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text2text-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. -->
# article2KW_test2.0_barthez-orangesum-title_finetuned_for_mlm
This model is a fine-tuned version of [moussaKam/barthez-orangesum-... | {"license": "apache-2.0", "tags": ["mlm", "generated_from_trainer"], "model-index": [{"name": "article2KW_test2.0_barthez-orangesum-title_finetuned_for_mlm", "results": []}]} | bthomas/article2KW_test2.0_barthez-orangesum-title_finetuned_for_mlm | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"mlm",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T10:39:40+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #mlm #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| article2KW\_test2.0\_barthez-orangesum-title\_finetuned\_for\_mlm
=================================================================
This model is a fine-tuned version of moussaKam/barthez-orangesum-title on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0681
Model descripti... | [
"### 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #mlm #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\\_size:... |
translation | 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. -->
# nils-nl-to-rx-pt-v4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It ac... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "nils-nl-to-rx-pt-v4", "results": []}]} | NilsDamAi/nils-nl-to-rx-pt-v4 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T10:40:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| nils-nl-to-rx-pt-v4
===================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3352
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### 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 #t5 #text2text-generation #translation #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* le... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | amrahmed/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-24T11:12:18+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-100h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Pa... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | saahith/wav2vec2_base_100h_test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T11:12:19+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Base-100h
==================
Facebook's Wav2Vec2
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Au... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text2text-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. -->
# distilled-mt5-base-pseudo-labeling
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-sm... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Lvxue/wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-base-pseudo-labeling", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "Lvxue/wmt16 ro-en", "type": "... | Lvxue/distilled-mt5-base-pseudo-labeling | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:Lvxue/wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T11:21:12+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-Lvxue/wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-base-pseudo-labeling
This model is a fine-tuned version of google/mt5-small on the Lvxue/wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6487
- Bleu: 24.1355
- Gen Len: 44.1211
## Model description
More information needed
## Intended uses & limitations
Mor... | [
"# distilled-mt5-base-pseudo-labeling\n\nThis model is a fine-tuned version of google/mt5-small on the Lvxue/wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6487\n- Bleu: 24.1355\n- Gen Len: 44.1211",
"## Model description\n\nMore information needed",
"## Intended uses ... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-Lvxue/wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-base-pseudo-labeling\n\nThis model is a fine-tuned ver... |
null | null | blackpink logosu el üzerinde dursun | {} | Jeolnighty/sen | null | [
"region:us"
] | null | 2022-08-24T11:28:00+00:00 | [] | [] | TAGS
#region-us
| blackpink logosu el üzerinde dursun | [] | [
"TAGS\n#region-us \n"
] |
audio-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. -->
# wav2vec2-base-finetuned-coscan-age_group
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["coscan-speech"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-coscan-age_group", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "Coscan Speech", "type": "... | versae/wav2vec2-base-finetuned-coscan-age_group | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:coscan-speech",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T11:33:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-coscan-speech #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-base-finetuned-coscan-age\_group
=========================================
This model is a fine-tuned version of facebook/wav2vec2-base on the coscan-speech dataset ('age\_group').
It achieves the following results on the evaluation set:
* Loss: 0.0118
* Accuracy: 0.9984
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-coscan-speech #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
translation | 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. -->
# nils-nl-to-rx-pt-v5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It ac... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "nils-nl-to-rx-pt-v5", "results": []}]} | NilsDamAi/nils-nl-to-rx-pt-v5 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T11:35:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| nils-nl-to-rx-pt-v5
===================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3414
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### 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 #t5 #text2text-generation #translation #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* le... |
text-classification | transformers |
## Model description
This model is a version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) fine-tuned on a monkeypox misinformation dataset available [here](https://www.kaggle.com/datasets/stephencrone/monkeypox).
## Intended uses & limitations... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "monkeypox-misinformation", "results": []}]} | smcrone/monkeypox-misinformation | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-24T11:46:36+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Model description
This model is a version of digitalepidemiologylab/covid-twitter-bert-v2 fine-tuned on a monkeypox misinformation dataset available here.
## Intended uses & limitations
The model is intended to be used for the binary classification of monkeypox related tweets into one of two classes: 'misinforma... | [
"## Model description\n\nThis model is a version of digitalepidemiologylab/covid-twitter-bert-v2 fine-tuned on a monkeypox misinformation dataset available here.",
"## Intended uses & limitations\n\nThe model is intended to be used for the binary classification of monkeypox related tweets into one of two classes:... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\nThis model is a version of digitalepidemiologylab/covid-twitter-bert-v2 fine-tuned on a monkeypox misinformation dataset a... |
text-classification | transformers |
# distilbert-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
| {"language": ["en"], "license": "mit", "datasets": ["imdb"], "base_model": "distilbert-base-uncased"} | jzonthemtn/distilbert-imdb | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:imdb",
"base_model:distilbert-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T11:47:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #dataset-imdb #base_model-distilbert-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-imdb
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
| [
"# distilbert-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset."
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #en #dataset-imdb #base_model-distilbert-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset."
] |
automatic-speech-recognition | transformers | Hello | {} | saahith/wav2vec2-base-100h-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T12:02:57+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Hello | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | # **PSW/bart-base-samsumgen-xsum-conv-samsum**
1. reverse trained on samsum
2. generate from xsum
3. train on synthetic data
4. fine-tune on samsum
| {} | PSW/bart-base-samsumgen-xsum-conv-samsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T12:17:14+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # PSW/bart-base-samsumgen-xsum-conv-samsum
1. reverse trained on samsum
2. generate from xsum
3. train on synthetic data
4. fine-tune on samsum
| [
"# PSW/bart-base-samsumgen-xsum-conv-samsum\n\n1. reverse trained on samsum\n2. generate from xsum\n3. train on synthetic data\n4. fine-tune on samsum"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# PSW/bart-base-samsumgen-xsum-conv-samsum\n\n1. reverse trained on samsum\n2. generate from xsum\n3. train on synthetic data\n4. fine-tune on samsum"
] |
text2text-generation | transformers | # **PSW/bart-base-dialogsumgen-xsum-conv-samsum**
1. reverse trained on dialogsum
2. generate from xsum
3. train on synthetic data
4. fine-tune on samsum
| {} | PSW/bart-base-dialogsumgen-xsum-conv-samsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T12:17:22+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # PSW/bart-base-dialogsumgen-xsum-conv-samsum
1. reverse trained on dialogsum
2. generate from xsum
3. train on synthetic data
4. fine-tune on samsum
| [
"# PSW/bart-base-dialogsumgen-xsum-conv-samsum\n\n1. reverse trained on dialogsum\n2. generate from xsum\n3. train on synthetic data\n4. fine-tune on samsum"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# PSW/bart-base-dialogsumgen-xsum-conv-samsum\n\n1. reverse trained on dialogsum\n2. generate from xsum\n3. train on synthetic data\n4. fine-tune on samsum"
] |
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. -->
# camembert-base-finetuned-RAW20-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-finetuned-RAW20-dd", "results": []}]} | ZhiyuanQiu/camembert-base-finetuned-RAW20-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T12:34:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-RAW20-dd
=================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4309
* Precision: 0.8706
* Recall: 0.8429
* F1: 0.8565
* Accuracy: 0.9926
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 #camembert #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: 2e-05\n* train\\_batch\\_... |
text-to-image | generic |
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods... | {"library_name": "generic", "tags": ["text-to-image"]} | Arkenbrien/text-to-image-Arkenbrien | null | [
"generic",
"text-to-image",
"has_space",
"region:us"
] | null | 2022-08-24T13:06:40+00:00 | [] | [] | TAGS
#generic #text-to-image #has_space #region-us
|
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These methods are c... | [
"# Text To Image repository template\n\nThis is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n1. Specify the requirements by defining a 'URL' file.\n2. Implement the 'URL' '__init__' and '__call__' methods. These metho... | [
