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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
automatic-speech-recognition | transformers |
<!-- 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. -->
# bangla_voice
This model is a fine-tuned version of [iftekher/bangla_voice](https://huggingface.co/iftekher/bangla_voice) on the ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bangla_voice", "results": []}]} | iftekher/bangla_voice | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T04:56:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| bangla\_voice
=============
This model is a fine-tuned version of iftekher/bangla\_voice on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 208.2614
* Wer: 0.3201
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_... |
automatic-speech-recognition | espnet |
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin ... | {"language": "fr", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["openslr"]} | espnet/aaf_openslr57 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"fr",
"dataset:openslr",
"arxiv:1804.00015",
"region:us"
] | null | 2022-03-21T04:58:18+00:00 | [
"1804.00015"
] | [
"fr"
] | TAGS
#espnet #audio #automatic-speech-recognition #fr #dataset-openslr #arxiv-1804.00015 #region-us
|
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #fr #dataset-openslr #arxiv-1804.00015 #region-us \n",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] |
null | null |
# Steins GAN (StyleGAN3)
I trained a slightly modified version of StyleGAN3 (See training_options.json) for ~3 days on image frames from the steins gate series.
Reference frames were 720p+ cropped down to 512x512
Hardware utilized:
Day 1-2 4 A100s for pretraining on raw unfiltered steins gate frames
... | {} | inarikami/SteinsGAN | null | [
"region:us"
] | null | 2022-03-21T05:43:40+00:00 | [] | [] | TAGS
#region-us
|
# Steins GAN (StyleGAN3)
I trained a slightly modified version of StyleGAN3 (See training_options.json) for ~3 days on image frames from the steins gate series.
Reference frames were 720p+ cropped down to 512x512
Hardware utilized:
Day 1-2 4 A100s for pretraining on raw unfiltered steins gate frames
... | [
"# Steins GAN (StyleGAN3)\r\n\r\nI trained a slightly modified version of StyleGAN3 (See training_options.json) for ~3 days on image frames from the steins gate series. \r\nReference frames were 720p+ cropped down to 512x512\r\n\r\n\r\nHardware utilized: \r\n\r\nDay 1-2 4 A100s for pretraining on raw unfiltered ste... | [
"TAGS\n#region-us \n",
"# Steins GAN (StyleGAN3)\r\n\r\nI trained a slightly modified version of StyleGAN3 (See training_options.json) for ~3 days on image frames from the steins gate series. \r\nReference frames were 720p+ cropped down to 512x512\r\n\r\n\r\nHardware utilized: \r\n\r\nDay 1-2 4 A100s for pretrain... |
token-classification | transformers |
<!-- 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. -->
# jo0hnd0e/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jo0hnd0e/bert-finetuned-ner", "results": []}]} | jo0hnd0e/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T06:03:50+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| jo0hnd0e/bert-finetuned-ner
===========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0276
* Validation Loss: 0.0565
* Epoch: 2
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
summarization | transformers |
<!-- 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. -->
# bart-large-finetuned-resume-summarizer-bathcsize-8-epoch-9
This model is a fine-tuned version of [Ameer05/tokenizer-repo](https:... | {"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-finetuned-resume-summarizer-bathcsize-8-epoch-9", "results": []}]} | Ameer05/bart-large-finetuned-resume-summarizer-bathcsize-8-epoch-9 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T07:05:27+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bart-large-finetuned-resume-summarizer-bathcsize-8-epoch-9
==========================================================
This model is a fine-tuned version of Ameer05/tokenizer-repo on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5988
* Rouge1: 54.4865
* Rouge2: 45.2321
* Roug... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 9\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\... |
fill-mask | transformers |
<!-- 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. -->
# twitter-roberta-base-finetuned-twitter-user-desc
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https:/... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "twitter-roberta-base-finetuned-twitter-user-desc", "results": []}]} | bdotloh/twitter-roberta-base-finetuned-twitter-user-desc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T07:33:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# twitter-roberta-base-finetuned-twitter-user-desc
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on a dataset of twitter user descriptions.
It achieves the following results on the evaluation set:
- eval_perplexity: 2.33
- epoch: 15
- step: 10635
## Model description
More information neede... | [
"# twitter-roberta-base-finetuned-twitter-user-desc\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base on a dataset of twitter user descriptions.\nIt achieves the following results on the evaluation set:\n- eval_perplexity: 2.33\n- epoch: 15\n- step: 10635",
"## Model description\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# twitter-roberta-base-finetuned-twitter-user-desc\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base on a dataset of twitter user descriptions.... |
summarization | transformers |
<!-- 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. -->
# test
This model is a fine-tuned version of [Ameer05/tokenizer-repo](https://huggingface.co/Ameer05/tokenizer-repo) on an unknown... | {"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "test", "results": []}]} | Ameer05/test | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T08:16:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| test
====
This model is a fine-tuned version of Ameer05/tokenizer-repo on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6109
* Rouge1: 54.9442
* Rouge2: 45.3299
* Rougel: 50.5219
* Rougelsum: 53.6475
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_siz... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `lichenda/chime4_fasnet_dprnn_tac`
This model was trained by LiChenda using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 98f5fb2185b98f9c08fd56492b3d3234504561e7
pip install -e .
cd egs2/chime4/enh1
./run.sh -... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["chime4"]} | lichenda/chime4_fasnet_dprnn_tac | null | [
"espnet",
"audio",
"audio-to-audio",
"dataset:chime4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-21T08:18:15+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'lichenda/chime4\_fasnet\_dprnn\_tac'
This model was trained by LiChenda using chime4 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sat Mar 19 07:17:45 CST 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14... | [
"### 'lichenda/chime4\\_fasnet\\_dprnn\\_tac'\n\n\nThis model was trained by LiChenda using chime4 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sat Mar 19 07:17:45 CST 2022'\n* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC... | [
"TAGS\n#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'lichenda/chime4\\_fasnet\\_dprnn\\_tac'\n\n\nThis model was trained by LiChenda using chime4 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------... |
sentence-similarity | sentence-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 ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | mrp/SimCSE-model-WangchanBERTa-V2 | null | [
"sentence-transformers",
"pytorch",
"camembert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T08:33:54+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #camembert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #camembert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 653519223
- CO2 Emissions (in grams): 24.879856894708393
## Validation Metrics
- Loss: 0.14671853184700012
- Accuracy: 0.9676666666666667
- Precision: 0.9794159885112494
- Recall: 0.9742857142857143
- AUC: 0.9901396825396825
- F1: 0.976... | {"language": "unk", "tags": "autonlp", "datasets": ["doctorlan/autonlp-data-ctrip"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 24.879856894708393} | doctorlan/autonlp-ctrip-653519223 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:doctorlan/autonlp-data-ctrip",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T08:38:42+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-doctorlan/autonlp-data-ctrip #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 653519223
- CO2 Emissions (in grams): 24.879856894708393
## Validation Metrics
- Loss: 0.14671853184700012
- Accuracy: 0.9676666666666667
- Precision: 0.9794159885112494
- Recall: 0.9742857142857143
- AUC: 0.9901396825396825
- F1: 0.976... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 653519223\n- CO2 Emissions (in grams): 24.879856894708393",
"## Validation Metrics\n\n- Loss: 0.14671853184700012\n- Accuracy: 0.9676666666666667\n- Precision: 0.9794159885112494\n- Recall: 0.9742857142857143\n- AUC: 0.9901396825... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-doctorlan/autonlp-data-ctrip #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 653519223\n- CO2 Emissions (in grams): 24.... |
fill-mask | transformers |
<!-- 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. -->
