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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="Saraswati/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"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": ... | Saraswati/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-12T03:25:40+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"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-3
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "legalectra-small-spanish-becasv3-3", "results": []}]} | Evelyn18/legalectra-small-spanish-becasv3-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T03:28:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| legalectra-small-spanish-becasv3-3
==================================
This model is a fine-tuned version of mrm8488/legalectra-small-spanish on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 4.4873
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-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: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-4
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "legalectra-small-spanish-becasv3-4", "results": []}]} | Evelyn18/legalectra-small-spanish-becasv3-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T03:36:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| legalectra-small-spanish-becasv3-4
==================================
This model is a fine-tuned version of mrm8488/legalectra-small-spanish on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 4.1290
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-5
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "legalectra-small-spanish-becasv3-5", "results": []}]} | Evelyn18/legalectra-small-spanish-becasv3-5 | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T03:43:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| legalectra-small-spanish-becasv3-5
==================================
This model is a fine-tuned version of mrm8488/legalectra-small-spanish on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 4.7020
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #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: 16\n* eval\\_bat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | MiguelCosta/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T03:48:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5805
- Accuracy: 0.8767
- F1: 0.8810
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5805\n- Accuracy: 0.8767\n- F1: 0.8810",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-6
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "legalectra-small-spanish-becasv3-6", "results": []}]} | Evelyn18/legalectra-small-spanish-becasv3-6 | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T03:49:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| legalectra-small-spanish-becasv3-6
==================================
This model is a fine-tuned version of mrm8488/legalectra-small-spanish on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8441
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 150",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
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. -->
# recipe-distilbert-i
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-distilbert-i", "results": []}]} | paola-md/recipe-distilbert-i | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T03:54:59+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipe-distilbert-i
===================
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: 1.0288
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: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #distilbert #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: 2e-05\n* train\\_batch\\_size: 256\n* eva... |
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. -->
# recipe-distilbert-tis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-distilbert-tis", "results": []}]} | paola-md/recipe-distilbert-tis | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T04:19:09+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipe-distilbert-tis
=====================
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: 0.9886
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: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #distilbert #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: 2e-05\n* train\\_batch\\_size: 256\n* eva... |
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-onomatopoeia-finetune_smalldata_ESC50pretrained
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrai... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained", "results": []}]} | nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T04:31:38+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wav2vec2-onomatopoeia-finetune\_smalldata\_ESC50pretrained
==========================================================
This model is a fine-tuned version of /root/workspace/wav2vec2-pretrained\_with\_ESC50\_10000epochs\_32batch\_2022-07-09\_22-16-46/pytorch\_model.bin on the None dataset.
It achieves the following res... | [
"### 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: 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\\_step... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 16\n* s... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-24000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-24000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args":... | MiguelCosta/finetuning-sentiment-model-24000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T05:17:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-24000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3505
- Accuracy: 0.9267
- F1: 0.9274
## Model description
More information needed
## Intended uses & limitations
More i... | [
"# finetuning-sentiment-model-24000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3505\n- Accuracy: 0.9267\n- F1: 0.9274",
"## Model description\n\nMore information needed",
"## Intended uses & l... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-24000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncase... |
null | null |
This repository contains a fine-tuned BERT model trained on tweets of categories Positive, Negative, and Neutral sentiments.
| {"license": "apache-2.0", "pipeline-tag": "text-classification"} | dungeoun/pos_neg_neu_tweet_BERT | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-07-12T05:22:25+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
This repository contains a fine-tuned BERT model trained on tweets of categories Positive, Negative, and Neutral sentiments.
| [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | ArneD/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T05:47:20+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset (EN, FR, DE, IT).
It achieves the following results on the evaluation set:
* Loss: 0.1769
* F1: 0.8535
Model description
-----------------
More information needed
... | [
"### 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 #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
token-classification | null |
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': 'avx512_vnni'}`
**Number of evaluation samples:** `1000`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
... | {"tags": ["distilbert"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification"} | fxmarty/20220712-h07m20s32_example_conll2003 | null | [
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] | null | 2022-07-12T06:20:32+00:00 | [] | [] | TAGS
#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us
| task: 'token-classification'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.g4dn.2xlarge', 'supported\_instructions': 'avx512\_vnni'}'
Number of evaluation samples: '1000'
Fixed parameters:
* model\_name\_or\_path: 'elastic/distilbert-base-uncased-finetuned-conll03-english'
* dataset:
... | [] | [
"TAGS\n#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us \n"
] |
null | null | CURRENTLY UNRELEASED!! FINAL VERSION MY VARY. This model is really only supposed to be for my [patreon patrons](https://www.patreon.com/kaliyuga_ai). I ask that, unless you *truly* can't afford to pay $5 to access this model, you not use it without being a patron. Regardless, you must give attribution if you use this m... | {"license": "cc-by-4.0"} | KaliYuga/rpgitem | null | [
"license:cc-by-4.0",
"region:us"
] | null | 2022-07-12T06:31:28+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
| CURRENTLY UNRELEASED!! FINAL VERSION MY VARY. This model is really only supposed to be for my patreon patrons. I ask that, unless you *truly* can't afford to pay $5 to access this model, you not use it without being a patron. Regardless, you must give attribution if you use this model in any product/app/game, etc | [] | [
"TAGS\n#license-cc-by-4.0 #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. -->