"TAGS\n#generic #text-to-image #has_space #region-us \n",
"# Text To Image repository template\n\nThis is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps\n1. Specify the requirements by defining a 'URL' file.\n2. Implem... |
text2text-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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | cataluna84/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T13:09:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4884
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* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #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\\... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | IbrahimMavus/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-24T13:21:53+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
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-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | hadiqa123/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T13:23:04+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-google-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: 0.5282
* Wer: 0.3302
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* 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: 8... |
text2text-generation | transformers | # **PSW/bart-base-dialogsumgen-xsum-conv-dialogsum**
1. reverse trained on dialogsum
2. generate from xsum
3. train on synthetic data
4. fine-tune on dialogsum
| {} | PSW/bart-base-dialogsumgen-xsum-conv-dialogsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T13:26:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # PSW/bart-base-dialogsumgen-xsum-conv-dialogsum
1. reverse trained on dialogsum
2. generate from xsum
3. train on synthetic data
4. fine-tune on dialogsum
| [
"# PSW/bart-base-dialogsumgen-xsum-conv-dialogsum\n\n1. reverse trained on dialogsum\n2. generate from xsum\n3. train on synthetic data\n4. fine-tune on dialogsum"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# PSW/bart-base-dialogsumgen-xsum-conv-dialogsum\n\n1. reverse trained on dialogsum\n2. generate from xsum\n3. train on synthetic data\n4. fine-tune on dialogsum"
] |
text2text-generation | transformers |
# MarianNMT-Tatoeba-en-lb | {"language": ["lb", "en"], "license": "cc-by-nc-sa-4.0", "tags": ["Translation", "MarianNMT"], "datasets": ["mbarnig/Tatoeba-en-lb"], "language_details": "ltz_Latn, eng_Latn"} | mbarnig/marianNMT-tatoeba-en-lb | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"Translation",
"MarianNMT",
"lb",
"en",
"dataset:mbarnig/Tatoeba-en-lb",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-24T13:48:54+00:00 | [] | [
"lb",
"en"
] | TAGS
#transformers #pytorch #marian #text2text-generation #Translation #MarianNMT #lb #en #dataset-mbarnig/Tatoeba-en-lb #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# MarianNMT-Tatoeba-en-lb | [
"# MarianNMT-Tatoeba-en-lb"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #Translation #MarianNMT #lb #en #dataset-mbarnig/Tatoeba-en-lb #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MarianNMT-Tatoeba-en-lb"
] |
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | jellicott/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T13:57:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0619
* Precision: 0.9347
* Recall: 0.9522
* F1: 0.9434
* Accuracy: 0.9869
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: 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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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* learning... |
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. -->
# bert-base-chinese-ws-finetuned-ner_all
This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-chinese-ws-finetuned-ner_all", "results": []}]} | HYM/bert-base-chinese-ws-finetuned-ner_all | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T14:01:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-chinese-ws-finetuned-ner\_all
=======================================
This model is a fine-tuned version of ckiplab/bert-base-chinese-ws on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0330
* Precision: 0.9723
* Recall: 0.9734
* F1: 0.9728
* Accuracy: 0.9879
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 18\n* eval\\_batch\\_size: 18\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 #bert #token-classification #generated_from_trainer #license-gpl-3.0 #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\\_size: 18\n* ev... |
null | null | Golden Circle of Floating Perfection A Halo Called Fred Steampunk | {"license": "cc-by-nc-4.0"} | geverend/GoldenCircle | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-08-24T14:04:09+00:00 | [] | [] | TAGS
#license-cc-by-nc-4.0 #region-us
| Golden Circle of Floating Perfection A Halo Called Fred Steampunk | [] | [
"TAGS\n#license-cc-by-nc-4.0 #region-us \n"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-129
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | IbrahimMavus/ddpm-butterflies-129 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-24T14:08:11+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-129
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-129",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-129",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
text-generation | transformers |
# Shaggy DialoGPT Model | {"tags": ["conversational"]} | EJoftheVern/DialoGPT-medium-shaggy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T14:08:43+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Shaggy DialoGPT Model | [
"# Shaggy DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Shaggy DialoGPT Model"
] |
token-classification | transformers | LayoutLMv3 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained with seqeval in strict mode:
| | Precision | Recall | F1-score | V... | {} | Sennodipoi/LayoutLMv3-kleisterNDA | null | [
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T14:26:45+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv3 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| LayoutLMv3 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my Master Degree thesis .
Results obtained with seqeval in strict mode:
Since I used the same segmentation strategy of the original paper i.e. using the labels to create... | [] | [
"TAGS\n#transformers #pytorch #layoutlmv3 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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. -->
# mlm_gh_issues
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "mlm_gh_issues", "results": []}]} | ericntay/mlm_gh_issues | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T15:07:53+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| mlm\_gh\_issues
===============
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2449
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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* num\\_epochs: 16",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #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: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
text-classification | transformers |
# KIZervus
This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased).
It is trained to classify german text into the classes "vulgar" speech and "non-vulgar" speech.
The data set is a collection of other labeled sources in german. For an overview, see t... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "widget": [{"text": "Ich liebe dich!", "example_title": "Non-vulgar"}, {"text": "Leck mich am arsch", "example_title": "Vulgar"}], "model-index": [{"name": "tmp3y468_8j", "results": []}]} | KIZervus/KIZervus | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T15:32:49+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| KIZervus
========
This model is a fine-tuned version of distilbert-base-german-cased.
It is trained to classify german text into the classes "vulgar" speech and "non-vulgar" speech.
The data set is a collection of other labeled sources in german. For an overview, see the github repository here: URL
Both data and trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 822, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name... | [
"TAGS\n#transformers #tf #distilbert #text-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': 'Adam', 'learning\\_rate':... |
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-small-finetuned-ner-to-multilabel-xglue-ner
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/bert_uncased_L-4_H-512_A-8", "model-index": [{"name": "bert-small-finetuned-ner-to-multilabel-xglue-ner", "results": []}]} | muhtasham/bert-small-finetuned-ner-to-multilabel-xglue-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google/bert_uncased_L-4_H-512_A-8",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T15:33:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-ner-to-multilabel-xglue-ner
================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0616
Model description
-----------------
More info... | [
"### 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: constant\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
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-small-finetuned-ner-to-multilabel-wnut-17
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https:/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-ner-to-multilabel-wnut-17", "results": []}]} | muhtasham/bert-small-finetuned-ner-to-multilabel-wnut-17 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T15:41:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-ner-to-multilabel-wnut-17
==============================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2186
Model description
-----------------
More informat... | [
"### 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: constant\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #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: 3e-05\n* tr... |
text-classification | transformers |
# distilbert-base-uncased-SpamFilter-DunnBC22
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1007
- Accuracy: 0.9907
- F1: 0.9906
## Model description
This is a bi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-SpamFilter-DunnBC22", "results": []}]} | DunnBC22/distilbert-base-uncased-SpamFilter-sm | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T15:46:08+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-SpamFilter-DunnBC22
===========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1007
* Accuracy: 0.9907
* F1: 0.9906
Model description
-----------------
T... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #distilbert #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\\_size: ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Hardwarize/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-24T15:58:02+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers | LEWIP-informal model is designed to transfer formal text into informal keeping the important slots from the source text. The slots can be either pre-defined or detected automatically.