# test-electra-small-yelp
This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["yelp_review_full"], "metrics": ["accuracy"], "model-index": [{"name": "test-electra-small-yelp", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "yelp_review_full yelp_review_full", "type": "y... | Yaxin/electra-small-discriminator-yelp-mlm | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T08:41:41+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #fill-mask #generated_from_trainer #dataset-yelp_review_full #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# test-electra-small-yelp
This model is a fine-tuned version of google/electra-small-discriminator on the yelp_review_full yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2601
- Accuracy: 0.5677
## Model description
More information needed
## Intended uses & limitatio... | [
"# test-electra-small-yelp\n\nThis model is a fine-tuned version of google/electra-small-discriminator on the yelp_review_full yelp_review_full dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.2601\n- Accuracy: 0.5677",
"## Model description\n\nMore information needed",
"## Intended... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #generated_from_trainer #dataset-yelp_review_full #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# test-electra-small-yelp\n\nThis model is a fine-tuned version of google/electra-small-discriminator on the yelp_review... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 653619233
- CO2 Emissions (in grams): 5.919372931976555
## Validation Metrics
- Loss: 0.15083155035972595
- Accuracy: 0.952650883627876
- Precision: 0.9631399317406143
- Recall: 0.9412941961307538
- AUC: 0.9828776962419389
- F1: 0.95209... | {"language": "unk", "tags": "autonlp", "datasets": ["doctorlan/autonlp-data-JD-bert"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.919372931976555} | doctorlan/autonlp-JD-bert-653619233 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:doctorlan/autonlp-data-JD-bert",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T08:48:42+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-doctorlan/autonlp-data-JD-bert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 653619233
- CO2 Emissions (in grams): 5.919372931976555
## Validation Metrics
- Loss: 0.15083155035972595
- Accuracy: 0.952650883627876
- Precision: 0.9631399317406143
- Recall: 0.9412941961307538
- AUC: 0.9828776962419389
- F1: 0.95209... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 653619233\n- CO2 Emissions (in grams): 5.919372931976555",
"## Validation Metrics\n\n- Loss: 0.15083155035972595\n- Accuracy: 0.952650883627876\n- Precision: 0.9631399317406143\n- Recall: 0.9412941961307538\n- AUC: 0.982877696241... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-doctorlan/autonlp-data-JD-bert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 653619233\n- CO2 Emissions (in grams): 5... |
null | transformers |
<!-- 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. -->
# TrOCR-Ar-Small
This model is a fine-tuned version of [microsoft/trocr-small-stage1](https://huggingface.co/microsoft/trocr-small... | {"language": "ar", "tags": ["generated_from_trainer", "trocr"], "model-index": [{"name": "TrOCR-Ar-Small", "results": []}]} | gagan3012/TrOCR-Ar-Small | null | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"generated_from_trainer",
"trocr",
"ar",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T09:18:30+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #trocr #ar #endpoints_compatible #region-us
| TrOCR-Ar-Small
==============
This model is a fine-tuned version of microsoft/trocr-small-stage1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2771
* Cer: 0.8211
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #trocr #ar #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1... |
fill-mask | transformers |
# Megatron-BERT-large Swedish 165k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-large with 340M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was ... | {"language": ["sv"]} | KBLab/megatron-bert-large-swedish-cased-165k | null | [
"transformers",
"pytorch",
"safetensors",
"megatron-bert",
"fill-mask",
"sv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T09:38:41+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #safetensors #megatron-bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us
|
# Megatron-BERT-large Swedish 165k
This BERT model was trained using the Megatron-LM library.
The size of the model is a regular BERT-large with 340M parameters.
The model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.
Training was ... | [
"# Megatron-BERT-large Swedish 165k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-large with 340M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTra... | [
"TAGS\n#transformers #pytorch #safetensors #megatron-bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us \n",
"# Megatron-BERT-large Swedish 165k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-large with 340M parameters.\nThe model was... |
null | null | YOLOv5 🚀 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.
| {} | imkaushalpatel/YOLOv5 | null | [
"region:us"
] | null | 2022-03-21T09:49:14+00:00 | [] | [] | TAGS
#region-us
| YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.
| [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers |
# Chinese Pegasus
## Model description
This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this pap... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u5185\u5bb9\u4e30\u5bcc\u3001\u7248\u5f0f\u8bbe\u8ba1\u8003\u7a76\u3001\u56fe\u7247\u534e\u4e3d\u3001\u5370\u5236\u7cbe\u7f8e\u3002[MASK]\u7eb8\u7bb1\u5185\u8fd8\u653e\u4e86\u5145\u6c14\u888b\u7528\u4e8e\u4fdd\u62a4\u3002"}]} | uer/pegasus-large-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"pegasus",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T11:05:16+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #pegasus #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese Pegasus
===============
Model description
-----------------
This model is pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extend... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #pegasus #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | Dahn/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T11:09:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4796
* Wer: 0.3434
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
token-classification | transformers |
TODO | {"license": "apache-2.0"} | Alvenir/bert-punct-restoration-en | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T11:15:27+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
TODO | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<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/1504478055275802628/EuQs... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/victoriamonet | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T13:07:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Victoria Monét
@victoriamonet
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<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/1488548719062654976/u6qf... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/twitter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T13:07:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Twitter
@twitter
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<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/2879716355/bd3a0d75f2ec0... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rupertboneham-rupertskids-survivorcbs/1647869465531/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rupertboneham-rupertskids-survivorcbs | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T13:26:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Rupert Boneham & Rupert Boneham & SURVIVOR
@rupertboneham-rupertskids-survivorcbs
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- 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-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | peterhsu/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T13:26:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
fill-mask | transformers | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer-v2
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
### 安装
- pip install roformer==0.4.3
## 评测对比
### CLUE-dev榜单分类任务结果，base+large版本。
| | iflytek | tnews | afqmc | cmnli | ocnli | wsc | csl |
| :-----: | :-----: | :---: | :---: | :--... | {"language": "zh", "tags": ["roformer-v2", "pytorch", "tf2.0"], "inference": false} | junnyu/roformer_v2_chinese_char_base | null | [
"transformers",
"pytorch",
"roformer",
"fill-mask",
"roformer-v2",
"tf2.0",
"zh",
"arxiv:2104.09864",
"autotrain_compatible",
"region:us"
] | null | 2022-03-21T13:50:53+00:00 | [
"2104.09864"
] | [
"zh"
] | TAGS
#transformers #pytorch #roformer #fill-mask #roformer-v2 #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #region-us
| 介绍
--
### tf版本
URL
### pytorch版本+tf2.0版本
URL
### 安装
* pip install roformer==0.4.3
评测对比
----
### CLUE-dev榜单分类任务结果，base+large版本。
### CLUE-1.0-test榜单分类任务结果，base+large版本。
### 注：
* 其中RoFormerV2\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。
* 其中不带有pytorch后缀结果都是从GAU-alpha仓库复制过来的。
* 其中带有pytorch后缀的结果都是自己... | [
"### tf版本\n\n\nURL",
"### pytorch版本+tf2.0版本\n\n\nURL",
"### 安装\n\n\n* pip install roformer==0.4.3\n\n\n评测对比\n----",
"### CLUE-dev榜单分类任务结果，base+large版本。",
"### CLUE-1.0-test榜单分类任务结果，base+large版本。",
"### 注：\n\n\n* 其中RoFormerV2\\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。\n* 其中不带有pytorch后缀结果都是从GAU-alpha仓库复... | [
"TAGS\n#transformers #pytorch #roformer #fill-mask #roformer-v2 #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #region-us \n",
"### tf版本\n\n\nURL",
"### pytorch版本+tf2.0版本\n\n\nURL",
"### 安装\n\n\n* pip install roformer==0.4.3\n\n\n评测对比\n----",
"### CLUE-dev榜单分类任务结果，base+large版本。",
"### CLUE-1.0-test榜单分... |
fill-mask | transformers | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer-v2
