-10-26T21:55:47Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T17:15:53Z |
---
language: en
thumbnail: http://www.huggingtweets.com/the_boolaidman/1666821342474/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1528444052034789378/E1BRWZyE_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">theboghog</div>
<div style="text-align: center; font-size: 14px;">@the_boolaidman</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from theboghog.
| Data | theboghog |
| --- | --- |
| Tweets downloaded | 184 |
| Retweets | 44 |
| Short tweets | 32 |
| Tweets kept | 108 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lez3uo4l/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @the_boolaidman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34ufbard) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34ufbard/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/the_boolaidman')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/nearcyan
|
huggingtweets
| 2022-10-26T21:10:01Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T21:08:44Z |
---
language: en
thumbnail: http://www.huggingtweets.com/nearcyan/1666818597137/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1446575702439043077/kNKnkoyI_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">nearcyan</div>
<div style="text-align: center; font-size: 14px;">@nearcyan</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from nearcyan.
| Data | nearcyan |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 132 |
| Short tweets | 136 |
| Tweets kept | 2978 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ilun9vdk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nearcyan's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16w8mubo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16w8mubo/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/nearcyan')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Kristijan/gpt2_wt103-40m_12-layer
|
Kristijan
| 2022-10-26T20:55:16Z | 3 | 0 |
pytorch
|
[
"pytorch",
"gpt2",
"language-model",
"transformer",
"wikitext-103",
"en",
"arxiv:2210.13569",
"model-index",
"region:us"
] | null | 2022-10-26T17:46:18Z |
---
language:
- en
library_name: pytorch
tags:
- language-model
- gpt2
- transformer
- wikitext-103
model-index:
- name: gpt2_wt103-40m_12-layer
results:
- task:
type: language-modeling
dataset:
type: wikitext
name: Wikitext-103
metrics:
- type: perplexity
value: 40.3
---
# Model description
paper: [Characterizing Verbatim Short-Term Memory in Neural Language Models](https://arxiv.org/abs/2210.13569)
This is a gpt2-small-like decoder-only transformer model trained on a 40M token subset of the [wikitext-103 dataset](https://paperswithcode.com/dataset/wikitext-103).
# Usage
You can download and load the model as follows:
```python
from transformers import GPT2LMHeadModel
model = GPT2LMHeadModel.from_pretrained("Kristijan/gpt2_wt103-40m_12-layer")
```
Alternatively, if you've downloaded the checkpoint files in this repository, you could also do:
```python
from transformers import GPT2LMHeadModel
model = GPT2LMHeadModel.from_pretrained(path_to_folder_with_checkpoint_files)
```
To tokenize your text for this model, you should use the [tokenizer trained on Wikitext-103](https://huggingface.co/Kristijan/wikitext-103-tokenizer)
# Intended uses
This checkpoint is intended for research purposes, for example those interested in studying the behavior of transformer language models trained on smaller datasets.
|
GhifSmile/mT5_multilingual_XLSum-finetuned-indosum
|
GhifSmile
| 2022-10-26T20:49:59Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-26T15:43:40Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mT5_multilingual_XLSum-finetuned-indosum
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. -->
# mT5_multilingual_XLSum-finetuned-indosum
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5512
- Rouge1: 0.3819
- Rouge2: 0.3102
- Rougel: 0.3529
- Rougelsum: 0.3687
## 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
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.8183 | 1.0 | 7131 | 1.5512 | 0.3819 | 0.3102 | 0.3529 | 0.3687 |
| 1.8191 | 2.0 | 14262 | 1.5512 | 0.3819 | 0.3102 | 0.3529 | 0.3687 |
| 1.8197 | 3.0 | 21393 | 1.5512 | 0.3819 | 0.3102 | 0.3529 | 0.3687 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Karelito00/beit-base-patch16-224-pt22k-ft22k-finetuned-mnist
|
Karelito00
| 2022-10-26T19:25:37Z | 49 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:mnist",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-26T15:25:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mnist
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-mnist
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: mnist
type: mnist
config: mnist
split: train
args: mnist
metrics:
- name: Accuracy
type: accuracy
value: 0.9935
---
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-finetuned-mnist
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0202
- Accuracy: 0.9935
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3376 | 1.0 | 937 | 0.0446 | 0.9855 |
| 0.318 | 2.0 | 1874 | 0.0262 | 0.9916 |
| 0.2374 | 3.0 | 2811 | 0.0202 | 0.9935 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/simerino1
|
huggingtweets
| 2022-10-26T19:03:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T19:02:08Z |
---
language: en
thumbnail: http://www.huggingtweets.com/simerino1/1666811016675/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1174133652399300608/3UF7GOrK_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">computer</div>
<div style="text-align: center; font-size: 14px;">@simerino1</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from computer.