# recipe-distilbert-upper-tIs
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-distilbert-upper-tIs", "results": []}]} | paola-md/recipe-distilbert-upper-tIs | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T06:36:46+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipe-distilbert-upper-tIs
===========================
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: 0.8746
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: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #distilbert #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: 2e-05\n* train\\_batch\\_size: 256\n* eva... |
token-classification | null |
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx512'}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
... | {"tags": ["distilbert"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification"} | fxmarty/20220712-h08m02s04_example | null | [
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
] | null | 2022-07-12T07:02:04+00:00 | [] | [] | TAGS
#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us
| task: 'token-classification'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.m5.2xlarge', 'supported\_instructions': 'avx512'}'
Number of evaluation samples: 'All dataset'
Fixed parameters:
* model\_name\_or\_path: 'elastic/distilbert-base-uncased-finetuned-conll03-english'
* dataset:
... | [] | [
"TAGS\n#tensorboard #distilbert #token-classification #dataset-conll2003 #region-us \n"
] |
image-classification | null |
**task**: `image-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `nateraw/vit-base-beans`
* **dataset**:
* **path**: `beans... | {"tags": ["vit"], "datasets": ["beans"], "metrics": ["accuracy"], "pipeline_tag": "image-classification"} | fxmarty/20220712-h08m05s32_ | null | [
"tensorboard",
"vit",
"image-classification",
"dataset:beans",
"region:us"
] | null | 2022-07-12T07:05:32+00:00 | [] | [] | TAGS
#tensorboard #vit #image-classification #dataset-beans #region-us
| task: 'image-classification'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.g4dn.2xlarge', 'supported\_instructions': None}'
Number of evaluation samples: 'All dataset'
Fixed parameters:
* model\_name\_or\_path: 'nateraw/vit-base-beans'
* dataset:
+ path: 'beans'
+ eval\_split: 'vali... | [] | [
"TAGS\n#tensorboard #vit #image-classification #dataset-beans #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-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": []}]} | MarLac/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 | 2022-07-12T07:24:30+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: 0.5816
* Wer: 0.3533
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: 4\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: 4... |
fill-mask | transformers |
# PROP-wiki
**PROP**, **P**re-training with **R**epresentative w**O**rds **P**rediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of ... | {"language": "en", "license": "apache-2.0", "tags": ["PROP", "Pretrain4IR", "fill-mask"], "datasets": ["wikipedia"]} | xyma/PROP-wiki | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"PROP",
"Pretrain4IR",
"fill-mask",
"en",
"dataset:wikipedia",
"arxiv:2010.10137",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T07:29:02+00:00 | [
"2010.10137"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #PROP #Pretrain4IR #fill-mask #en #dataset-wikipedia #arxiv-2010.10137 #license-apache-2.0 #endpoints_compatible #region-us
|
# PROP-wiki
PROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text representative ... | [
"# PROP-wiki\n\nPROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text representa... | [
"TAGS\n#transformers #pytorch #bert #pretraining #PROP #Pretrain4IR #fill-mask #en #dataset-wikipedia #arxiv-2010.10137 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# PROP-wiki\n\nPROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieval. PRO... |
fill-mask | transformers |
# PROP-marco
**PROP**, **P**re-training with **R**epresentative w**O**rds **P**rediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of... | {"language": "en", "license": "apache-2.0", "tags": ["PROP", "fill-mask", "Pretrain4IR"], "datasets": ["msmarco"]} | xyma/PROP-marco | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"PROP",
"fill-mask",
"Pretrain4IR",
"en",
"dataset:msmarco",
"arxiv:2010.10137",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T07:55:28+00:00 | [
"2010.10137"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #PROP #fill-mask #Pretrain4IR #en #dataset-msmarco #arxiv-2010.10137 #license-apache-2.0 #endpoints_compatible #region-us
|
# PROP-marco
PROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text representative... | [
"# PROP-marco\n\nPROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text represent... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #PROP #fill-mask #Pretrain4IR #en #dataset-msmarco #arxiv-2010.10137 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# PROP-marco\n\nPROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieva... |
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. -->
# newsmodelclassification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "newsmodelclassification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "defaul... | aatmasidha/newsmodelclassification | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T07:59:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| newsmodelclassification
=======================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2065
* Accuracy: 0.927
* F1: 0.9271
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
null | transformers |
# PROP-marco-step400k
**PROP**, **P**re-training with **R**epresentative w**O**rds **P**rediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the... | {"language": "en", "license": "apache-2.0", "tags": ["PROP", "Pretrain4IR"], "datasets": ["msmarco"]} | xyma/PROP-marco-step400k | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"PROP",
"Pretrain4IR",
"en",
"dataset:msmarco",
"arxiv:2010.10137",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T08:06:57+00:00 | [
"2010.10137"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #PROP #Pretrain4IR #en #dataset-msmarco #arxiv-2010.10137 #license-apache-2.0 #endpoints_compatible #region-us
|
# PROP-marco-step400k
PROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text repre... | [
"# PROP-marco-step400k\n\nPROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text ... | [
"TAGS\n#transformers #pytorch #bert #pretraining #PROP #Pretrain4IR #en #dataset-msmarco #arxiv-2010.10137 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# PROP-marco-step400k\n\nPROP, Pre-training with Representative wOrds Prediction, is a new pre-training method tailored for ad-hoc retrieval. PROP i... |
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-newsmodelclassification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-newsmodelclassification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "e... | aatmasidha/distilbert-base-uncased-newsmodelclassification | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T08:10:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-newsmodelclassification
===============================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2177
* Accuracy: 0.928
* F1: 0.9278
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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", "ar... | moonzi/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 | 2022-07-12T08:23:29+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: 0.5608
* Matthews Correlation: 0.5384
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... |
token-classification | transformers | # tner/bert-large-tweetner7-2021
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-param... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/bert-large-tweetner7-2021 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T08:24:07+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/bert-large-tweetner7-2021
This model is a fine-tuned version of bert-large-cased on the
tner/tweetner7 dataset ('train_2021' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.5974... | [
"# tner/bert-large-tweetner7-2021\n\nThis model is a fine-tuned version of bert-large-cased on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (mic... | [
"TAGS\n#transformers #pytorch #bert #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/bert-large-tweetner7-2021\n\nThis model is a fine-tuned version of bert-large-cased on the \ntner/tweetner7 dataset ('train_2021' split).\nModel fine-t... |
token-classification | transformers | # tner/bert-large-tweetner7-all
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-paramet... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/bert-large-tweetner7-all | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T08:28:41+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/bert-large-tweetner7-all
This model is a fine-tuned version of bert-large-cased on the
tner/tweetner7 dataset ('train_all' split).