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## 评测对比
### CLUE-dev榜单分类任务结果，base+large版本。
| | iflytek | tnews | afqmc | cmnli | ocnli | wsc | csl |
| :-----: | :-----: | :---: | :---: | :---: | :---: | :---: | :---: |
| BERT | ... | {"language": "zh", "tags": ["roformer-v2", "pytorch", "tf2.0"], "inference": false} | junnyu/roformer_v2_chinese_char_large | null | [
"transformers",
"pytorch",
"roformer",
"fill-mask",
"roformer-v2",
"tf2.0",
"zh",
"arxiv:2104.09864",
"autotrain_compatible",
"region:us"
] | null | 2022-03-21T13:51:14+00:00 | [
"2104.09864"
] | [
"zh"
] | TAGS
#transformers #pytorch #roformer #fill-mask #roformer-v2 #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #region-us
| 介绍
--
### tf版本
URL
### pytorch版本+tf2.0版本
URL
评测对比
----
### CLUE-dev榜单分类任务结果，base+large版本。
### CLUE-1.0-test榜单分类任务结果，base+large版本。
### 注：
* 其中RoFormerV2\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。
* 其中不带有pytorch后缀结果都是从GAU-alpha仓库复制过来的。
* 其中带有pytorch后缀的结果都是自己训练得出的。
* 苏神代码中拿了cls标签后直接进行了分类，而本仓库使用了如下的分... | [
"### tf版本\n\n\nURL",
"### pytorch版本+tf2.0版本\n\n\nURL\n\n\n评测对比\n----",
"### CLUE-dev榜单分类任务结果，base+large版本。",
"### CLUE-1.0-test榜单分类任务结果，base+large版本。",
"### 注：\n\n\n* 其中RoFormerV2\\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。\n* 其中不带有pytorch后缀结果都是从GAU-alpha仓库复制过来的。\n* 其中带有pytorch后缀的结果都是自己训练得出的。\n* 苏神代码中拿了c... | [
"TAGS\n#transformers #pytorch #roformer #fill-mask #roformer-v2 #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #region-us \n",
"### tf版本\n\n\nURL",
"### pytorch版本+tf2.0版本\n\n\nURL\n\n\n评测对比\n----",
"### CLUE-dev榜单分类任务结果，base+large版本。",
"### CLUE-1.0-test榜单分类任务结果，base+large版本。",
"### 注：\n\n\n* 其中RoForm... |
fill-mask | transformers | ## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer-v2
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## 评测对比
### CLUE-dev榜单分类任务结果，base+large版本。
| | iflytek | tnews | afqmc | cmnli | ocnli | wsc | csl |
| :-----: | :-----: | :---: | :---: | :---: | :---: | :---: | :---: |
| BERT | ... | {"language": "zh", "tags": ["roformer-v2", "pytorch", "tf2.0"], "inference": false} | junnyu/roformer_v2_chinese_char_small | null | [
"transformers",
"pytorch",
"roformer",
"fill-mask",
"roformer-v2",
"tf2.0",
"zh",
"arxiv:2104.09864",
"autotrain_compatible",
"region:us"
] | null | 2022-03-21T13:51:23+00:00 | [
"2104.09864"
] | [
"zh"
] | TAGS
#transformers #pytorch #roformer #fill-mask #roformer-v2 #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #region-us
| 介绍
--
### tf版本
URL
### pytorch版本+tf2.0版本
URL
评测对比
----
### CLUE-dev榜单分类任务结果，base+large版本。
### CLUE-1.0-test榜单分类任务结果，base+large版本。
### 注：
* 其中RoFormerV2\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。
* 其中不带有pytorch后缀结果都是从GAU-alpha仓库复制过来的。
* 其中带有pytorch后缀的结果都是自己训练得出的。
* 苏神代码中拿了cls标签后直接进行了分类，而本仓库使用了如下的分... | [
"### tf版本\n\n\nURL",
"### pytorch版本+tf2.0版本\n\n\nURL\n\n\n评测对比\n----",
"### CLUE-dev榜单分类任务结果，base+large版本。",
"### CLUE-1.0-test榜单分类任务结果，base+large版本。",
"### 注：\n\n\n* 其中RoFormerV2\\*表示的是未进行多任务学习的RoFormerV2模型，该模型苏神并未开源，感谢苏神的提醒。\n* 其中不带有pytorch后缀结果都是从GAU-alpha仓库复制过来的。\n* 其中带有pytorch后缀的结果都是自己训练得出的。\n* 苏神代码中拿了c... | [
"TAGS\n#transformers #pytorch #roformer #fill-mask #roformer-v2 #tf2.0 #zh #arxiv-2104.09864 #autotrain_compatible #region-us \n",
"### tf版本\n\n\nURL",
"### pytorch版本+tf2.0版本\n\n\nURL\n\n\n评测对比\n----",
"### CLUE-dev榜单分类任务结果，base+large版本。",
"### CLUE-1.0-test榜单分类任务结果，base+large版本。",
"### 注：\n\n\n* 其中RoForm... |
null | null | Binary-classification model for malicious and benign requests
```
from keras import models
models.load_model('xxx.h5')
```
---
language:
- python 3.7
---
libraries:
- keras==2.4.3
- tensorflow==2.3.1
| {} | Newt007/bin_cls_att.h5 | null | [
"region:us"
] | null | 2022-03-21T14:11:06+00:00 | [] | [] | TAGS
#region-us
| Binary-classification model for malicious and benign requests
---
language:
- python 3.7
---
libraries:
- keras==2.4.3
- tensorflow==2.3.1
| [] | [
"TAGS\n#region-us \n"
] |
null | null | Code for a Norwegian T5 that is based on the mT5 and continued pretrained on the NCC corpus.
| {"license": "apache-2.0"} | pere/norwegian-mt5x | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-03-21T14:15:28+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Code for a Norwegian T5 that is based on the mT5 and continued pretrained on the NCC corpus.
| [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | cb2-kai/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T14:19:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3568
- Accuracy: 0.86
- F1: 0.8679
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3568\n- Accuracy: 0.86\n- F1: 0.8679",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
text2text-generation | transformers | # Italian Contextual Spellchecker
The model is a fine-tuned version of [IT5](https://huggingface.co/models?search=it5)[1], specifically modelled for computing a spellchecking in the shape of a sequence-to-sequence task.
### USAGE
The input sequence should have the structure <b>seq: <i>your text</i>.</b>. Missi... | {"language": ["it"], "license": "mit", "tags": ["seq2seq"]} | Daniele/italian-spellchecker | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T14:33:20+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #it #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Italian Contextual Spellchecker
The model is a fine-tuned version of IT5[1], specifically modelled for computing a spellchecking in the shape of a sequence-to-sequence task.
### USAGE
The input sequence should have the structure <b>seq: <i>your text</i>.</b>. Missing the seq token at the beginning or the fin... | [
"# Italian Contextual Spellchecker\r\n\r\nThe model is a fine-tuned version of IT5[1], specifically modelled for computing a spellchecking in the shape of a sequence-to-sequence task.",
"### USAGE\r\n\r\nThe input sequence should have the structure <b>seq: <i>your text</i>.</b>. Missing the seq token at the begin... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #it #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Italian Contextual Spellchecker\r\n\r\nThe model is a fine-tuned version of IT5[1], specifically modelled for computing a spellchecking in the... |
null | transformers |
Optimized YOLOv5 model trained on the PWMFD medical masks dataset using transfer learning from COCO with frozen backbone, data augmentations such as mosaic, and an input image size of 320 x 320.
**Architecture:** [here](https://huggingface.co/joangog/pwmfd-yolov5/tensorboard?scroll=1#graphs&run=.)
**AP:**
- Evaluat... | {"language": ["en"], "tags": ["pytorch", "yolov5"], "datasets": ["pwmfd"], "metrics": ["coco"]} | joangog/pwmfd-yolov5 | null | [
"transformers",
"pytorch",
"tensorboard",
"yolov5",
"en",
"dataset:pwmfd",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T14:37:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #yolov5 #en #dataset-pwmfd #endpoints_compatible #region-us
|
Optimized YOLOv5 model trained on the PWMFD medical masks dataset using transfer learning from COCO with frozen backbone, data augmentations such as mosaic, and an input image size of 320 x 320.
Architecture: here
AP:
- Evaluation from pycocotools: 67%
- Evaluation from yolov5 URL script: 71%
fps:
- Nvidia Geforce... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #yolov5 #en #dataset-pwmfd #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- 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-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klu... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["pearsonr"], "model-index": [{"name": "bert-base-finetuned-sts", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "sts"}, "metrics": [{"type": "pearsonr", "value... | rurupang/bert-base-finetuned-sts | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T15:10:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-sts
=======================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4274
* Pearsonr: 0.8722
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
null | transformers |
<!-- 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. -->
# test-xlm-roberta-base-amzaon-reviews-mlm
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-rob... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "test-xlm-roberta-base-amzaon-reviews-mlm", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "amazon_reviews_multi all_languag... | Yaxin/xlm-roberta-base-amzaon-reviews-mlm | null | [
"transformers",
"pytorch",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T15:32:48+00:00 | [] | [] | TAGS
#transformers #pytorch #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #model-index #endpoints_compatible #region-us
|
# test-xlm-roberta-base-amzaon-reviews-mlm
This model is a fine-tuned version of xlm-roberta-base on the amazon_reviews_multi all_languages dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1091
- Accuracy: 0.5032
## Model description
More information needed
## Intended uses & limitatio... | [
"# test-xlm-roberta-base-amzaon-reviews-mlm\n\nThis model is a fine-tuned version of xlm-roberta-base on the amazon_reviews_multi all_languages dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.1091\n- Accuracy: 0.5032",
"## Model description\n\nMore information needed",
"## Intended... | [
"TAGS\n#transformers #pytorch #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #model-index #endpoints_compatible #region-us \n",
"# test-xlm-roberta-base-amzaon-reviews-mlm\n\nThis model is a fine-tuned version of xlm-roberta-base on the amazon_reviews_multi all_languages dataset.\nIt achieves ... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/marathi_openslr64`
This model was trained by Sujay Suresh Kumar using mr_openslr64 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 91325a1e58ca0b13494b94bf79b186b095fe0b58
pip install -e .