The model is based on LEWIP (Levenshtein editing with the parallel corpus). It exploits the ability of T5 model to fill the slots betwe... | {"language": ["en"], "tags": ["formality transfer"], "licenses": ["cc-by-nc-sa"]} | s-nlp/lewit-informal | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"formality transfer",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T15:59:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #formality transfer #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| LEWIP-informal model is designed to transfer formal text into informal keeping the important slots from the source text. The slots can be either pre-defined or detected automatically.
The model is based on LEWIP (Levenshtein editing with the parallel corpus). It exploits the ability of T5 model to fill the slots betwe... | [
"## How to use",
"### Installation",
"### Generating content preserving informal paraphrases",
"#### When the important entities are known in advance",
"#### When the important entities are NOT known in advance\n\nIn case the important slots are not known, they are automatically detected with auxiliary tagg... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #formality transfer #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"### Installation",
"### Generating content preserving informal paraphrases",
"#### When the important entities are known in... |
token-classification | transformers |
This is an auxilary model for content preserving formality transfer model [LEWIP-informal](https://huggingface.co/SkolkovoInstitute/LEWIP-informal). This particular model classifies the tokens from the source formal text and label them as keep or delete/replace. These labels are further used by LEWIP-informal to gener... | {"language": ["en"], "tags": ["formality transfer"], "licenses": ["cc-by-nc-sa"]} | s-nlp/lewit-informal-tagger | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"formality transfer",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T16:03:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #formality transfer #en #autotrain_compatible #endpoints_compatible #region-us
|
This is an auxilary model for content preserving formality transfer model LEWIP-informal. This particular model classifies the tokens from the source formal text and label them as keep or delete/replace. These labels are further used by LEWIP-informal to generate content preserving informal paraphrases. | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #formality transfer #en #autotrain_compatible #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. -->
# distilrubert-tiny-cased-conversational-v1_finetuned_empathy_classifier
This model is a fine-tuned version of [DeepPavlov/distilr... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilrubert-tiny-cased-conversational-v1_finetuned_empathy_classifier", "results": []}]} | mmillet/distilrubert-tiny-cased-conversational-v1_finetuned_empathy_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T16:05:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilrubert-tiny-cased-conversational-v1\_finetuned\_empathy\_classifier
=========================================================================
This model is a fine-tuned version of DeepPavlov/distilrubert-tiny-cased-conversational-v1 on an unknown dataset.
It achieves the following results on the evaluation set:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #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: 0.0001\n* train\\_batch\\_size: 64\n* ... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | dboshardy/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-24T16:51:08+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
text2text-generation | transformers | # T5-mt5-Tatoeba-en-lb
| {"language": ["lb", "en"], "license": "cc-by-nc-sa-4.0", "tags": ["Translation", "T5", "mt5"], "datasets": ["mbarnig/Tatoeba-en-lb"], "language_details": "ltz_Latn, eng_Latn"} | mbarnig/T5-mt5-tatoeba-en-lb | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"Translation",
"T5",
"lb",
"en",
"dataset:mbarnig/Tatoeba-en-lb",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T17:11:39+00:00 | [] | [
"lb",
"en"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #Translation #T5 #lb #en #dataset-mbarnig/Tatoeba-en-lb #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # T5-mt5-Tatoeba-en-lb
| [
"# T5-mt5-Tatoeba-en-lb"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #Translation #T5 #lb #en #dataset-mbarnig/Tatoeba-en-lb #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5-mt5-Tatoeba-en-lb"
] |
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. -->
# wav2vec2-xlsr-korean-speech-emotion-recognition2_data_rebalance
This model is a fine-tuned version of [jungjongho/wav2vec2-large... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-xlsr-korean-speech-emotion-recognition2_data_rebalance", "results": []}]} | jungjongho/wav2vec2-xlsr-korean-speech-emotion-recognition2_data_rebalance | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T17:21:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-korean-speech-emotion-recognition2\_data\_rebalance
=================================================================
This model is a fine-tuned version of jungjongho/wav2vec2-large-xlsr-korean-demo-colab\_epoch15 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #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: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* ... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | dvalbuena1/Reinforce-CartPole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-24T17:39:32+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-generation | transformers |
Hello. I am a model, to be evaluated.
| {"language": "en", "tags": ["text-generation", "opt"], "inference": false} | autoevaluate/zero-shot-classification | null | [
"transformers",
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T17:44:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #opt #text-generation #en #autotrain_compatible #text-generation-inference #region-us
|
Hello. I am a model, to be evaluated.
| [] | [
"TAGS\n#transformers #pytorch #tf #jax #opt #text-generation #en #autotrain_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. -->
# camembert-base-finetuned-RAW15-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-finetuned-RAW15-dd", "results": []}]} | ZhiyuanQiu/camembert-base-finetuned-RAW15-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T18:21:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-RAW15-dd
=================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2787
* Precision: 0.8842
* Recall: 0.8761
* F1: 0.8801
* Accuracy: 0.9939
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 #camembert #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: 2e-05\n* train\\_batch\\_... |
null | transformers |
# Czech Language Electra Small Model
This repository hosts the Czech Language Electra Small Model, a language model trained on 10GB of text data. The model is based on the Electra architecture, with the primary intent of understanding and generating Czech text. It is useful for various NLP tasks such as text generati... | {"license": "cc-by-4.0"} | stistko/ElectraCzech-small | null | [
"transformers",
"pytorch",
"electra",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T18:51:09+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #license-cc-by-4.0 #endpoints_compatible #region-us
|
# Czech Language Electra Small Model
This repository hosts the Czech Language Electra Small Model, a language model trained on 10GB of text data. The model is based on the Electra architecture, with the primary intent of understanding and generating Czech text. It is useful for various NLP tasks such as text generati... | [
"# Czech Language Electra Small Model\n\nThis repository hosts the Czech Language Electra Small Model, a language model trained on 10GB of text data. The model is based on the Electra architecture, with the primary intent of understanding and generating Czech text. It is useful for various NLP tasks such as text ge... | [
"TAGS\n#transformers #pytorch #electra #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Czech Language Electra Small Model\n\nThis repository hosts the Czech Language Electra Small Model, a language model trained on 10GB of text data. The model is based on the Electra architecture, with the primary int... |
fill-mask | transformers |
# Model Description
BioTinyBERT is the result of training the [TinyBERT](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset.
# Initialisation
We initialise our model with the pre-trained chec... | {"license": "mit", "title": "README", "emoji": "\ud83c\udfc3", "colorFrom": "gray", "colorTo": "purple", "sdk": "static", "pinned": false} | nlpie/bio-tinybert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:2209.03182",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T19:07:36+00:00 | [
"2209.03182"
] | [] | TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Model Description
BioTinyBERT is the result of training the TinyBERT model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset.