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.635801... | [
"# tner/bert-large-tweetner7-all\n\nThis model is a fine-tuned version of bert-large-cased on the \ntner/tweetner7 dataset ('train_all' split).\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set of 2021:\n- F1 (micro... | [
"TAGS\n#transformers #pytorch #bert #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/bert-large-tweetner7-all\n\nThis model is a fine-tuned version of bert-large-cased on the \ntner/tweetner7 dataset ('train_all' split).\nModel fine-tun... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnn-dm-test
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5-small-finetuned-cnn-dm-test", "results": []}]} | mohammedbriman/t5-small-finetuned-cnn-dm-test | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T08:51:25+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnn-dm-test
==============================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.4521
* Validation Loss: 2.1296
* Epoch: 0
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 408096, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycl... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
text2text-generation | transformers | # This model predicts the sentiment('Negative'/'Positive') for the input sentence. It is fine-tuned mt5-small
The present model supports 6 languages -
1) English
2) Hindi
3) German
4) Korean
5) Japanese
6) Portuguese
Here is how to use this model
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqL... | {} | Chirayu/mt5-multilingual-sentiment | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T08:51:54+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # This model predicts the sentiment('Negative'/'Positive') for the input sentence. It is fine-tuned mt5-small
The present model supports 6 languages -
1) English
2) Hindi
3) German
4) Korean
5) Japanese
6) Portuguese
Here is how to use this model
| [
"# This model predicts the sentiment('Negative'/'Positive') for the input sentence. It is fine-tuned mt5-small\n\nThe present model supports 6 languages -\n1) English\n2) Hindi\n3) German\n4) Korean\n5) Japanese\n6) Portuguese\n\nHere is how to use this model"
] | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# This model predicts the sentiment('Negative'/'Positive') for the input sentence. It is fine-tuned mt5-small\n\nThe present model supports 6 languages -\n1... |
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. -->
# Fine_Tuning_XLSR_300M_testing_model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Fine_Tuning_XLSR_300M_testing_model", "results": []}]} | rajat99/Fine_Tuning_XLSR_300M_testing_model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T09:26:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Fine\_Tuning\_XLSR\_300M\_testing\_model
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2861
* Wer: 1.0
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-960h-Lv60 + Self-Training
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objecti... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-960h-lv60", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dat... | Vikasbhandari/wav2vec2-train | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:... | null | 2022-07-12T10:11:37+00:00 | [
"2010.11430",
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #wav2vec2 #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.11430 #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-Large-960h-Lv60 + Self-Training
========================================
Facebook's Wav2Vec2
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with Self-Training objective. When using the model make sure that your speech i... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #wav2vec2 #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.11430 #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
null | null | Monkey eye red body blue
| {} | Apton010/APTON | null | [
"region:us"
] | null | 2022-07-12T11:07:28+00:00 | [] | [] | TAGS
#region-us
| Monkey eye red body blue
| [] | [
"TAGS\n#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. -->
# distilbert-base-uncased-finetuned-sst2
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": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}... | suc155/distilbert-base-uncased-finetuned-sst2 | 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 | 2022-07-12T11:22:16+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-sst2
======================================
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: 0.3056
* Accuracy: 0.9151
Model description
-----------------
More information needed
... | [
"### 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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}... | ymcnabb/finetuning-sentiment-model | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T11:24:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3291
- Accuracy: 0.8733
- F1: 0.8758
## Model description
More information needed
## Intended uses & limitations
More information nee... | [
"# finetuning-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3291\n- Accuracy: 0.8733\n- F1: 0.8758",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\n... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb ... |
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. -->
# distilroberta-base-Mark_example
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-Mark_example", "results": []}]} | xuantsh/distilroberta-base-Mark_example | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T11:57:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-Mark\_example
================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6043
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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 #tensorboard #roberta #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: 2e-05\n* train\\_batch\\_size: ... |
automatic-speech-recognition | transformers |
# Arabic Hubert-Large - with CTC fine-tuned on MGB-3 and Egyptian Arabic Conversational Speech Corpus (No LM)
This model is a fine-tuned version of [Arabic Hubert-Large](https://huggingface.co/asafaya/hubert-large-arabic). We finetuned this model on the MGB-3 and Egyptian Arabic Conversational Speech Corpus datasets,... | {"language": "ar", "license": "cc-by-nc-4.0", "tags": ["CTC", "Attention", "pytorch", "Transformer"], "datasets": ["MGB-3", "egyptian-arabic-conversational-speech-corpus"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | omarxadel/hubert-large-arabic-egyptian | null | [
"transformers",
"pytorch",
"safetensors",
"hubert",
"automatic-speech-recognition",
"CTC",
"Attention",
"Transformer",
"ar",
"dataset:MGB-3",
"dataset:egyptian-arabic-conversational-speech-corpus",
"arxiv:2106.07447",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"has_... | null | 2022-07-12T12:23:37+00:00 | [
"2106.07447"
] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #hubert #automatic-speech-recognition #CTC #Attention #Transformer #ar #dataset-MGB-3 #dataset-egyptian-arabic-conversational-speech-corpus #arxiv-2106.07447 #license-cc-by-nc-4.0 #model-index #endpoints_compatible #has_space #region-us
| Arabic Hubert-Large - with CTC fine-tuned on MGB-3 and Egyptian Arabic Conversational Speech Corpus (No LM)
===========================================================================================================
This model is a fine-tuned version of Arabic Hubert-Large. We finetuned this model on the MGB-3 and Eg... | [] | [