cd egs2/mr_openslr64/a... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["mr_openslr64"]} | espnet/marathi_openslr64 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:mr_openslr64",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-21T16:17:30+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-mr_openslr64 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/marathi\_openslr64'
This model was trained by Sujay Suresh Kumar using mr\_openslr64 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Mar 21 16:06:03 UTC 2022'
* python version: '3.9.7 (default, Sep 16 20... | [
"### 'espnet/marathi\\_openslr64'\n\n\nThis model was trained by Sujay Suresh Kumar using mr\\_openslr64 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Mar 21 16:06:03 UTC 2022'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58)... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-mr_openslr64 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/marathi\\_openslr64'\n\n\nThis model was trained by Sujay Suresh Kumar using mr\\_openslr64 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\n... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2_2`
This model was trained by YushiUeda using iemocap recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 17089cb2cf5f1275132163f6327defbcc1b1bc1b
pip inst... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["iemocap"]} | espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2_2 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:iemocap",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-21T16:49:38+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-iemocap #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 ASR model
### 'espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2_2'
This model was trained by YushiUeda using iemocap recipe in espnet.
### Demo: How to use in ESPnet2
## ASR config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR model",
"### 'espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2_2'\n\nThis model was trained by YushiUeda using iemocap recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ASR config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\n... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-iemocap #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR model",
"### 'espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2_2'\n\nThis model was trained by YushiUeda using iemocap recipe in espnet.",
"### Demo:... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 654919306
- CO2 Emissions (in grams): 0.7013851565380207
## Validation Metrics
- Loss: 2.5570242404937744
- Rouge1: 72.7273
- Rouge2: 44.4444
- RougeL: 72.7273
- RougeLsum: 72.7273
- Gen Len: 17.0
## Usage
You can use cURL to access this mode... | {"language": "unk", "tags": "autonlp", "datasets": ["McIan91/autonlp-data-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.7013851565380207} | ianMconversica/autonlp-test-654919306 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:McIan91/autonlp-data-test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T17:28:50+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-McIan91/autonlp-data-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 654919306
- CO2 Emissions (in grams): 0.7013851565380207
## Validation Metrics
- Loss: 2.5570242404937744
- Rouge1: 72.7273
- Rouge2: 44.4444
- RougeL: 72.7273
- RougeLsum: 72.7273
- Gen Len: 17.0
## Usage
You can use cURL to access this mode... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 654919306\n- CO2 Emissions (in grams): 0.7013851565380207",
"## Validation Metrics\n\n- Loss: 2.5570242404937744\n- Rouge1: 72.7273\n- Rouge2: 44.4444\n- RougeL: 72.7273\n- RougeLsum: 72.7273\n- Gen Len: 17.0",
"## Usage\n\nYou can use... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-McIan91/autonlp-data-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 654919306\n- CO2 Emissions ... |
null | null |
# Model description
This model corresponds to the paper "A Domain-adaptive Pre-training Approach for Language Bias Detection in News" (Krieger et al.,2022): https://github.com/Media-Bias-Group/A-Domain-adaptive-Pre-training-Approach-for-Language-BiasDetection-in-News
The model can be used for sequence classific... | {"license": "apache-2.0"} | Datadave09/DA-RoBERTa | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-03-21T17:42:40+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
# Model description
This model corresponds to the paper "A Domain-adaptive Pre-training Approach for Language Bias Detection in News" (Krieger et al.,2022): URL
The model can be used for sequence classification tasks of biased and non-biased language in news and media. It is initialized with *roberta-base* weig... | [
"# Model description\r\n\r\nThis model corresponds to the paper \"A Domain-adaptive Pre-training Approach for Language Bias Detection in News\" (Krieger et al.,2022): URL\r\n\r\nThe model can be used for sequence classification tasks of biased and non-biased language in news and media. It is initialized with *rober... | [
"TAGS\n#license-apache-2.0 #region-us \n",
"# Model description\r\n\r\nThis model corresponds to the paper \"A Domain-adaptive Pre-training Approach for Language Bias Detection in News\" (Krieger et al.,2022): URL\r\n\r\nThe model can be used for sequence classification tasks of biased and non-biased language in ... |
text-classification | transformers |
# DistilBERT base model (uncased) for Interactive Fiction
[`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) finetuned on a dataset of Interactive
Fiction commands.
Details on the datasets can be found [here](https://github.com/aporporato/jericho-corpora).
The resulting model scored... | {"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia"]} | Aureliano/distilbert-base-uncased-if | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T17:46:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #en #dataset-bookcorpus #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# DistilBERT base model (uncased) for Interactive Fiction
'distilbert-base-uncased' finetuned on a dataset of Interactive
Fiction commands.
Details on the datasets can be found here.
The resulting model scored an accuracy of 0.976253 on the WordNet task test set.
## How to use the discriminator in 'trans... | [
"# DistilBERT base model (uncased) for Interactive Fiction\r\n\r\n'distilbert-base-uncased' finetuned on a dataset of Interactive\r\nFiction commands.\r\n\r\nDetails on the datasets can be found here.\r\n\r\nThe resulting model scored an accuracy of 0.976253 on the WordNet task test set.",
"## How to use the disc... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #en #dataset-bookcorpus #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT base model (uncased) for Interactive Fiction\r\n\r\n'distilbert-base-uncased' finetuned on a dataset of Intera... |
null | null | ## Model Description
Quantized version [uk-ner model](https://huggingface.co/ukr-models/uk-ner). Returns B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags
## How to Use
After cloning the repository, please use the following code (download script get_predictions.py from the repository, it uses [package tokenize_uk](h... | {"language": ["uk"], "license": "mit", "tags": ["ukrainian"]} | ukr-models/uk-ner-quantized | null | [
"pytorch",
"ukrainian",
"uk",
"license:mit",
"region:us"
] | null | 2022-03-21T17:48:46+00:00 | [] | [
"uk"
] | TAGS
#pytorch #ukrainian #uk #license-mit #region-us
| ## Model Description
Quantized version uk-ner model. Returns B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags
## How to Use
After cloning the repository, please use the following code (download script get_predictions.py from the repository, it uses package tokenize_uk for splitting)
| [
"## Model Description\r\nQuantized version uk-ner model. Returns B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags",
"## How to Use\r\n\r\nAfter cloning the repository, please use the following code (download script get_predictions.py from the repository, it uses package tokenize_uk for splitting)"
] | [
"TAGS\n#pytorch #ukrainian #uk #license-mit #region-us \n",
"## Model Description\r\nQuantized version uk-ner model. Returns B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG tags",
"## How to Use\r\n\r\nAfter cloning the repository, please use the following code (download script get_predictions.py from the repository, ... |
text-generation | transformers |
<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/1421289007753859077/3X1V... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/rebeudeter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T17:55:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Billy ️
@rebeudeter
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | ## Model Description
Quantized version [uk-morph model](https://huggingface.co/ukr-models/uk-morph). Returns both UPOS and morphological features (joined by double underscore symbol)
## How to Use
After cloning the repository, please use the following code (download script get_predictions.py from the repository, it u... | {"language": ["uk"], "license": "mit", "tags": ["ukrainian"]} | ukr-models/uk-morph-quantized | null | [
"pytorch",
"ukrainian",
"uk",
"license:mit",
"region:us"
] | null | 2022-03-21T18:00:25+00:00 | [] | [
"uk"
] | TAGS
#pytorch #ukrainian #uk #license-mit #region-us
| ## Model Description
Quantized version uk-morph model. Returns both UPOS and morphological features (joined by double underscore symbol)
## How to Use
After cloning the repository, please use the following code (download script get_predictions.py from the repository, it uses package tokenize_uk for splitting)
| [
"## Model Description\nQuantized version uk-morph model. Returns both UPOS and morphological features (joined by double underscore symbol)",
"## How to Use\n\nAfter cloning the repository, please use the following code (download script get_predictions.py from the repository, it uses package tokenize_uk for splitt... | [
"TAGS\n#pytorch #ukrainian #uk #license-mit #region-us \n",
"## Model Description\nQuantized version uk-morph model. Returns both UPOS and morphological features (joined by double underscore symbol)",
"## How to Use\n\nAfter cloning the repository, please use the following code (download script get_predictions.... |
fill-mask | transformers |
<!-- 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. -->
# xtremedistil-l12-h384-uncased-finetuned-wikitext103
This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncas... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikitext"], "model-index": [{"name": "xtremedistil-l12-h384-uncased-finetuned-wikitext103", "results": []}]} | saghar/xtremedistil-l12-h384-uncased-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:wikitext",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T18:15:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-wikitext #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xtremedistil-l12-h384-uncased-finetuned-wikitext103
===================================================
This model is a fine-tuned version of microsoft/xtremedistil-l12-h384-uncased on the wikitext dataset.