| Data | computer |
| --- | --- |
| Tweets downloaded | 980 |
| Retweets | 366 |
| Short tweets | 96 |
| Tweets kept | 518 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/356xy36h/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @simerino1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1eld4xfg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1eld4xfg/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/simerino1')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
PraveenKishore/dqn-SpaceInvadersNoFrameskip-v4
|
PraveenKishore
| 2022-10-26T18:07:45Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-26T18:07:09Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 626.50 +/- 127.69
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PraveenKishore -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PraveenKishore -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga PraveenKishore
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
asparius/big-balanced-combined-bert
|
asparius
| 2022-10-26T17:56:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-24T19:41:04Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: big-balanced-combined-bert
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. -->
# big-balanced-combined-bert
This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2872
- Accuracy: 0.9055
- F1: 0.9061
## 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: 3
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Stamford-Maxwell/ppo-LunarLander-v2
|
Stamford-Maxwell
| 2022-10-26T17:35:19Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-26T16:01:04Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 217.69 +/- 10.37
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
huggingtweets/nuclearkatie
|
huggingtweets
| 2022-10-26T16:33:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T16:28:44Z |
---
language: en
thumbnail: http://www.huggingtweets.com/nuclearkatie/1666801970584/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1334988663629942789/nDPoGclx_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Katie 🎃Boo👻-mah</div>
<div style="text-align: center; font-size: 14px;">@nuclearkatie</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Katie 🎃Boo👻-mah.
| Data | Katie 🎃Boo👻-mah |
| --- | --- |
| Tweets downloaded | 3205 |
| Retweets | 1130 |
| Short tweets | 225 |
| Tweets kept | 1850 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vtpuc3cq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nuclearkatie's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vpu6vsq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vpu6vsq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/nuclearkatie')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Jsjjdnwjskxij6/Ffg
|
Jsjjdnwjskxij6
| 2022-10-26T15:24:13Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-10-26T15:24:13Z |
---
license: bigscience-bloom-rail-1.0
---
|
pig4431/rtm_ALBERT_5E
|
pig4431
| 2022-10-26T15:04:14Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:rotten_tomatoes",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-26T15:03:22Z |
---
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
model-index:
- name: model_output_dir
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_output_dir
This model was trained from scratch on the rotten_tomatoes 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
YumaSaito/distilbert-base-uncased-finetuned-emotion
|
YumaSaito
| 2022-10-26T15:03:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-23T14:15:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9261092845869646
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2181
- Accuracy: 0.926
- F1: 0.9261
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8618 | 1.0 | 250 | 0.3206 | 0.903 | 0.8990 |
| 0.2549 | 2.0 | 500 | 0.2181 | 0.926 | 0.9261 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
judaschrist/ddpm-butterflies-128
|
judaschrist
| 2022-10-26T14:30:42Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:json",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-10-25T15:52:48Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: json
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `json` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/judaschrist/ddpm-butterflies-128/tensorboard?#scalars)
|
mrm8488/codebert-base-finetuned-code-ner-15e
|
mrm8488
| 2022-10-26T13:42:00Z | 24 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-26T11:57:15Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: codebert-base-finetuned-code-ner-15e
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. -->
# codebert-base-finetuned-code-ner-15e
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3831
- Precision: 0.6363
- Recall: 0.6494
- F1: 0.6428
- Accuracy: 0.9197
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 191 | 0.4566 | 0.5021 | 0.4220 | 0.4585 | 0.8827 |
| No log | 2.0 | 382 | 0.3756 | 0.5699 | 0.5764 | 0.5731 | 0.9043 |
| 0.5133 | 3.0 | 573 | 0.3605 | 0.6001 | 0.5767 | 0.5882 | 0.9093 |
| 0.5133 | 4.0 | 764 | 0.3500 | 0.6130 | 0.6130 | 0.6130 | 0.9153 |
| 0.5133 | 5.0 | 955 | 0.3501 | 0.6337 | 0.6172 | 0.6254 | 0.9178 |
| 0.2203 | 6.0 | 1146 | 0.3645 | 0.6250 | 0.6352 | 0.6300 | 0.9163 |
| 0.2203 | 7.0 | 1337 | 0.3488 | 0.6263 | 0.6422 | 0.6341 | 0.9189 |
| 0.1457 | 8.0 | 1528 | 0.3575 | 0.6372 | 0.6397 | 0.6384 | 0.9194 |