"TAGS\n#transformers #pytorch #safetensors #hubert #automatic-speech-recognition #CTC #Attention #Transformer #ar #dataset-MGB-3 #dataset-egyptian-arabic-conversational-speech-corpus #arxiv-2106.07447 #license-cc-by-nc-4.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | andreaschandra/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T12:28:29+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-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_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | workRL/TEST2ppo-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-12T12:29:34+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
question-answering | 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. -->
# juancopi81/course-bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased)... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-base-cased", "model-index": [{"name": "juancopi81/course-bert-finetuned-squad", "results": []}]} | juancopi81/course-bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T12:48:32+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us
| juancopi81/course-bert-finetuned-squad
======================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0547
* Epoch: 0
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 5546, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learnin... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/770622589664460802/bgUHf... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/piotrikonowicz1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T13:00:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Piotr Ikonowicz
@piotrikonowicz1
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | juliensimon/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T13:01:18+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #base_model-distilbert-base-uncased #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: 0.7737
* Matthews Correlation: 0.5335
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 #distilbert #text-classification #generated_from_trainer #dataset-glue #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
question-answering | transformers |
# xtremedistil-l6-h256-uncased fine-tuned on SQuAD
This model was developed as part of a project for the Deep Learning for NLP (DL4NLP) lecture at Technische Universität Darmstadt (2022). It uses [xtremedistil-l6-h256-uncased]( https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) as a base model and was fin... | {"language": "en", "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Who is the best girl in NieR:Automata?", "context": "2B is a fictional character from the game NieR: Automata. She is considered by many to be best girl of the series, perhaps due to her appealing design (wearing quite a provoking outf... | DL4NLP-Group11/xtremedistil-l6-h256-uncased-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T13:05:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #dataset-squad #endpoints_compatible #region-us
| xtremedistil-l6-h256-uncased fine-tuned on SQuAD
================================================
This model was developed as part of a project for the Deep Learning for NLP (DL4NLP) lecture at Technische Universität Darmstadt (2022). It uses xtremedistil-l6-h256-uncased as a base model and was fine-tuned on the SQuA... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-squad #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLSR-53 - with CTC fine-tuned on MGB-3 and Egyptian Arabic Conversational Speech Corpus (No LM)
This model is a fine-tuned version of [Wav2Vec2-XLSR-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53). We finetuned this model on the MGB-3 and Egyptian Arabic Conversational Speech Corpus datasets, a... | {"language": "ar", "license": "cc-by-nc-4.0", "tags": ["CTC", "Attention", "pytorch", "Transformer"], "datasets": ["MGB-3", "egyptian-arabic-conversational-speech-corpus"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | omarxadel/wav2vec2-large-xlsr-53-arabic-egyptian | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"CTC",
"Attention",
"Transformer",
"ar",
"dataset:MGB-3",
"dataset:egyptian-arabic-conversational-speech-corpus",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T13:17:43+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #CTC #Attention #Transformer #ar #dataset-MGB-3 #dataset-egyptian-arabic-conversational-speech-corpus #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-XLSR-53 - with CTC fine-tuned on MGB-3 and Egyptian Arabic Conversational Speech Corpus (No LM)
========================================================================================================
This model is a fine-tuned version of Wav2Vec2-XLSR-53. We finetuned this model on the MGB-3 and Egyptian Ar... | [] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #CTC #Attention #Transformer #ar #dataset-MGB-3 #dataset-egyptian-arabic-conversational-speech-corpus #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us \n"
] |
text-generation | transformers | This generation model is based on [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2). It's trained on large corpus of dialog data and can be used for buildning generative conversational agents
The model was trained with context size 3
On a private validation set we ... | {"language": ["ru"], "license": "mit", "tags": ["conversational"], "pipeline_tag": "text-generation", "widget": [{"text": "@@\u041f\u0415\u0420\u0412\u042b\u0419@@ \u043f\u0440\u0438\u0432\u0435\u0442 @@\u0412\u0422\u041e\u0420\u041e\u0419@@ \u043f\u0440\u0438\u0432\u0435\u0442 @@\u041f\u0415\u0420\u0412\u042b\u0419@@ ... | tinkoff-ai/ruDialoGPT-small | null | [
"transformers",
"pytorch",
"gpt2",
"conversational",
"text-generation",
"ru",
"arxiv:2001.09977",
"license:mit",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T13:24:39+00:00 | [
"2001.09977"
] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #conversational #text-generation #ru #arxiv-2001.09977 #license-mit #endpoints_compatible #has_space #text-generation-inference #region-us
| This generation model is based on sberbank-ai/rugpt3small\_based\_on\_gpt2. It's trained on large corpus of dialog data and can be used for buildning generative conversational agents
The model was trained with context size 3
On a private validation set we calculated metrics introduced in this paper:
* Sensiblenes... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #conversational #text-generation #ru #arxiv-2001.09977 #license-mit #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"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": "7.50 +/... | Kuro96/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-12T13:25:52+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"
] |
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="Kuro96/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"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": ... | Kuro96/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-12T13:35:21+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-generation | transformers | This is a `gpt2` model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of **14.84** using a "sliding window" context, using the `run_clm.py` script at [https://github.com/neulab/knn-transformers](https://github.com/neulab/knn-transformers).
| Base LM: | `distilgpt2` | `gpt2` |
| :--- ... | {} | neulab/gpt2-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2201.12431",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T13:37:59+00:00 | [
"2201.12431"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2201.12431 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a 'gpt2' model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of 14.84 using a "sliding window" context, using the 'run\_clm.py' script at URL
This model was released as part of the paper "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML'2022).