It achieves the following results on the evaluation set:
* Loss: 6.7699
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-wikitext #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
summarization | transformers |
<!-- 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. -->
# distilbart-cnn-12-6-finetuned-resume-summarizer
This model is a fine-tuned version of [Ameer05/model-tokenizer-repo](https://hug... | {"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-12-6-finetuned-resume-summarizer", "results": []}]} | Ameer05/distilbart-cnn-12-6-finetuned-resume-summarizer | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T19:18:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilbart-cnn-12-6-finetuned-resume-summarizer
===============================================
This model is a fine-tuned version of Ameer05/model-tokenizer-repo on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1123
* Rouge1: 52.5826
* Rouge2: 34.3861
* Rougel: 41.8525
* Ro... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_siz... |
text-classification | transformers |
<!-- 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. -->
# clasificacion-texto-suicida-finetuned-amazon-review
This model is a fine-tuned version of [mrm8488/electricidad-small-discrimina... | {"language": "es", "tags": ["generated_from_trainer", "sentiment", "emotion"], "metrics": ["accuracy"], "widget": [{"text": "no me gusta esta vida.", "example_title": "Ejemplo 1"}, {"text": "odio estar ahi", "example_title": "Ejemplo 2"}, {"text": "me siento triste por no poder viajar", "example_title": "Ejemplo 3"}], ... | dannyvas23/clasificacion-texto-suicida-finetuned-amazon-review | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"sentiment",
"emotion",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T19:26:40+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #sentiment #emotion #es #autotrain_compatible #endpoints_compatible #region-us
| clasificacion-texto-suicida-finetuned-amazon-review
===================================================
This model is a fine-tuned version of mrm8488/electricidad-small-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1546
* Accuracy: 0.9488
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #sentiment #emotion #es #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
text-generation | transformers |
<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/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/elonmusk-garyvee/1647892564866/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-garyvee | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T19:55:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Gary Vaynerchuk
@elonmusk-garyvee
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- 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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the f... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds", "results": []}]} | mimicheng/codeparrot-ds | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T19:59:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# codeparrot-ds
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.7397
- eval_runtime: 603.8598
- eval_samples_per_second: 154.281
- eval_steps_per_second: 4.822
- epoch: 0.08
- step: 5000
## Model description
More information... | [
"# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.7397\n- eval_runtime: 603.8598\n- eval_samples_per_second: 154.281\n- eval_steps_per_second: 4.822\n- epoch: 0.08\n- step: 5000",
"## Model description\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on ... |
token-classification | transformers |
<!-- 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-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_EN
This model is a fine-tuned version of [PlanTL-GOB-ES/robert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_EN", "results": []}]} | StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_EN | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T20:11:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT\_Augmented\_EN
======================================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the CRAFT dataset.
It achieves the following results on the evaluation set:
* Loss:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_bat... |
token-classification | transformers |
<!-- 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-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES
This model is a fine-tuned version of [PlanTL-GOB-ES/robert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES", "results": []}]} | StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_ES | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T20:16:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT\_Augmented\_ES
======================================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the CRAFT dataset.
It achieves the following results on the evaluation set:
* Loss:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_bat... |
translation | transformers | # opus-mt-tc-big-zle-en
Neural machine translation model for translating from East Slavic languages (zle) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the... | {"language": ["be", "en", "ru", "uk", "zle"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-zle-en", "results": [{"task": {"type": "translation", "name": "Translation rus-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "rus eng devt... | Helsinki-NLP/opus-mt-tc-big-zle-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"en",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-21T20:55:50+00:00 | [] | [
"be",
"en",
"ru",
"uk",
"zle"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #en #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-zle-en
=====================
Neural machine translation model for translating from East Slavic languages (zle) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models ar... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #be #en #ru #uk #zle #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers |
<!-- 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-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_EN
This model is a fine-tuned version of [StivenLanche... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_EN", "results": []}]} | StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_EN | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T21:04:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT\_AugmentedTransfer\_EN
==============================================================================
This model is a fine-tuned version of StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT\_Augmented\_EN on the CRAFT dataset.
It achieves t... | [
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0\n* Pytorch 1.10.0+cu111\n* Datasets 2.0.0\n* Tokenizers 0.11.6"
] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0\n* Pytorch 1.10.0+cu111\n* Datasets 2.0.0\n* Tokenizers 0.1... |
text2text-generation | transformers | # Text2SQL Task T5-Base + Foreign Keys
This is our T5 model fine-tuned on Spider using a schema serialization which includes foreign keys
## Running the model
Inspired by the work done by [Picard](https://github.com/ElementAI/picard/) by adding foreign keys relations.
| {} | elena-soare/docu-t5-base-FK | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T21:16:08+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Text2SQL Task T5-Base + Foreign Keys
This is our T5 model fine-tuned on Spider using a schema serialization which includes foreign keys
## Running the model
Inspired by the work done by Picard by adding foreign keys relations.
| [
"# Text2SQL Task T5-Base + Foreign Keys\n\nThis is our T5 model fine-tuned on Spider using a schema serialization which includes foreign keys",
"## Running the model\n\nInspired by the work done by Picard by adding foreign keys relations."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Text2SQL Task T5-Base + Foreign Keys\n\nThis is our T5 model fine-tuned on Spider using a schema serialization which includes foreign keys",
"## Running the model\n\n... |
text2text-generation | transformers | # Text2SQL Task T5-Base + Fine-tuning on Spider + Table Augumentation
This is our T5 model fine-tuned on Spider using a schema serialization, which includes a table description for injecting domain knowledge into T5
## Running the model
Inspired by the work done by [Picard](https://github.com/ElementAI/picard/) by ad... | {} | elena-soare/bat-table-aug | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T21:23:22+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Text2SQL Task T5-Base + Fine-tuning on Spider + Table Augumentation
This is our T5 model fine-tuned on Spider using a schema serialization, which includes a table description for injecting domain knowledge into T5
## Running the model
Inspired by the work done by Picard by adding a table description to the question... | [
"# Text2SQL Task T5-Base + Fine-tuning on Spider + Table Augumentation\n\nThis is our T5 model fine-tuned on Spider using a schema serialization, which includes a table description for injecting domain knowledge into T5",
"## Running the model\n\nInspired by the work done by Picard by adding a table description t... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Text2SQL Task T5-Base + Fine-tuning on Spider + Table Augumentation\n\nThis is our T5 model fine-tuned on Spider using a schema serialization, which includes a table de... |
text2text-generation | transformers | # Text2SQL Task T5-Base + E-commerce pre-training
This is our T5 model pre-trained on 18k e-commerce pages from popular blogs and fine-tuned on Spider using a schema serialization.
## Running the model
Inspired by the work done by [Picard](https://github.com/ElementAI/picard/) by adding a pre-training step for better... | {} | elena-soare/bat-pre-trained | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-21T21:28:30+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Text2SQL Task T5-Base + E-commerce pre-training
This is our T5 model pre-trained on 18k e-commerce pages from popular blogs and fine-tuned on Spider using a schema serialization.