# Initialisation
We initialise our model with the pre-trained checkpoints of the TinyBERT model available on Huggingface.
# Arch... | [
"# Model Description\nBioTinyBERT is the result of training the TinyBERT model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset.",
"# Initialisation\nWe initialise our model with the pre-trained checkpoints of the TinyBERT model available on Huggingface... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Description\nBioTinyBERT is the result of training the TinyBERT model in a continual learning fashion for 200k training steps using a total batch size of 192 on the Pu... |
fill-mask | transformers |
# Model Description
BioMobileBERT is the result of training the [MobileBERT-uncased](https://huggingface.co/google/mobilebert-uncased) model in a continual learning scenario for 200k training steps using a total batch size of 192 on the PubMed dataset.
# Initialisation
We initialise our model with the pre-trained ch... | {"license": "mit", "title": "README", "emoji": "\ud83c\udfc3", "colorFrom": "gray", "colorTo": "purple", "sdk": "static", "pinned": false} | nlpie/bio-mobilebert | null | [
"transformers",
"pytorch",
"mobilebert",
"fill-mask",
"arxiv:2209.03182",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-24T19:22:19+00:00 | [
"2209.03182"
] | [] | TAGS
#transformers #pytorch #mobilebert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Description
BioMobileBERT is the result of training the MobileBERT-uncased model in a continual learning scenario for 200k training steps using a total batch size of 192 on the PubMed dataset.
# Initialisation
We initialise our model with the pre-trained checkpoints of the MobileBERT-uncased model available ... | [
"# Model Description\nBioMobileBERT is the result of training the MobileBERT-uncased model in a continual learning scenario for 200k training steps using a total batch size of 192 on the PubMed dataset.",
"# Initialisation\nWe initialise our model with the pre-trained checkpoints of the MobileBERT-uncased model a... | [
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"# Model Description\nBioMobileBERT is the result of training the MobileBERT-uncased model in a continual learning scenario for 200k training steps using a tot... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1310050094
- CO2 Emissions (in grams): 4.0941
## Validation Metrics
- Loss: 0.449
- Accuracy: 0.800
- Precision: 0.855
- Recall: 0.844
- AUC: 0.851
- F1: 0.849
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["noob123/autotrain-data-app_review_bert_train"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 4.094086460501482}} | noob123/autotrain-app_review_bert_train-1310050094 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
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"dataset:noob123/autotrain-data-app_review_bert_train",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T19:28:47+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-noob123/autotrain-data-app_review_bert_train #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1310050094
- CO2 Emissions (in grams): 4.0941
## Validation Metrics
- Loss: 0.449
- Accuracy: 0.800
- Precision: 0.855
- Recall: 0.844
- AUC: 0.851
- F1: 0.849
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1310050094\n- CO2 Emissions (in grams): 4.0941",
"## Validation Metrics\n\n- Loss: 0.449\n- Accuracy: 0.800\n- Precision: 0.855\n- Recall: 0.844\n- AUC: 0.851\n- F1: 0.849",
"## Usage\n\nYou can use cURL to access this model:... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1310050094\n- CO2 Emis... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | wrice/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-24T19:45:34+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | dvalbuena1/Reinforce-Pixelcopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-24T19:51:41+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
null | null | ---
license: cc-by-2.5
child | {} | Sodaanimates/child-eater | null | [
"region:us"
] | null | 2022-08-24T20:15:56+00:00 | [] | [] | TAGS
#region-us
| ---
license: cc-by-2.5
child | [] | [
"TAGS\n#region-us \n"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddim-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | dboshardy/ddim-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDIMPipeline",
"region:us"
] | null | 2022-08-24T20:41:06+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDIMPipeline #region-us
|
# ddim-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddim-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDIMPipeline #region-us \n",
"# ddim-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
null | transformers | # Model description
LegalBert is a BERT-base-cased model fine-tuned on a subset of the `case.law` corpus. Further details can be found in this paper:
[A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering](http://ceur-ws.org/Vol-2645/paper5.pdf)
Nils Holzenberger, Andrew Blair-Stanek and Be... | {"license": "mit"} | jhu-clsp/LegalBert | null | [
"transformers",
"pytorch",
"bert",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T21:21:57+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #license-mit #endpoints_compatible #region-us
| # Model description
LegalBert is a BERT-base-cased model fine-tuned on a subset of the 'URL' corpus. Further details can be found in this paper:
A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering
Nils Holzenberger, Andrew Blair-Stanek and Benjamin Van Durme
*Proceedings of the 2020 Nat... | [
"# Model description \nLegalBert is a BERT-base-cased model fine-tuned on a subset of the 'URL' corpus. Further details can be found in this paper:\n\nA Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering \nNils Holzenberger, Andrew Blair-Stanek and Benjamin Van Durme \n*Proceedings of th... | [
"TAGS\n#transformers #pytorch #bert #license-mit #endpoints_compatible #region-us \n",
"# Model description \nLegalBert is a BERT-base-cased model fine-tuned on a subset of the 'URL' corpus. Further details can be found in this paper:\n\nA Dataset for Statutory Reasoning in Tax Law Entailment and Question Answeri... |
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. -->
# bert-base-cased-ner_cv-med-ft
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Vivo en Buenos Aires,Argentina. Egresado de la carrera Ingenier\u00eda en Computaci\u00f3n Conocimientos de lenguajes HTML, CSS, Javascript y MySQL. Experiencia trabajando en \u00e1mb... | jhonparra18/bert-base-cased-ner_cv-med-ft | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T22:03:34+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-ner\_cv-med-ft
==============================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5926
* Precision: 0.2559
* Recall: 0.3460
* F1: 0.2942
* Accuracy: 0.8368
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #bert #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: 1e-05\n* train\\_batch\\_size: 16\n*... |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | xtraXpert/DialoGPT-small-RickAndMorty2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-24T23:03:55+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
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. -->
# camembert-base-finetuned-Train_RAW10-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-finetuned-Train_RAW10-dd", "results": []}]} | ZhiyuanQiu/camembert-base-finetuned-Train_RAW10-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-24T23:06:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-Train\_RAW10-dd
========================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2175
* Precision: 0.8744
* Recall: 0.9056
* F1: 0.8897
* Accuracy: 0.9357
Model descripti... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #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: 2e-05\n* train\\_batch\\_... |
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... | stevevee0101/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T01:30:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #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: 0.1363
* F1: 0.8627
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: 24\n* eval\\_batch\\_size: 24\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 #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\\_... |
image-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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-beans", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "config": "default", "split": "tra... | liangy2/vit-base-beans | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T01:30:16+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-beans
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Accuracy: 0.9850
* Loss: 0.0711
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: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-beans #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\\_rate: 2e-05\n* ... |
text2text-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. -->
# mbart-large-50-many-to-many-mmt-finetuned-ar_AR-to-en_XX
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mbart-large-50-many-to-many-mmt-finetuned-skr-en_2.8k", "results": []}]} | Shamus/mbart-large-50-many-to-many-mmt-finetuned-skr-en_2.8k | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T02:12:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-50-many-to-many-mmt-finetuned-ar\_AR-to-en\_XX
==========================================================
This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2315
* Bleu: 28.2149
* Gen Len:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #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: 2e-05\n* train\\_batch\\_size: 1\n* eval\\... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | jcgarciaca/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-25T02:22:13+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1314150168
- CO2 Emissions (in grams): 0.0159
## Validation Metrics
- Loss: 0.445
- Accuracy: 0.801
- Precision: 0.862
- Recall: 0.835
- AUC: 0.859
- F1: 0.848
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["noob123/autotrain-data-app_review_train_roberta"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.015900476118356374}} | noob123/autotrain-app_review_train_roberta-1314150168 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:noob123/autotrain-data-app_review_train_roberta",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T03:27:11+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-noob123/autotrain-data-app_review_train_roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1314150168