For more informa... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2201.12431 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1535379125296418821/ntSM... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/scottduncanwx/1657637010818/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/scottduncanwx | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T13:37:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Scott Duncan
@scottduncanwx
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | andreaschandra/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T13:49:14+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1619
* F1: 0.8599
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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 #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
null | null |
# Speaker diarization
Relies on pyannote.audio 2.0 currently in development: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation).
```python
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("AMITKESARI2000/pyannote_SD1")
output = pipeline("audio... | {"license": "mit", "tags": ["pyannote", "speaker-diarization"], "datasets": ["ami", "voxconverse"]} | AMITKESARI2000/pyannote_SD1 | null | [
"pyannote",
"speaker-diarization",
"dataset:ami",
"dataset:voxconverse",
"license:mit",
"region:us"
] | null | 2022-07-12T13:50:19+00:00 | [] | [] | TAGS
#pyannote #speaker-diarization #dataset-ami #dataset-voxconverse #license-mit #region-us
| Speaker diarization
===================
Relies on URL 2.0 currently in development: see installation instructions.
Benchmark
---------
| [] | [
"TAGS\n#pyannote #speaker-diarization #dataset-ami #dataset-voxconverse #license-mit #region-us \n"
] |
text-generation | transformers |
This generation model is based on [sberbank-ai/rugpt3medium_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3medium_based_on_gpt2). It's trained on large corpus of dialog data and can be used for buildning generative conversational agents
The model was trained with context size 3
On a private validation set ... | {"language": ["ru"], "license": "mit", "tags": ["conversational"], "widget": [{"text": "@@\u041f\u0415\u0420\u0412\u042b\u0419@@ \u043f\u0440\u0438\u0432\u0435\u0442 @@\u0412\u0422\u041e\u0420\u041e\u0419@@ \u043f\u0440\u0438\u0432\u0435\u0442 @@\u041f\u0415\u0420\u0412\u042b\u0419@@ \u043a\u0430\u043a \u0434\u0435\u04... | tinkoff-ai/ruDialoGPT-medium | null | [
"transformers",
"pytorch",
"gpt2",
"conversational",
"ru",
"arxiv:2001.09977",
"license:mit",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T13:52:19+00:00 | [
"2001.09977"
] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #conversational #ru #arxiv-2001.09977 #license-mit #endpoints_compatible #has_space #text-generation-inference #region-us
| This generation model is based on sberbank-ai/rugpt3medium\_based\_on\_gpt2. It's trained on large corpus of dialog data and can be used for buildning generative conversational agents
The model was trained with context size 3
On a private validation set we calculated metrics introduced in this paper:
* Sensiblene... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #conversational #ru #arxiv-2001.09977 #license-mit #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | andreaschandra/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T14:15:58+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1059
* F1: 0.9275
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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | andreaschandra/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T14:30:49+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2380
* F1: 0.8289
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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | andreaschandra/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T14:35:21+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3932
* F1: 0.6774
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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-generation | transformers | This is a `gpt2-medium` model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of **11.55** using a "sliding window" context, using the `run_clm.py` script at [https://github.com/neulab/knn-transformers](https://github.com/neulab/knn-transformers).
| Base LM: | `distilgpt2` | `gpt2` |
| :--- ... | {} | neulab/gpt2-med-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2201.12431",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T14:40:48+00:00 | [
"2201.12431"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2201.12431 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a 'gpt2-medium' model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of 11.55 using a "sliding window" context, using the 'run\_clm.py' script at URL
This model was released as part of the paper "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML'2022).
For more ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2201.12431 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | This is a `gpt2-large` model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of **10.56** using a "sliding window" context, using the `run_clm.py` script at [https://github.com/neulab/knn-transformers](https://github.com/neulab/knn-transformers).
| Base LM: | `distilgpt2` | `gpt2` |
| :--- ... | {} | neulab/gpt2-large-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2201.12431",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T14:48:02+00:00 | [
"2201.12431"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2201.12431 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a 'gpt2-large' model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of 10.56 using a "sliding window" context, using the 'run\_clm.py' script at URL
This model was released as part of the paper "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML'2022).