## Running the model
Inspired by the work done by Picard by adding a pre-training step for better performance on e-commerce data.
| [
"# Text2SQL Task T5-Base + E-commerce pre-training\n\nThis is our T5 model pre-trained on 18k e-commerce pages from popular blogs and fine-tuned on Spider using a schema serialization.",
"## Running the model\n\nInspired by the work done by Picard by adding a pre-training step for better performance on e-commerce... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Text2SQL Task T5-Base + E-commerce pre-training\n\nThis is our T5 model pre-trained on 18k e-commerce pages from popular blogs and fine-tuned on Spider using a schema s... |
automatic-speech-recognition | transformers |
<!-- 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. -->
# C0_LID_DEV
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-3... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | ntoldalagi/C0_LID_DEV | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T21:34:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| C0\_LID\_DEV
============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: inf
* Wer: 0.8267
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4... |
token-classification | transformers |
<!-- 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-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES
This model is a fine-tuned version of [StivenLanche... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES", "results": []}]} | StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T22:05:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT\_AugmentedTransfer\_ES
==============================================================================
This model is a fine-tuned version of StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT\_Augmented\_ES on the CRAFT dataset.
It achieves t... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_bat... |
text-classification | transformers |
<!-- 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. -->
# canine-s-finetuned-sst2
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "canine-s-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{... | celine98/canine-s-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T22:35:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| canine-s-finetuned-sst2
=======================
This model is a fine-tuned version of google/canine-s on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5259
* Accuracy: 0.8578
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #canine #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_common_voice_accents_indian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2_common_voice_accents_indian", "results": []}]} | willcai/wav2vec2_common_voice_accents_indian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-21T23:09:02+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2\_common\_voice\_accents\_indian
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2692
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 32\n*... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/bn_openslr53`
This model was trained by dzeinali using bn_openslr53 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout fa1b865352475b744c37f70440de1cc6b257ba70
pip install -e .
cd egs2/bn_openslr53/asr1
./run.sh --... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["bn_openslr53"]} | espnet/bn_openslr53 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:bn_openslr53",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-22T01:12:35+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-bn_openslr53 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/bn\_openslr53'
This model was trained by dzeinali using bn\_openslr53 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Jan 31 10:53:20 EST 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [G... | [
"### 'espnet/bn\\_openslr53'\n\n\nThis model was trained by dzeinali using bn\\_openslr53 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Jan 31 10:53:20 EST 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]'\n*... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-bn_openslr53 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/bn\\_openslr53'\n\n\nThis model was trained by dzeinali using bn\\_openslr53 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n-... |
text-classification | transformers |
<!-- 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. -->
# results
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on a... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "results", "results": []}]} | EALeon16/results | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T03:57:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| results
=======
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9229
* Accuracy: 0.7586
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-demo-colab", "results": []}]} | aaraki/wav2vec2-base-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T04:44:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base 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 hyperpar... | [
"# wav2vec2-base-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proced... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore in... |
image-classification | transformers |
# WEC-types
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/huggingpic... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | lazyturtl/WEC-types | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T04:53:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# WEC-types
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Attenuators
!Attenuators
#### Oscillating water column
!Oscillating water column
#### Overtopping Devices
... | [
"# WEC-types\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Attenuators\n\n!Attenuators",
"#### Oscillating water column\n\n!Oscillating water column",
... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# WEC-types\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues wit... |
text-classification | transformers |
<!-- 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... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | loulou/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T04:55:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2285
* Accuracy: 0.922
* F1: 0.9222
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | clisi2000/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T05:03:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7796
* Accuracy: 0.9158
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
token-classification | transformers |
<!-- 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. -->
# test-conll2003-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conl... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "test-conll2003-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll200... | Yaxin/xlm-roberta-base-conll2003-ner | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T07:36:34+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# test-conll2003-ner
This model is a fine-tuned version of xlm-roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0470
- Precision: 0.9459
- Recall: 0.9537
- F1: 0.9498
- Accuracy: 0.9911
## Model description
More information needed
## Intended uses & limita... | [
"# test-conll2003-ner\n\nThis model is a fine-tuned version of xlm-roberta-base on the conll2003 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0470\n- Precision: 0.9459\n- Recall: 0.9537\n- F1: 0.9498\n- Accuracy: 0.9911",
"## Model description\n\nMore information needed",
"## In... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# test-conll2003-ner\n\nThis model is a fine-tuned version of xlm-roberta-base on the conll2003 dataset.\nIt achieves t... |
token-classification | transformers |
# Work in progress
## Classification report over all languages
```
precision recall f1-score support
0 0.99 0.99 0.99 47903344
. 0.94 0.95 0.95 2798780
, 0.85 0.84 0.85 3451618
? 0.88 0.85 ... | {"language": ["en", "de", "fr", "it", "nl", "multilingual"], "license": "mit", "tags": ["punctuation prediction", "punctuation"], "datasets": "wmt/europarl", "metrics": ["f1"], "widget": [{"text": "Ondanks dat het nu bijna voorjaar is hebben we nog steds best koude dagen", "example_title": "Dutch"}, {"text": "Ho sentit... | oliverguhr/fullstop-punctuation-multilingual-base | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"punctuation prediction",
"punctuation",
"en",
"de",
"fr",
"it",
"nl",
"multilingual",
"dataset:wmt/europarl",
"arxiv:2301.03319",
"license:mit",
"autotrain_compatible",
"endpoints_com... | null | 2022-03-22T09:03:02+00:00 | [
"2301.03319"
] | [
"en",
"de",
"fr",
"it",
"nl",
"multilingual"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #punctuation prediction #punctuation #en #de #fr #it #nl #multilingual #dataset-wmt/europarl #arxiv-2301.03319 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Work in progress
## Classification report over all languages
## How to cite us
| [
"# Work in progress",
"## Classification report over all languages",
"## How to cite us"
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #punctuation prediction #punctuation #en #de #fr #it #nl #multilingual #dataset-wmt/europarl #arxiv-2301.03319 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Work in progress",
"## Classificat... |
text-generation | null | # DEMON_SLAYER DialoGPT Model v2 | {"tags": ["conversational"]} | duanxingjuan/DialoGPT-large-DEMON_SLAYER_v1 | null | [
"conversational",
"region:us"
] | null | 2022-03-22T09:21:36+00:00 | [] | [] | TAGS
#conversational #region-us
| # DEMON_SLAYER DialoGPT Model v2 | [
"# DEMON_SLAYER DialoGPT Model v2"
] | [
"TAGS\n#conversational #region-us \n",
"# DEMON_SLAYER DialoGPT Model v2"
] |
token-classification | transformers |
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | edmz/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T09:27:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0612
* Precision: 0.9247
* Recall: 0.9385
* F1: 0.9315
* Accuracy: 0.9837
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-generation | null | # DEMON_SLAYER DialoGPT Model 3 | {"tags": ["conversational"]} | duanxingjuan/DialoGPT-large-DEMON | null | [
"conversational",
"region:us"
] | null | 2022-03-22T09:58:58+00:00 | [] | [] | TAGS
#conversational #region-us
| # DEMON_SLAYER DialoGPT Model 3 | [
"# DEMON_SLAYER DialoGPT Model 3"
] | [
"TAGS\n#conversational #region-us \n",
"# DEMON_SLAYER DialoGPT Model 3"
] |
automatic-speech-recognition | transformers |
<!-- 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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-rnd-regularisation | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T10:13:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6977
* Wer: 0.1231
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
automatic-speech-recognition | transformers |
<!-- 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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-rnd-no-adapter-regularisation | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T10:13:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7177
* Wer: 0.1283
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
null | null | # Tokenizer used for all BLOOM models
Tokenizer information are provided at [https://huggingface.co/bigscience/bloom#preprocessing](https://huggingface.co/bigscience/bloom#preprocessing)
TODO: point to paper once it comes out with extra details on the tokenizer | {"license": "bigscience-bloom-rail-1.0"} | bigscience/tokenizer | null | [
"license:bigscience-bloom-rail-1.0",
"has_space",
"region:us"
] | null | 2022-03-22T10:31:14+00:00 | [] | [] | TAGS
#license-bigscience-bloom-rail-1.0 #has_space #region-us
| # Tokenizer used for all BLOOM models
Tokenizer information are provided at URL
TODO: point to paper once it comes out with extra details on the tokenizer | [
"# Tokenizer used for all BLOOM models\n\nTokenizer information are provided at URL\n\nTODO: point to paper once it comes out with extra details on the tokenizer"
] | [
"TAGS\n#license-bigscience-bloom-rail-1.0 #has_space #region-us \n",
"# Tokenizer used for all BLOOM models\n\nTokenizer information are provided at URL\n\nTODO: point to paper once it comes out with extra details on the tokenizer"
] |
question-answering | transformers |
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | caiosantillo/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T11:40:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1551
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text-generation | transformers |
<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/1505670688635564034/K4L2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/laurentozon/1647951707700/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/laurentozon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-22T12:21:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Laurent Ozon
@laurentozon
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | edwardjross/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T12:33:44+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1360
* F1: 0.8645
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
translation | transformers | # opus-mt-tc-big-fi-en
Neural machine translation model for translating from Finnish (fi) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "fi"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-fi-en", "results": [{"task": {"type": "translation", "name": "Translation fin-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "fin eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-fi-en | null | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"fi",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-22T12:39:30+00:00 | [] | [
"en",
"fi"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #en #fi #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-fi-en
====================
Neural machine translation model for translating from Finnish (fi) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #en #fi #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-fi
Neural machine translation model for translating from English (en) to Finnish (fi).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "fi"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-fi", "results": [{"task": {"type": "translation", "name": "Translation eng-fin"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng fin devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-fi | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"fi",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-22T12:45:06+00:00 | [] | [
"en",
"fi"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #fi #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-fi
====================
Neural machine translation model for translating from English (en) to Finnish (fi).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #fi #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | Dahn/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T12:52:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3965
* Wer: 0.3807
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
text-generation | transformers |
<!-- 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. -->
# distilgpt2-ttds
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It ac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-ttds", "results": []}]} | elihoole/distilgpt2-ttds | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-22T12:52:20+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-ttds
===============
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.3666
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
summarization | transformers |
# [Mukayese: Turkish NLP Strikes Back](https://arxiv.org/abs/2203.01215)
## Summarization: mukayese/transformer-turkish-summarization
_This model is uncased_, it was initialized from scratch and trained only the mlsum/tu dataset with no pre-training.