- CO2 Emissions (in grams): 0.0159
## Validation Metrics
- Loss: 0.445
- Accuracy: 0.801
- Precision: 0.862
- Recall: 0.835
- AUC: 0.859
- F1: 0.848
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1314150168\n- CO2 Emissions (in grams): 0.0159",
"## Validation Metrics\n\n- Loss: 0.445\n- Accuracy: 0.801\n- Precision: 0.862\n- Recall: 0.835\n- AUC: 0.859\n- F1: 0.848",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-noob123/autotrain-data-app_review_train_roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1314150168\n- CO... |
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-xls-r-300m-german
This model is a fine-tuned version of [wav2vec2-xls-r-300m-german](https://huggingface.co/wav2vec2-xl... | {"language": ["de"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_10_0", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-300m-german", "results": []}]} | aware-ai/wav2vec2-xls-r-300m-german | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_10_0",
"generated_from_trainer",
"de",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T03:37:30+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_10_0 #generated_from_trainer #de #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-german
==========================
This model is a fine-tuned version of wav2vec2-xls-r-300m-german on the MOZILLA-FOUNDATION/COMMON\_VOICE\_10\_0 - DE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4842
* Wer: 0.3940
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-07\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_10_0 #generated_from_trainer #de #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-07\n* tr... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1314250179
- CO2 Emissions (in grams): 0.0044
## Validation Metrics
- Loss: 0.447
- Accuracy: 0.809
- Precision: 0.857
- Recall: 0.855
- AUC: 0.857
- F1: 0.856
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["noob123/autotrain-data-app_review_train_dilbert"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.004444293595896442}} | noob123/autotrain-app_review_train_dilbert-1314250179 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"unk",
"dataset:noob123/autotrain-data-app_review_train_dilbert",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T03:42:31+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-noob123/autotrain-data-app_review_train_dilbert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1314250179
- CO2 Emissions (in grams): 0.0044
## Validation Metrics
- Loss: 0.447
- Accuracy: 0.809
- Precision: 0.857
- Recall: 0.855
- AUC: 0.857
- F1: 0.856
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1314250179\n- CO2 Emissions (in grams): 0.0044",
"## Validation Metrics\n\n- Loss: 0.447\n- Accuracy: 0.809\n- Precision: 0.857\n- Recall: 0.855\n- AUC: 0.857\n- F1: 0.856",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-noob123/autotrain-data-app_review_train_dilbert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1314250179\n-... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1314550196
- CO2 Emissions (in grams): 0.0154
## Validation Metrics
- Loss: 0.443
- Accuracy: 0.813
- Precision: 0.883
- Recall: 0.829
- AUC: 0.863
- F1: 0.855
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["noob123/autotrain-data-app_review_train_albert"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.01544918242939362}} | noob123/autotrain-app_review_train_albert-1314550196 | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain",
"unk",
"dataset:noob123/autotrain-data-app_review_train_albert",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T04:11:19+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #albert #text-classification #autotrain #unk #dataset-noob123/autotrain-data-app_review_train_albert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1314550196
- CO2 Emissions (in grams): 0.0154
## Validation Metrics
- Loss: 0.443
- Accuracy: 0.813
- Precision: 0.883
- Recall: 0.829
- AUC: 0.863
- F1: 0.855
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1314550196\n- CO2 Emissions (in grams): 0.0154",
"## Validation Metrics\n\n- Loss: 0.443\n- Accuracy: 0.813\n- Precision: 0.883\n- Recall: 0.829\n- AUC: 0.863\n- F1: 0.855",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain #unk #dataset-noob123/autotrain-data-app_review_train_albert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1314550196\n- CO2 ... |
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... | admarcosai/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T04:16:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #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: 0.1365
* F1: 0.8649
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: 24\n* eval\\_batch\\_size: 24\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 #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\\_... |
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. -->
# Sayan007/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Sayan007/distilbert-base-uncased-finetuned-cola", "results": []}]} | Sayan007/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T04:31:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Sayan007/distilbert-base-uncased-finetuned-cola
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3181
* Validation Loss: 0.5309
* Train Matthews Correlation: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #distilbert #text-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': 'Ada... |
text2text-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. -->
# t5_finetune_assests
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an [cnn-dailymail](http... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5_finetune_assests", "results": []}]} | d0r1h/t5_cnn_dailymail | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-25T04:42:31+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5_finetune_assests
This model is a fine-tuned version of t5-small on an cnn-dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8677
- Rouge1: 35.9309
- Rouge2: 15.2979
- Rougel: 25.937
- Rougelsum: 33.4088
- Gen Len: 64.675
## Model description
More information needed
## Int... | [
"# t5_finetune_assests\n\nThis model is a fine-tuned version of t5-small on an cnn-dailymail dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8677\n- Rouge1: 35.9309\n- Rouge2: 15.2979\n- Rougel: 25.937\n- Rougelsum: 33.4088\n- Gen Len: 64.675",
"## Model description\n\nMore informati... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5_finetune_assests\n\nThis model is a fine-tuned version of t5-small on an cnn-dailymail dataset.\nI... |
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "co... | SiraH/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T04:50:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8442
* Matthews Correlation: 0.5443
Model description
-----------------
More informa... | [
"### 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 #distilbert #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... |
null | null | wqeqr | {} | mostafaAIuser/aicube | null | [
"region:us"
] | null | 2022-08-25T05:08:57+00:00 | [] | [] | TAGS
#region-us
| wqeqr | [] | [
"TAGS\n#region-us \n"
] |
null | null | a yellow cat on the cloud | {"license": "afl-3.0"} | hyd/test1 | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-08-25T05:23:58+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| a yellow cat on the cloud | [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | muyang/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-25T05:38:48+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
text2text-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. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [digit82/kolang-t5-base](https://huggingface.co/digit82/ko... | {"tags": ["generated_from_trainer"], "datasets": ["squad_modified_for_t5_qg"], "model-index": [{"name": "t5-end2end-questions-generation", "results": []}]} | Hyeoni/t5-e2e-questions-generation-KorQuAD | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad_modified_for_t5_qg",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-25T05:43:21+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-end2end-questions-generation
===============================
This model is a fine-tuned version of digit82/kolang-t5-base on the korquad\_modified\_for\_t5\_qg dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1449
Model description
-----------------
More information needed
Traini... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #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\\_rat... |
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-small-finetuned-ner-to-multilabel-finer-139
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-ner-to-multilabel-finer-139", "results": []}]} | muhtasham/bert-small-finetuned-ner-to-multilabel-finer-139 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T05:59:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-ner-to-multilabel-finer-139
================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0019
Model description
-----------------
More info... | [
"### 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: constant\n* num\\_epochs: 40",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #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: 3e-05\n* tr... |
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. -->
# silviacamplani/distilbert-finetuned-ner-ai
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dist... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-finetuned-ner-ai", "results": []}]} | silviacamplani/distilbert-finetuned-ner-ai | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T06:36:43+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-finetuned-ner-ai
==========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8962
* Validation Loss: 0.9088
* Train Precision: 0.3895
* Train Recall:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 1e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #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: {'inner\\_optimizer':... |
translation | 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. -->
# opus-mt-tc-big-wiki-en-ko
This model was trained from scratch on the None dataset.