For more i... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2201.12431 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# orcs-and-friends
Five-way classifier for orcs and their friends
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at t... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | andy-0v0/orcs-and-friends | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T14:50:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# orcs-and-friends
Five-way classifier for orcs and their friends
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### goblin
!goblin
#### gremlin
!gremlin
#### ogre
!ogre... | [
"# orcs-and-friends\n\nFive-way classifier for orcs and their friends\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### goblin\n\n!goblin",
"#### gremlin\n\n... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# orcs-and-friends\n\nFive-way classifier for orcs and their friends\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by run... |
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. -->
# s288cExpressionPrediction_k6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "s288cExpressionPrediction_k6", "results": []}]} | zluvolyote/s288cExpressionPrediction_k6 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T15:02:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| s288cExpressionPrediction\_k6
=============================
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: 0.4418
* Accuracy: 0.8067
* F1: 0.7882
Model description
-----------------
More information needed
Int... | [
"### 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: 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 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
image-classification | null |
**task**: `image-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `nateraw/vit-base-beans`
* **dataset**:
* **path**: `beans... | {"tags": ["vit"], "datasets": ["beans"], "metrics": ["accuracy"], "pipeline_tag": "image-classification"} | fxmarty/20220712-h16m02s58_example_beans | null | [
"tensorboard",
"vit",
"image-classification",
"dataset:beans",
"region:us"
] | null | 2022-07-12T15:02:58+00:00 | [] | [] | TAGS
#tensorboard #vit #image-classification #dataset-beans #region-us
| task: 'image-classification'
Backend: 'sagemaker-training'
Backend args: '{'instance\_type': 'ml.g4dn.2xlarge', 'supported\_instructions': None}'
Number of evaluation samples: 'All dataset'
Fixed parameters:
* model\_name\_or\_path: 'nateraw/vit-base-beans'
* dataset:
+ path: 'beans'
+ eval\_split: 'vali... | [] | [
"TAGS\n#tensorboard #vit #image-classification #dataset-beans #region-us \n"
] |
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... | reachrkr/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-12T15:20:08+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... |
text-generation | transformers | This is a `distilgpt2` model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of **18.25** using a "sliding window" context, using the `run_clm.py` script at [https://github.com/neulab/knn-transformers](https://github.com/neulab/knn-transformers).
| Base LM: | `distilgpt2` | `gpt2` |
| :--- ... | {} | neulab/distilgpt2-finetuned-wikitext103 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2201.12431",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T15:42:14+00:00 | [
"2201.12431"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2201.12431 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This is a 'distilgpt2' model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of 18.25 using a "sliding window" context, using the 'run\_clm.py' script at URL
This model was released as part of the paper "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML'2022).
For more i... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2201.12431 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410026132121047041/LiYe... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/masonhaggerty/1657646221015/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/masonhaggerty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T15:48:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Mason Haggerty
@masonhaggerty
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Li-Tang/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T15:57:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
question-answering | transformers |
# bert-base-finnish-cased-v1 for QA
This is the [bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model, fine-tuned using an automatically translated [Finnish version of the SQuAD2.0 dataset](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) in combination with the Finnish ... | {"language": "fi", "license": "gpl-3.0", "datasets": ["SQuAD_v2_fi + Finnish partition of TyDi-QA"]} | ilmariky/bert-base-finnish-cased-squad1-fi | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"fi",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T16:01:30+00:00 | [] | [
"fi"
] | TAGS
#transformers #pytorch #bert #question-answering #fi #license-gpl-3.0 #endpoints_compatible #region-us
|
# bert-base-finnish-cased-v1 for QA
This is the bert-base-finnish-cased-v1 model, fine-tuned using an automatically translated Finnish version of the SQuAD2.0 dataset in combination with the Finnish partition of the TyDi-QA dataset. It's been trained on question-answer pairs, excluding unanswerable questions, for th... | [
"# bert-base-finnish-cased-v1 for QA \n\nThis is the bert-base-finnish-cased-v1 model, fine-tuned using an automatically translated Finnish version of the SQuAD2.0 dataset in combination with the Finnish partition of the TyDi-QA dataset. It's been trained on question-answer pairs, excluding unanswerable questions, ... | [
"TAGS\n#transformers #pytorch #bert #question-answering #fi #license-gpl-3.0 #endpoints_compatible #region-us \n",
"# bert-base-finnish-cased-v1 for QA \n\nThis is the bert-base-finnish-cased-v1 model, fine-tuned using an automatically translated Finnish version of the SQuAD2.0 dataset in combination with the Fin... |
sentence-similarity | sentence-transformers |
# TimKond/S-BioLinkBert-MedQuAD
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 b... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | TimKond/S-BioLinkBert-MedQuAD | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T16:28:43+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# TimKond/S-BioLinkBert-MedQuAD
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:
... | [
"# TimKond/S-BioLinkBert-MedQuAD\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 i... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# TimKond/S-BioLinkBert-MedQuAD\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... |
question-answering | transformers |
# bert-base-finnish-cased-v1 for QA
This is the [bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model, fine-tuned using an automatically translated [Finnish version of the SQuAD2.0 dataset](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) in combination with the Finnish ... | {"language": "fi", "license": "gpl-3.0", "datasets": ["SQuAD_v2_fi + Finnish partition of TyDi-QA"]} | ilmariky/bert-base-finnish-cased-squad2-fi | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"fi",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T17:27:12+00:00 | [] | [
"fi"
] | TAGS
#transformers #pytorch #bert #question-answering #fi #license-gpl-3.0 #endpoints_compatible #region-us
|
# bert-base-finnish-cased-v1 for QA
This is the bert-base-finnish-cased-v1 model, fine-tuned using an automatically translated Finnish version of the SQuAD2.0 dataset in combination with the Finnish partition of the TyDi-QA dataset. It's been trained on question-answer pairs, including unanswerable questions, for th... | [
"# bert-base-finnish-cased-v1 for QA \n\nThis is the bert-base-finnish-cased-v1 model, fine-tuned using an automatically translated Finnish version of the SQuAD2.0 dataset in combination with the Finnish partition of the TyDi-QA dataset. It's been trained on question-answer pairs, including unanswerable questions, ... | [
"TAGS\n#transformers #pytorch #bert #question-answering #fi #license-gpl-3.0 #endpoints_compatible #region-us \n",
"# bert-base-finnish-cased-v1 for QA \n\nThis is the bert-base-finnish-cased-v1 model, fine-tuned using an automatically translated Finnish version of the SQuAD2.0 dataset in combination with the Fin... |
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", "ar... | wonkwonlee/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T17:42:43+00:00 | [] | [] | TAGS
#transformers #pytorch #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: 0.5263
* Matthews Correlation: 0.5475
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 #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\\_rate: 2e-0... |
sentence-similarity | sentence-transformers |