It achieves the following results on the evaluation set:
- Rouge... | {"language": ["tr"], "license": "mit", "datasets": ["mlsum"], "metrics": ["rouge"], "pipeline_tag": "summarization", "model-index": [{"name": "mukayese/transformer-turkish-summarization", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "mlsum tu", "type": "mlsum", "args": "t... | mukayese/transformer-turkish-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"tr",
"dataset:mlsum",
"arxiv:2203.01215",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T12:58:19+00:00 | [
"2203.01215"
] | [
"tr"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #summarization #tr #dataset-mlsum #arxiv-2203.01215 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Mukayese: Turkish NLP Strikes Back
## Summarization: mukayese/transformer-turkish-summarization
_This model is uncased_, it was initialized from scratch and trained only the mlsum/tu dataset with no pre-training.
It achieves the following results on the evaluation set:
- Rouge1: 43.2049
- Rouge2: 30.7082
- Rouge... | [
"# Mukayese: Turkish NLP Strikes Back",
"## Summarization: mukayese/transformer-turkish-summarization\n\n_This model is uncased_, it was initialized from scratch and trained only the mlsum/tu dataset with no pre-training.\n\nIt achieves the following results on the evaluation set:\n\n- Rouge1: 43.2049\n- Rouge2: ... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #summarization #tr #dataset-mlsum #arxiv-2203.01215 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Mukayese: Turkish NLP Strikes Back",
"## Summarization: mukayese/transformer-turkish-summarization\n... |
token-classification | transformers |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | edwardjross/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T13:12:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1686
* F1: 0.8606
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n*... |
token-classification | transformers |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | edwardjross/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T13:23:09+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2961
* F1: 0.8330
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | edwardjross/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T13:27:35+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2532
* F1: 0.8331
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | edwardjross/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T13:30:48+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3792
* F1: 0.6918
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | edwardjross/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T13:33:47+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1812
* F1: 0.8567
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n*... |
text2text-generation | transformers |
# [Mukayese: Turkish NLP Strikes Back](https://arxiv.org/abs/2203.01215)
## Summarization: mukayese/mbart-large-turkish-sum
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the mlsum/tu dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "base_model": "facebook/mbart-large-50", "model-index": [{"name": "mbart-large-turkish-sum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "mlsum tu", "type": "mlsum", "args": "tu"}, "metrics... | mukayese/mbart-large-turkish-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"arxiv:2203.01215",
"base_model:facebook/mbart-large-50",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T13:39:42+00:00 | [
"2203.01215"
] | [] | TAGS
#transformers #pytorch #safetensors #mbart #text2text-generation #generated_from_trainer #dataset-mlsum #arxiv-2203.01215 #base_model-facebook/mbart-large-50 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Mukayese: Turkish NLP Strikes Back
## Summarization: mukayese/mbart-large-turkish-sum
This model is a fine-tuned version of facebook/mbart-large-50 on the mlsum/tu dataset.
It achieves the following results on the evaluation set:
- Rouge1: 46.7011
- Rouge2: 34.0087
- Rougel: 41.5475
- Rougelsum: 43.2108
Check t... | [
"# Mukayese: Turkish NLP Strikes Back",
"## Summarization: mukayese/mbart-large-turkish-sum\n\nThis model is a fine-tuned version of facebook/mbart-large-50 on the mlsum/tu dataset.\n\nIt achieves the following results on the evaluation set:\n\n- Rouge1: 46.7011\n- Rouge2: 34.0087\n- Rougel: 41.5475\n- Rougelsum:... | [
"TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #generated_from_trainer #dataset-mlsum #arxiv-2203.01215 #base_model-facebook/mbart-large-50 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Mukayese: Turkish NLP Strikes Back",
"## Summarization: mukayese/mbart-... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 657119381
- CO2 Emissions (in grams): 3.516233232503715
## Validation Metrics
- Loss: 0.00037395773688331246
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
```
$ cu... | {"language": "en", "tags": "autonlp", "datasets": ["esiebomajeremiah/autonlp-data-email-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.516233232503715} | esiebomajeremiah/autonlp-email-classification-657119381 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:esiebomajeremiah/autonlp-data-email-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T13:54:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-esiebomajeremiah/autonlp-data-email-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 657119381
- CO2 Emissions (in grams): 3.516233232503715
## Validation Metrics
- Loss: 0.00037395773688331246
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
Or Pyt... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 657119381\n- CO2 Emissions (in grams): 3.516233232503715",
"## Validation Metrics\n\n- Loss: 0.00037395773688331246\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0",
"## Usage\n\nYou can use cURL to acc... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-esiebomajeremiah/autonlp-data-email-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 657119381\n- CO2 Emis... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2`
This model was trained by YushiUeda using swbd_sentiment recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 17089cb2cf5f1275132163f6327defbcc1b1bc1b
pip ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["swbd_sentiment"]} | espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:swbd_sentiment",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-22T14:10:53+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-swbd_sentiment #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
|
## ESPnet2 ASR model
### 'espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2'
This model was trained by YushiUeda using swbd_sentiment recipe in espnet.
### Demo: How to use in ESPnet2
## ASR config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR model",
"### 'espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2'\n\nThis model was trained by YushiUeda using swbd_sentiment recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ASR config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-swbd_sentiment #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n",
"## ESPnet2 ASR model",
"### 'espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2'\n\nThis model was trained by YushiUeda using swbd_sentiment recipe in ... |
text2text-generation | transformers |
# [Mukayese: Turkish NLP Strikes Back](https://arxiv.org/abs/2203.01215)
## Summarization: mukayese/mbart-large-turkish-sum
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum/tu dataset.
It achieves the following results on the evaluation set:
- Rouge1: 47... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "base_model": "google/mt5-base", "model-index": [{"name": "mt5-base-turkish-sum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "mlsum tu", "type": "mlsum", "args": "... | mukayese/mt5-base-turkish-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"arxiv:2203.01215",
"base_model:google/mt5-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:... | null | 2022-03-22T14:12:33+00:00 | [
"2203.01215"
] | [] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #arxiv-2203.01215 #base_model-google/mt5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mukayese: Turkish NLP Strikes Back
## Summarization: mukayese/mbart-large-turkish-sum
This model is a fine-tuned version of google/mt5-base on the mlsum/tu dataset.