It achieves the following results on the eval... | {"tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-tc-big-wiki-en-ko", "results": []}]} | jhk/opus-mt-tc-big-wiki-en-ko | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T06:49:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-tc-big-wiki-en-ko
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3008
- Bleu: 7.8420
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More i... | [
"# opus-mt-tc-big-wiki-en-ko\n\nThis model was trained from scratch on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.3008\n- Bleu: 7.8420",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-tc-big-wiki-en-ko\n\nThis model was trained from scratch on the None dataset.\nIt achieves the following results on the evaluation se... |
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. -->
# distilrubert-tiny-cased-conversational-v1_empathy_preprocessed_punct_lowercasing
This model is a fine-tuned version of [DeepPavl... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilrubert-tiny-cased-conversational-v1_empathy_preprocessed_punct_lowercasing", "results": []}]} | mmillet/distilrubert-tiny-cased-conversational-v1_empathy_preprocessed_punct_lowercasing | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T07:58:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilrubert-tiny-cased-conversational-v1\_empathy\_preprocessed\_punct\_lowercasing
====================================================================================
This model is a fine-tuned version of DeepPavlov/distilrubert-tiny-cased-conversational-v1 on an unknown dataset.
It achieves the following results ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #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: 0.0001\n* train\\_batch\\_size: 64\n* ... |
null | null |
## SpeechT5 VC Manifest
| [**Github**](https://github.com/microsoft/SpeechT5) | [**Huggingface**](https://huggingface.co/mechanicalsea/speecht5-vc) |
This manifest is an attempt to recreate the Voice Conversion recipe used for training [SpeechT5](https://aclanthology.org/2022.acl-long.393). This manifest was constru... | {"license": "mit", "tags": ["speech", "text", "cross-modal", "unified model", "self-supervised learning", "SpeechT5", "Voice Conversion"], "datasets": ["CMUARCTIC", "bdl", "clb", "rms", "slt"]} | mechanicalsea/speecht5-vc | null | [
"speech",
"text",
"cross-modal",
"unified model",
"self-supervised learning",
"SpeechT5",
"Voice Conversion",
"dataset:CMUARCTIC",
"dataset:bdl",
"dataset:clb",
"dataset:rms",
"dataset:slt",
"license:mit",
"has_space",
"region:us"
] | null | 2022-08-25T08:24:52+00:00 | [] | [] | TAGS
#speech #text #cross-modal #unified model #self-supervised learning #SpeechT5 #Voice Conversion #dataset-CMUARCTIC #dataset-bdl #dataset-clb #dataset-rms #dataset-slt #license-mit #has_space #region-us
|
## SpeechT5 VC Manifest
| Github | Huggingface |
This manifest is an attempt to recreate the Voice Conversion recipe used for training SpeechT5. This manifest was constructed using CMU ARCTIC four speakers, e.g., bdl, clb, rms, slt. There are 932 utterances for training, 100 utterances for validation, and 100 uttera... | [
"## SpeechT5 VC Manifest\n\n| Github | Huggingface |\n\nThis manifest is an attempt to recreate the Voice Conversion recipe used for training SpeechT5. This manifest was constructed using CMU ARCTIC four speakers, e.g., bdl, clb, rms, slt. There are 932 utterances for training, 100 utterances for validation, and 10... | [
"TAGS\n#speech #text #cross-modal #unified model #self-supervised learning #SpeechT5 #Voice Conversion #dataset-CMUARCTIC #dataset-bdl #dataset-clb #dataset-rms #dataset-slt #license-mit #has_space #region-us \n",
"## SpeechT5 VC Manifest\n\n| Github | Huggingface |\n\nThis manifest is an attempt to recreate the ... |
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-small-finetuned-ner-to-multilabel-finer-19
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-ner-to-multilabel-finer-19", "results": []}]} | muhtasham/bert-small-finetuned-ner-to-multilabel-finer-19 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T08:32:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-ner-to-multilabel-finer-19
===============================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1389
Model description
-----------------
More inform... | [
"### 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: constant\n* num\\_epochs: 40",
"### Train... | [
"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: 3e-05\n* train\\_batch\\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Dataset: 1:1 (unknotted : knotted)
- Model ID: 1315550258
- CO2 Emissions (in grams): 0.0216
## Validation Metrics
- Loss: 0.391
- Accuracy: 0.833
- Precision: 0.836
- Recall: 0.823
- AUC: 0.900
- F1: 0.829
## Usage
You can use cURL to access... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["dav3794/autotrain-data-demo-knots2"], "widget": [{"text": "AA sequence with spaces"}], "co2_eq_emissions": {"emissions": 0.021557396511961088}} | dav3794/demo_knots_1_1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:dav3794/autotrain-data-demo-knots2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T08:54:54+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Dataset: 1:1 (unknotted : knotted)
- Model ID: 1315550258
- CO2 Emissions (in grams): 0.0216
## Validation Metrics
- Loss: 0.391
- Accuracy: 0.833
- Precision: 0.836
- Recall: 0.823
- AUC: 0.900
- F1: 0.829
## Usage
You can use cURL to access... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Dataset: 1:1 (unknotted : knotted)\n- Model ID: 1315550258\n- CO2 Emissions (in grams): 0.0216",
"## Validation Metrics\n\n- Loss: 0.391\n- Accuracy: 0.833\n- Precision: 0.836\n- Recall: 0.823\n- AUC: 0.900\n- F1: 0.829",
"## Usage\n\n... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Dataset: 1:1 (unknotted : knotted)\n- Mode... |
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-small-finetuned-ner-to-multilabel-finer-50
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-ner-to-multilabel-finer-50", "results": []}]} | muhtasham/bert-small-finetuned-ner-to-multilabel-finer-50 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T09:03:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-ner-to-multilabel-finer-50
===============================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0716
Model description
-----------------
More inform... | [
"### 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: constant\n* num\\_epochs: 40",
"### Train... | [
"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: 3e-05\n* train\\_batch\\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Retrial9842/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | Retrial9842/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-25T09:07:06+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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-small-finetuned-ner-to-multilabel-wnut-17-new
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](htt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-ner-to-multilabel-wnut-17-new", "results": []}]} | muhtasham/bert-small-finetuned-ner-to-multilabel-wnut-17-new | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T09:15:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-ner-to-multilabel-wnut-17-new
==================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2039
Model description
-----------------
More ... | [
"### 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: constant\n* num\\_epochs: 40",
"### Train... | [
"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: 3e-05\n* train\\_batch\\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Retrial9842/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/... | Retrial9842/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-25T09:26:13+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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-small-finetuned-ner-to-multilabel-xglue-ner-new
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-small-finetuned-ner-to-multilabel-xglue-ner-new", "results": []}]} | muhtasham/bert-small-finetuned-ner-to-multilabel-xglue-ner-new | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T09:30:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small-finetuned-ner-to-multilabel-xglue-ner-new
====================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0525
Model description
-----------------
M... | [
"### 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: constant\n* num\\_epochs: 40",
"### Train... | [
"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: 3e-05\n* train\\_batch\\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1315750263
- CO2 Emissions (in grams): 0.0331
## Validation Metrics
- Loss: 0.345
- Accuracy: 0.880
- Precision: 0.894
- Recall: 0.955
- AUC: 0.888
- F1: 0.923