# anatel/bert-augmented-pt-anatel
O modelo foi treinado seguindo a estratégia descrita no [Augmented SBERT](https://www.sbert.net/examples/training/data_augmentation/README.html) para retreinar o modelo BERT para o contexto da Anatel. O objetivo final é retreinar o modelo BERT mesmo com poucos textos rotulados para a... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | anatel/bert-augmented-pt-anatel | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-12T18:23:14+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# anatel/bert-augmented-pt-anatel
O modelo foi treinado seguindo a estratégia descrita no Augmented SBERT para retreinar o modelo BERT para o contexto da Anatel. O objetivo final é retreinar o modelo BERT mesmo com poucos textos rotulados para a tarefa desejada. Para a execução do script é necessário ter o modelo cro... | [
"# anatel/bert-augmented-pt-anatel\n\nO modelo foi treinado seguindo a estratégia descrita no Augmented SBERT para retreinar o modelo BERT para o contexto da Anatel. O objetivo final é retreinar o modelo BERT mesmo com poucos textos rotulados para a tarefa desejada. Para a execução do script é necessário ter o mode... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n",
"# anatel/bert-augmented-pt-anatel\n\nO modelo foi treinado seguindo a estratégia descrita no Augmented SBERT para retreinar o modelo BERT para o contexto da Anatel.... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | MichalRoztocki/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T18:35:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3085
- Accuracy: 0.8767
- F1: 0.8779
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3085\n- Accuracy: 0.8767\n- F1: 0.8779",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
null | keras |
# Image Classifier
`image-classifier` is an extendable TensorFlow image classifier w/ a Bash cli and Hugging Face integration - to see the list of `image-classifier` commands complete [installation](#Installation) and type in:
```
image_classifier ?
```
## Installation
To install `image-classifier` first [install a... | {"license": "cc"} | kamangir/image-classifier | null | [
"keras",
"license:cc",
"region:us"
] | null | 2022-07-12T18:36:45+00:00 | [] | [] | TAGS
#keras #license-cc #region-us
| Image Classifier
================
'image-classifier' is an extendable TensorFlow image classifier w/ a Bash cli and Hugging Face integration - to see the list of 'image-classifier' commands complete installation and type in:
Installation
------------
To install 'image-classifier' first install and configure aweso... | [] | [
"TAGS\n#keras #license-cc #region-us \n"
] |
multiple-choice | 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. -->
# medqa_fine_tuned_generic_bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "medqa_fine_tuned_generic_bert", "results": []}]} | Shaier/medqa_fine_tuned_generic_bert | null | [
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T18:49:52+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| medqa\_fine\_tuned\_generic\_bert
=================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4239
* Accuracy: 0.2869
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #bert #multiple-choice #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: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* s... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506510453286924293/NXf3... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ydouright/1657656913047/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/ydouright | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T19:13:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
URL
@ydouright
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
Th... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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... | jakka/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-12T19:22:41+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... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1384643526772678657/O7Sz... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dylanfromsf/1657657784578/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dylanfromsf | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T19:29:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
dylan
@dylanfromsf
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"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": ... | AntiSquid/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-12T19:49:52+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"
] |
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="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"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": "7.48 +/... | AntiSquid/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-12T19:51:18+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"
] |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **quadrotor_multi** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"]} | andrewzhang505/quad-swarm-rl-1 | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-07-12T20:09:52+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #region-us
|
A(n) APPO model trained on the quadrotor_multi environment.
This model was trained using Sample Factory 2.0: URL
| [] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# FelipeAD/mt5-small-SENTENCE_COMPRESSION
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-sma... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "FelipeAD/mt5-small-SENTENCE_COMPRESSION", "results": []}]} | FelipeAD/mt5-small-SENTENCE_COMPRESSION | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T20:29:25+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| FelipeAD/mt5-small-SENTENCE\_COMPRESSION
========================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1433
* Validation Loss: 0.9768
* Epoch: 3
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 179848, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycl... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
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-model-666", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"t... | AntiSquid/Reinforce-model-666 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-12T20:51:51+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 |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1268284452586700800/BtFz... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/reillocity/1658731242865/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/reillocity | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-12T22:14:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Matt Collier
@reillocity
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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-pix-5", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}... | AntiSquid/Reinforce-pix-5 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-12T22:21:12+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 ... |
token-classification | transformers |
To use our fine-tuned BioBERT model to remove references to priors from radiology reports, run the following:
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
modelname = "rajpurkarlab/gilbert"
tokenizer = AutoTokenizer.from_pretrained(modelname)
model = AutoModelForTokenClassificati... | {"language": ["py"], "metrics": ["f1"]} | rajpurkarlab/gilbert | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"py",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T22:24:29+00:00 | [] | [
"py"
] | TAGS
#transformers #pytorch #bert #token-classification #py #autotrain_compatible #endpoints_compatible #region-us
|
To use our fine-tuned BioBERT model to remove references to priors from radiology reports, run the following:
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #py #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. -->
# scibert-lm-const-finetuned-20
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/alle... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model-index": [{"name": "scibert-lm-const-finetuned-20", "results": []}]} | ariesutiono/scibert-lm-const-finetuned-20 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-12T22:32:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us
| scibert-lm-const-finetuned-20
=============================
This model is a fine-tuned version of allenai/scibert\_scivocab\_cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0099
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-conll2003 #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: 4\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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | xliu128/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T00:44:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7720
* Accuracy: 0.9184
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
null | null |
### How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12
cd https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12
git lfs pull
```
| {"license": "apache-2.0"} | yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12 | null | [