It achieves the following results on the evaluation set:
- Rouge1: 47.4222
- Rouge2: 34.8624
- Rougel: 42.2487
- Rougelsum: 43.9494
Check this pap... | [
"# Mukayese: Turkish NLP Strikes Back",
"## Summarization: mukayese/mbart-large-turkish-sum\n\nThis model is a fine-tuned version of google/mt5-base on the mlsum/tu dataset.\n\nIt achieves the following results on the evaluation set:\n\n- Rouge1: 47.4222\n- Rouge2: 34.8624\n- Rougel: 42.2487\n- Rougelsum: 43.9494... | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #arxiv-2203.01215 #base_model-google/mt5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mukayese: Turkish NLP Strikes Back",... |
text-classification | transformers |
<!-- 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-finetuned-sts
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) o... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["pearsonr"], "model-index": [{"name": "roberta-base-finetuned-sts", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "sts"}, "metrics": [{"type": "pearsonr", "va... | rurupang/roberta-base-finetuned-sts | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T14:13:32+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-sts
==========================
This model is a fine-tuned version of klue/roberta-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1999
* Pearsonr: 0.9560
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_si... |
text-generation | transformers |
<!-- 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. -->
# my-gpt-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-gpt-model", "results": []}]} | bigmorning/my-gpt-model | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-22T14:15:39+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my-gpt-model
============
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.3002
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeig... |
token-classification | transformers |
# bert-base-slavic-cyrillic-upos
## Model Description
This is a BERT model pre-trained with Slavic-Cyrillic ([UD_Belarusian](https://universaldependencies.org/be/) [UD_Bulgarian](https://universaldependencies.org/bg/) [UD_Russian](https://universaldependencies.org/ru/) [UD_Serbian](https://universaldependencies.org/... | {"language": ["be", "bg", "mk", "ru", "sr", "uk"], "license": "cc-by-sa-4.0", "tags": ["belarusian", "bulgarian", "macedonian", "russian", "serbian", "ukrainian", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/bert-base-slavic-cyrillic-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"belarusian",
"bulgarian",
"macedonian",
"russian",
"serbian",
"ukrainian",
"pos",
"dependency-parsing",
"be",
"bg",
"mk",
"ru",
"sr",
"uk",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compati... | null | 2022-03-22T14:20:36+00:00 | [] | [
"be",
"bg",
"mk",
"ru",
"sr",
"uk"
] | TAGS
#transformers #pytorch #bert #token-classification #belarusian #bulgarian #macedonian #russian #serbian #ukrainian #pos #dependency-parsing #be #bg #mk #ru #sr #uk #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-slavic-cyrillic-upos
## Model Description
This is a BERT model pre-trained with Slavic-Cyrillic (UD_Belarusian UD_Bulgarian UD_Russian UD_Serbian UD_Ukrainian) for POS-tagging and dependency-parsing, derived from ruBert-base. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
... | [
"# bert-base-slavic-cyrillic-upos",
"## Model Description\n\nThis is a BERT model pre-trained with Slavic-Cyrillic (UD_Belarusian UD_Bulgarian UD_Russian UD_Serbian UD_Ukrainian) for POS-tagging and dependency-parsing, derived from ruBert-base. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #belarusian #bulgarian #macedonian #russian #serbian #ukrainian #pos #dependency-parsing #be #bg #mk #ru #sr #uk #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-slavic-cyrillic-... |
text-classification | transformers |
衛生局文本分類->六元
Data random_state=43
| {"language": "unk", "tags": "autonlp", "widget": [{"text": "\u6c11\u773e\u4f86\u96fb\u53cd\u6620\uff1a\u4e8b\u7531\uff1a\u8b77\u58eb\u614b\u5ea6\u60e1\u52a3\uff0c\u5c0d\u75c5\u4eba\u5927\u543c\u5927\u53eb\uff0c\u5c0d\u65bc\u614b\u5ea6\u60e1\u52a3\u7684\u4eba\u537b\u65bc\u8207\u9304\u7528\uff0c\u656c\u8acb\u76f8\u95dc\u... | ShihTing/HealthBureauSix | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T14:39:48+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #autotrain_compatible #endpoints_compatible #region-us
|
衛生局文本分類->六元
Data random_state=43
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | # ✨ bert-restore-punctuation
[]()
This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews).
The model predicts the punctuation and upper-casing of plai... | {"language": ["en"], "license": "mit", "tags": ["punctuation"], "datasets": ["yelp_polarity"], "metrics": ["f1"]} | speeqo/bert-restore-punctuation | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"punctuation",
"en",
"dataset:yelp_polarity",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T14:57:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #punctuation #en #dataset-yelp_polarity #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-restore-punctuation
========================
![forthebadge]()
This a bert-base-uncased model finetuned for punctuation restoration on Yelp Reviews.
The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuat... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #punctuation #en #dataset-yelp_polarity #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<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/1501714358644051970/2qQM... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/garymarcus/1647980350256/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/garymarcus | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-22T15:35:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Gary Marcus 🇺🇦
@garymarcus
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# robertuito-sentiment-analysis-hate-finetuned-sentiments_reviews_politicos
This model is a fine-tuned version of [Hate-speech-CNE... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "robertuito-sentiment-analysis-hate-finetuned-sentiments_reviews_politicos", "results": []}]} | anthonny/dehatebert-mono-spanish-finetuned-sentiments_reviews_politicos | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T15:44:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| robertuito-sentiment-analysis-hate-finetuned-sentiments\_reviews\_politicos
===========================================================================
This model is a fine-tuned version of Hate-speech-CNERG/dehatebert-mono-spanish on an unknown dataset.
It achieves the following results on the evaluation set:
* Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
automatic-speech-recognition | transformers |
<!-- 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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-bart-large-cnn | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T16:26:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3524
* Wer: 0.1042
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* trai... |
text-classification | transformers |
<!-- 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. -->
# job-listing-relevance-model
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "xlm-roberta-base", "model-index": [{"name": "job-listing-relevance-model", "results": []}]} | saattrupdan/job-listing-relevance-model | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T16:56:45+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| job-listing-relevance-model
===========================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1649
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text-generation | transformers |
<!-- 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. -->
# gpt2-xl_ft_logits_25k
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown dataset.
##... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl_ft_logits_25k", "results": []}]} | beston91/gpt2-xl_ft_logits_25k | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-22T17:03:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl\_ft\_logits\_25k
========================
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
--------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-07\n* train\\_batc... |
text-classification | transformers |
<!-- 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. -->
# job-listing-filtering-model
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "xlm-roberta-base", "model-index": [{"name": "job-listing-filtering-model", "results": []}]} | saattrupdan/job-listing-filtering-model | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T17:05:53+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| job-listing-filtering-model
===========================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1992
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text-classification | transformers |
<!-- 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. -->
# classificationEsp1
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "classificationEsp1", "results": []}]} | Zarkit/classificationEsp1 | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T17:07:31+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# classificationEsp1
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More in... | [
"# classificationEsp1\n\nThis model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and eval... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# classificationEsp1\n\nThis model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.\nIt achieves the following resu... |
token-classification | transformers |
# Bert Punctuation Restoration Danish
This model performs the punctuation restoration task in Danish. The method used is sequence classification similar to how NER models
are trained.
## Model description
TODO
### How to use
The model requires some additional inference code, hence we created an awesome little pip pa... | {"language": "da", "license": "apache-2.0", "tags": ["bert", "punctuation restoration"], "datasets": ["custom"]} | Alvenir/bert-punct-restoration-da | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"punctuation restoration",
"da",
"dataset:custom",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T17:33:25+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #bert #token-classification #punctuation restoration #da #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Bert Punctuation Restoration Danish
This model performs the punctuation restoration task in Danish. The method used is sequence classification similar to how NER models
are trained.
## Model description
TODO
### How to use
The model requires some additional inference code, hence we created an awesome little pip pa... | [
"# Bert Punctuation Restoration Danish\nThis model performs the punctuation restoration task in Danish. The method used is sequence classification similar to how NER models\nare trained.",
"## Model description\nTODO",
"### How to use\nThe model requires some additional inference code, hence we created an aweso... | [
"TAGS\n#transformers #pytorch #bert #token-classification #punctuation restoration #da #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bert Punctuation Restoration Danish\nThis model performs the punctuation restoration task in Danish. The method used is sequence... |
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