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["dav3794/autotrain-data-demo-knots3"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.03305239439397985}} | dav3794/demo_knots_1_4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:dav3794/autotrain-data-demo-knots3",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T09:55:03+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1315750263
- CO2 Emissions (in grams): 0.0331
## Validation Metrics
- Loss: 0.345
- Accuracy: 0.880
- Precision: 0.894
- Recall: 0.955
- AUC: 0.888
- F1: 0.923
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1315750263\n- CO2 Emissions (in grams): 0.0331",
"## Validation Metrics\n\n- Loss: 0.345\n- Accuracy: 0.880\n- Precision: 0.894\n- Recall: 0.955\n- AUC: 0.888\n- F1: 0.923",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1315750263\n- CO2 Emissions (in ... |
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. -->
# camembert-base-finetuned-Train_RAW15-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-finetuned-Train_RAW15-dd", "results": []}]} | ZhiyuanQiu/camembert-base-finetuned-Train_RAW15-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T10:05:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-Train\_RAW15-dd
========================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3634
* Precision: 0.8788
* Recall: 0.9009
* F1: 0.8897
* Accuracy: 0.9256
Model descripti... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #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: 2e-05\n* train\\_batch\\_... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1315850267
- CO2 Emissions (in grams): 0.1286
## Validation Metrics
- Loss: 0.085
- Accuracy: 0.982
- Precision: 0.984
- Recall: 0.997
- AUC: 0.761
- F1: 0.991
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["dav3794/autotrain-data-demo-knots-all"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.1285808899475734}} | dav3794/demo_knots_all | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:dav3794/autotrain-data-demo-knots-all",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T10:08:10+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots-all #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1315850267
- CO2 Emissions (in grams): 0.1286
## Validation Metrics
- Loss: 0.085
- Accuracy: 0.982
- Precision: 0.984
- Recall: 0.997
- AUC: 0.761
- F1: 0.991
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1315850267\n- CO2 Emissions (in grams): 0.1286",
"## Validation Metrics\n\n- Loss: 0.085\n- Accuracy: 0.982\n- Precision: 0.984\n- Recall: 0.997\n- AUC: 0.761\n- F1: 0.991",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots-all #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1315850267\n- CO2 Emissions (... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1315950270
- CO2 Emissions (in grams): 0.0199
## Validation Metrics
- Loss: 0.396
- Accuracy: 0.792
- Precision: 0.915
- Recall: 0.652
- AUC: 0.900
- F1: 0.761
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["dav3794/autotrain-data-demo-knots-1-2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.019866640922183956}} | dav3794/demo_knots_12_error | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:dav3794/autotrain-data-demo-knots-1-2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T10:37:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots-1-2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1315950270
- CO2 Emissions (in grams): 0.0199
## Validation Metrics
- Loss: 0.396
- Accuracy: 0.792
- Precision: 0.915
- Recall: 0.652
- AUC: 0.900
- F1: 0.761
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1315950270\n- CO2 Emissions (in grams): 0.0199",
"## Validation Metrics\n\n- Loss: 0.396\n- Accuracy: 0.792\n- Precision: 0.915\n- Recall: 0.652\n- AUC: 0.900\n- F1: 0.761",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots-1-2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1315950270\n- CO2 Emissions (... |
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-turkish-cased-finetuned-emotion
This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](http... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["turkish-multiclass-dataset"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-turkish-cased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "turkish-multiclass-d... | BenTata-86/distilbert-base-turkish-cased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:turkish-multiclass-dataset",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T10:45:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-turkish-multiclass-dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-turkish-cased-finetuned-emotion
===============================================
This model is a fine-tuned version of dbmdz/distilbert-base-turkish-cased on the turkish-multiclass-dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4861
* F1: {'f1': 0.8276613385259164... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-turkish-multiclass-dataset #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
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-najianews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-najianews", "results": []}]} | okite97/distilbert-base-uncased-najianews | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-25T10:51:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-najianews
=================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3788
* Accuracy: 0.9075
* F1: 0.9074
Model description
-----------------
More information ne... | [
"### 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 #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-classification | transformers |
Three classes sentiment analysis (positive, negative, neutral)
Based on https://huggingface.co/j-hartmann/sentiment-roberta-large-english-3-classes
**Fine-tuned using:**
- annotated sentences from book reviews in English https://www.gti.uvigo.es/index.php/en/book-reviews-annotated-dataset-for-aspect-based-sentiment-... | {"license": "mit"} | fpianz/roberta-english-book-reviews-sentiment | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.11769",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T10:56:48+00:00 | [
"1910.11769"
] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #arxiv-1910.11769 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Three classes sentiment analysis (positive, negative, neutral)
Based on URL
Fine-tuned using:
* annotated sentences from book reviews in English URL
* annotated paragraphs from amateur writers' stories URL
Performance for books:
Num examples = 1666
Batch size = 16
Performance for reviews:
Num examples ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #arxiv-1910.11769 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1316050278
- CO2 Emissions (in grams): 0.0636
## Validation Metrics
- Loss: 0.242
- Accuracy: 0.931
- Precision: 0.943
- Recall: 0.981
- AUC: 0.852
- F1: 0.962
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["dav3794/autotrain-data-demo-knots_1_8"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.06357782150508624}} | dav3794/demo_knots_1_8 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:dav3794/autotrain-data-demo-knots_1_8",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T11:13:15+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots_1_8 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1316050278
- CO2 Emissions (in grams): 0.0636
## Validation Metrics
- Loss: 0.242
- Accuracy: 0.931
- Precision: 0.943
- Recall: 0.981
- AUC: 0.852
- F1: 0.962
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1316050278\n- CO2 Emissions (in grams): 0.0636",
"## Validation Metrics\n\n- Loss: 0.242\n- Accuracy: 0.931\n- Precision: 0.943\n- Recall: 0.981\n- AUC: 0.852\n- F1: 0.962",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-dav3794/autotrain-data-demo-knots_1_8 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1316050278\n- CO2 Emissions (... |
text2text-generation | transformers |
# WikiBert2WikiBert
Bert language models can be employed for Summarization tasks. WikiBert2WikiBert is an encoder-decoder transformer model that is initialized using the Persian WikiBert Model weights. The WikiBert Model is a Bert language model which is fine-tuned on Persian Wikipedia. After using the WikiBert weigh... | {"language": ["fa"], "license": ["apache-2.0"], "tags": ["Wikipedia", "Summarizer", "bert2bert", "Summarization"], "datasets": ["pn-summary", "XL-Sum"], "metrics": ["rouge-1", "rouge-2", "rouge-l"], "task_categories": ["Summarization", "text generation"], "task_ids": ["news-articles-summarization"], "multilinguality": ... | Arashasg/WikiBert2WikiBert | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"Wikipedia",
"Summarizer",
"bert2bert",
"Summarization",
"fa",
"dataset:pn-summary",
"dataset:XL-Sum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T11:17:42+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #Wikipedia #Summarizer #bert2bert #Summarization #fa #dataset-pn-summary #dataset-XL-Sum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| WikiBert2WikiBert
=================