| 0.1457 | 9.0 | 1719 | 0.3662 | 0.6406 | 0.6343 | 0.6375 | 0.9189 |
| 0.1457 | 10.0 | 1910 | 0.3613 | 0.6374 | 0.6473 | 0.6423 | 0.9201 |
| 0.107 | 11.0 | 2101 | 0.3716 | 0.6329 | 0.6544 | 0.6435 | 0.9197 |
| 0.107 | 12.0 | 2292 | 0.3754 | 0.6328 | 0.6487 | 0.6406 | 0.9193 |
| 0.107 | 13.0 | 2483 | 0.3826 | 0.6395 | 0.6490 | 0.6443 | 0.9204 |
| 0.0863 | 14.0 | 2674 | 0.3821 | 0.6368 | 0.6535 | 0.6451 | 0.9200 |
| 0.0863 | 15.0 | 2865 | 0.3831 | 0.6363 | 0.6494 | 0.6428 | 0.9197 |
### Evaluation results
| | Algorithm | Application | Class | Code_Block | Data_Structure | Data_Type | Device | Error_Name | File_Name | File_Type | Function | HTML_XML_Tag | Keyboard_IP | Language | Library | Operating_System | Output_Block | User_Interface_Element | User_Name | Value | Variable | Version | Website | overall_precision | overall_recall | overall_f1 | overall_accuracy |
|:----------|------------:|--------------:|------------:|-------------:|-----------------:|------------:|----------:|-------------:|------------:|------------:|-----------:|---------------:|--------------:|-----------:|-----------:|-------------------:|---------------:|-------------------------:|------------:|-----------:|-----------:|-----------:|----------:|--------------------:|-----------------:|-------------:|-------------------:|
| precision | 0 | 0.619835 | 0.680851 | 0.455629 | 0.813187 | 0.592593 | 0.395062 | 0.181818 | 0.800505 | 0.775956 | 0.757664 | 0.585366 | 0.333333 | 0.689769 | 0.61807 | 0.769231 | 0.0212766 | 0.542214 | 0.4375 | 0.370236 | 0.560479 | 0.883721 | 0.382353 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| recall | 0 | 0.677711 | 0.696864 | 0.494253 | 0.840909 | 0.8 | 0.533333 | 0.333333 | 0.794486 | 0.628319 | 0.631387 | 0.470588 | 0.0169492 | 0.81323 | 0.546279 | 0.843373 | 0.04 | 0.653846 | 0.518519 | 0.52987 | 0.54482 | 0.914089 | 0.270833 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| f1 | 0 | 0.647482 | 0.688765 | 0.474156 | 0.826816 | 0.680851 | 0.453901 | 0.235294 | 0.797484 | 0.694377 | 0.688786 | 0.521739 | 0.0322581 | 0.746429 | 0.579961 | 0.804598 | 0.0277778 | 0.592821 | 0.474576 | 0.435897 | 0.552538 | 0.898649 | 0.317073 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| number | 31 | 664 | 1148 | 696 | 264 | 120 | 60 | 30 | 798 | 226 | 822 | 102 | 59 | 257 | 551 | 83 | 25 | 442 | 54 | 385 | 859 | 291 | 48 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Linus4Lyf/test-food
|
Linus4Lyf
| 2022-10-26T13:34:09Z | 24 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-26T13:33:53Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 10 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 10,
"warmup_steps": 1,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
KGsteven/distilbert-base-uncased-finetuned-cola
|
KGsteven
| 2022-10-26T12:36:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-19T11:25:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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. -->
# distilbert-base-uncased-finetuned-cola
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:
- Loss: 0.3038
- Matthews Correlation: 0.9198
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 1.2169 | 1.0 | 626 | 0.6782 | 0.8605 |
| 0.5513 | 2.0 | 1252 | 0.4085 | 0.8998 |
| 0.343 | 3.0 | 1878 | 0.3346 | 0.9122 |
| 0.1642 | 4.0 | 2504 | 0.3106 | 0.9165 |
| 0.1216 | 5.0 | 3130 | 0.3038 | 0.9198 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Tokenizers 0.13.1
|
huggingtweets/doaenel
|
huggingtweets
| 2022-10-26T12:29:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-30T20:24:02Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1469646540612509701/x4eJRlkK_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Dantes</div>
<div style="text-align: center; font-size: 14px;">@doaenel</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Dantes.
| Data | Dantes |
| --- | --- |
| Tweets downloaded | 2609 |
| Retweets | 29 |
| Short tweets | 464 |
| Tweets kept | 2116 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1sbwdgoz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @doaenel's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/8u23yy7u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/8u23yy7u/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/doaenel')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
doodlevelyn/bert-base-NER
|
doodlevelyn
| 2022-10-26T12:28:21Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-26T07:36:50Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-NER
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-NER
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4459
- Precision: 0.3972
- Recall: 0.2378
- F1: 0.2975
- Accuracy: 0.9571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0006 | 1.0 | 7365 | 0.3996 | 0.4030 | 0.2121 | 0.2780 | 0.9573 |
| 0.0001 | 2.0 | 14730 | 0.3969 | 0.3798 | 0.2371 | 0.2920 | 0.9570 |
| 0.0 | 3.0 | 22095 | 0.4459 | 0.3972 | 0.2378 | 0.2975 | 0.9571 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
studiolike/caps
|
studiolike
| 2022-10-26T12:04:28Z | 13 | 0 |
tf-keras
|
[
"tf-keras",
"ocr",
"computer vision",
"object detection",
"image-to-text",
"license:cc0-1.0",
"region:us"
] |
image-to-text
| 2022-10-22T05:21:34Z |
---
tags:
- ocr
- computer vision
- object detection
- image-to-text
license:
- cc0-1.0
---
## Keras Implementation of OCR model for reading captcha 🤖🦹🏻
|
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