Bert language models can be employed for Summarization tasks. WikiBert2WikiBert is an encoder-decoder transformer model that is initialized using the Persian WikiBert Model weights. The WikiBert Model is a Bert language model which is fine-tuned on Persian Wikipedia. After using th... | [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #Wikipedia #Summarizer #bert2bert #Summarization #fa #dataset-pn-summary #dataset-XL-Sum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | generic |
# Shareded fp16 copy of [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B)
> This is fork of [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) with shareded fp16 weights implementing a custom `handler.py` as an example for how to use `gpt-j` [inference-endpoints](https://hf.co/inference... | {"library_name": "generic", "tags": ["endpoints-template"]} | philschmid/gpt-j-6B-fp16-sharded | null | [
"generic",
"pytorch",
"gptj",
"endpoints-template",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T11:19:41+00:00 | [] | [] | TAGS
#generic #pytorch #gptj #endpoints-template #endpoints_compatible #region-us
|
# Shareded fp16 copy of EleutherAI/gpt-j-6B
> This is fork of EleutherAI/gpt-j-6B with shareded fp16 weights implementing a custom 'URL' as an example for how to use 'gpt-j' inference-endpoints | [
"# Shareded fp16 copy of EleutherAI/gpt-j-6B\n\n> This is fork of EleutherAI/gpt-j-6B with shareded fp16 weights implementing a custom 'URL' as an example for how to use 'gpt-j' inference-endpoints"
] | [
"TAGS\n#generic #pytorch #gptj #endpoints-template #endpoints_compatible #region-us \n",
"# Shareded fp16 copy of EleutherAI/gpt-j-6B\n\n> This is fork of EleutherAI/gpt-j-6B with shareded fp16 weights implementing a custom 'URL' as an example for how to use 'gpt-j' inference-endpoints"
] |
sentence-similarity | sentence-transformers |
# teven/all_bs192_hardneg
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... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | teven/all_bs192_hardneg | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T11:36:54+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# teven/all_bs192_hardneg
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:
The... | [
"# teven/all_bs192_hardneg\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 install... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# teven/all_bs192_hardneg\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 sema... |
video-classification | transformers |
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et... | {"language": "en", "license": "mit", "tags": ["vision", "video-classification"], "model-index": [{"name": "nielsr/xclip-base-patch32", "results": [{"task": {"type": "video-classification"}, "dataset": {"name": "Kinetics 400", "type": "kinetics-400"}, "metrics": [{"type": "top-1 accuracy", "value": 80.4}, {"type": "top-... | microsoft/xclip-base-patch32 | null | [
"transformers",
"pytorch",
"safetensors",
"xclip",
"vision",
"video-classification",
"en",
"arxiv:2208.02816",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-25T12:06:15+00:00 | [
"2208.02816"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #xclip #vision #video-classification #en #arxiv-2208.02816 #license-mit #model-index #endpoints_compatible #has_space #region-us
|
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository.
This model was trained using 8 frames pe... | [
"# X-CLIP (base-sized model) \n\nX-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository.\n\nThis model was trained using 8 f... | [
"TAGS\n#transformers #pytorch #safetensors #xclip #vision #video-classification #en #arxiv-2208.02816 #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"# X-CLIP (base-sized model) \n\nX-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on Kinetics-400. It was intro... |
sentence-similarity | sentence-transformers |
# teven/all_bs320_vanilla
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... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | teven/all_bs320_vanilla | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T12:45:51+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# teven/all_bs320_vanilla
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:
The... | [
"# teven/all_bs320_vanilla\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 install... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# teven/all_bs320_vanilla\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 sema... |
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. -->
# my-awesome-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-awesome-model", "results": []}]} | AaronMarker/my-awesome-model | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T12:48:45+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| my-awesome-model
================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8153
* Validation Loss: 0.4165
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 500, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name... | [
"TAGS\n#transformers #tf #distilbert #text-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': 'Adam', 'learning\\_rate':... |
fill-mask | 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. -->
# distilbert-finetuned-dapt-lm-ai
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert-finetuned-dapt-lm-ai", "results": []}]} | silviacamplani/distilbert-finetuned-dapt-lm-ai | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T12:59:01+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-finetuned-dapt-lm-ai
This model is a fine-tuned version of distilbert-base-uncased 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
M... | [
"# distilbert-finetuned-dapt-lm-ai\n\nThis model is a fine-tuned version of distilbert-base-uncased 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 an... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-finetuned-dapt-lm-ai\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following resul... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Shivus/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-25T13:02:29+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-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. -->
# t5-small_corrector_15
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small_corrector_15", "results": []}]} | qBob/t5-small_corrector_15 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T13:02:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| t5-small\_corrector\_15
=======================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3416
* Rouge1: 34.7998
* Rouge2: 9.0842
* Rougel: 27.8188
* Rougelsum: 27.839
* Gen Len: 18.5561
Model description
--------------... | [
"### 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: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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\... |
translation | 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. -->
# m2m100_ru_kbd_44K
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on a... | {"language": ["ru", "kbd"], "license": "mit", "tags": ["translation"], "datasets": ["anzorq/kbd-ru"], "widget": [{"text": "\u042f \u0438\u0434\u0443 \u0434\u043e\u043c\u043e\u0439.", "example_title": "\u042f \u0438\u0434\u0443 \u0434\u043e\u043c\u043e\u0439."}, {"text": "\u0414\u0435\u0442\u0438 \u0438\u0433\u0440\u043... | anzorq/m2m100_418M_ft_ru-kbd_44K | null | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"m2m_100",
"text2text-generation",
"translation",
"ru",
"kbd",
"dataset:anzorq/kbd-ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-25T13:10:25+00:00 | [] | [
"ru",
"kbd"
] | TAGS
#transformers #pytorch #onnx #safetensors #m2m_100 #text2text-generation #translation #ru #kbd #dataset-anzorq/kbd-ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| m2m100\_ru\_kbd\_44K
====================
This model is a fine-tuned version of facebook/m2m100\_418M on a ru-kbd dataset, containing 44K sentences from books, textbooks, dictionaries etc..
It achieves the following results on the evaluation set:
* Loss: 0.9399
* Bleu: 22.389
* Gen Len: 16.562
Model description
-... | [
"### 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* 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 #onnx #safetensors #m2m_100 #text2text-generation #translation #ru #kbd #dataset-anzorq/kbd-ru #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
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. -->
# silviacamplani/distilbert-finetuned-dapt-ner-ai
This model was trained from scratch on an unknown dataset.
It achieves the following r... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-finetuned-dapt-ner-ai", "results": []}]} | silviacamplani/distilbert-finetuned-dapt-ner-ai | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-25T13:11:40+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-finetuned-dapt-ner-ai
===============================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9448
* Validation Loss: 0.9212
* Train Precision: 0.3164
* Train Recall: 0.3186
* Train ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 1e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'A... |
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