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-13T01:19:09+00:00 | [] | [] | TAGS
#license-apache-2.0 #has_space #region-us
|
### How to clone this repo
| [
"### How to clone this repo"
] | [
"TAGS\n#license-apache-2.0 #has_space #region-us \n",
"### How to clone this repo"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1542204712455241729/6E7r... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/majigglydoobers/1657681081092/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/majigglydoobers | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T01:56:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
doobers ️
@majigglydoobers
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2_chatbot
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "gpt2_chatbot", "results": []}]} | kuttersn/gpt2_chatbot | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T02:00:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# gpt2_chatbot
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5732
- Accuracy: 0.3909
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More i... | [
"# gpt2_chatbot\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5732\n- Accuracy: 0.3909",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# gpt2_chatbot\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following r... |
image-classification | transformers |
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transfo... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k", "imagenet-21k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapo... | Loc/lucky-model | null | [
"transformers",
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T02:43:48+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transfor... | [
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Tr... | [
"TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-t... |
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. -->
# tst-summarization
This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bar... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "tst-summarization", "results": []}]} | dafraile/Clini-dialog-sum-BART | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T02:49:10+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# tst-summarization
This model is a fine-tuned version of philschmid/bart-large-cnn-samsum on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 56.239
- Rouge2: 28.9873
- Rougel: 38.5242
- Rougelsum: 53.7902
- Gen Len: 105.2973
## Model description
More informat... | [
"# tst-summarization\n\nThis model is a fine-tuned version of philschmid/bart-large-cnn-samsum on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.9975\n- Rouge1: 56.239\n- Rouge2: 28.9873\n- Rougel: 38.5242\n- Rougelsum: 53.7902\n- Gen Len: 105.2973",
"## Model description... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# tst-summarization\n\nThis model is a fine-tuned version of philschmid/bart-large-cnn-samsum on an unknown dataset.\nIt achieves the following results on the... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1542316332972228608/Hs2W... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/burdeevt/1657685656540/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/burdeevt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T02:51:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Burdee
@burdeevt
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# tst-summarization
This model is a fine-tuned version of [henryu-lin/t5-large-samsum-deepspeed](https://huggingface.co/henryu-lin... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "tst-summarization", "results": []}]} | dafraile/Clini-dialog-sum-T5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T03:04:00+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# tst-summarization
This model is a fine-tuned version of henryu-lin/t5-large-samsum-deepspeed on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3922
- Rouge1: 54.905
- Rouge2: 26.6374
- Rougel: 40.4619
- Rougelsum: 52.3653
- Gen Len: 104.7241
## Model description
More info... | [
"# tst-summarization\n\nThis model is a fine-tuned version of henryu-lin/t5-large-samsum-deepspeed on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.3922\n- Rouge1: 54.905\n- Rouge2: 26.6374\n- Rougel: 40.4619\n- Rougelsum: 52.3653\n- Gen Len: 104.7241",
"## Model descrip... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# tst-summarization\n\nThis model is a fine-tuned version of henryu-lin/t5-large-samsum-deepspeed on an unknown dataset.\nIt achieves... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becasv3-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv3"], "model-index": [{"name": "distilbert-base-uncased-becasv3-1", "results": []}]} | Evelyn18/distilbert-base-uncased-becasv3-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T03:27:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv3 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becasv3-1
=================================
This model is a fine-tuned version of distilbert-base-uncased on the becasv3 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1086
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv3 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | AdiKompella/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-13T04:18:07+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
sentence-similarity | sentence-transformers |
# NimaBoscarino/STPushToHub-test2
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... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | NimaBoscarino/STPushToHub-test2 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T04:49:12+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# NimaBoscarino/STPushToHub-test2
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... | [
"# NimaBoscarino/STPushToHub-test2\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... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# NimaBoscarino/STPushToHub-test2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for ta... |
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. -->
# wav2vec-base-Millad_TIMIT
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-base-Millad_TIMIT", "results": []}]} | Siyong/MT | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T04:57:40+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec-base-Millad\_TIMIT
==========================
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: 1.3772
* Wer: 0.6859
* Cer: 0.3217
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #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\n* eval\\_ba... |
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... | abx/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 | 2022-07-13T05:04:39+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: 0.0623
* Precision: 0.9342
* Recall: 0.9505
* F1: 0.9423
* Accuracy: 0.9861
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... |
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-finetuned-cc-news-es-titles
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cc-news-es-titles"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cc-news-es-titles", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cc-news-es... | casasdorjunior/t5-small-finetuned-cc-news-es-titles | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cc-news-es-titles",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-13T06:38:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cc-news-es-titles #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cc-news-es-titles
====================================
This model is a fine-tuned version of t5-small on the cc-news-es-titles dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6383
* Rouge1: 16.701
* Rouge2: 4.1265
* Rougel: 14.8175
* Rougelsum: 14.8193
* Gen Len: 18.91... | [
"### 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cc-news-es-titles #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used... |
text-classification | transformers |
# General Information
This model is trained on journal publications of belonging to the domain: **Artificial Intelligence**.
This is an `allenai/scibert_scivocab_cased` model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the prov... | {"language": "en", "tags": ["bert", "regression", "pytorch"], "pipeline": ["text-classification"], "widget": [{"text": "We propose a new approach, based on Transformer-based encoding, to highlight extraction. To the best of our knowledge, this is the first attempt to use transformer architectures to address automatic h... | morenolq/thext-ai-scibert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"regression",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T06:42:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #regression #en #autotrain_compatible #endpoints_compatible #region-us
|
# General Information
This model is trained on journal publications of belonging to the domain: Artificial Intelligence.
This is an 'allenai/scibert_scivocab_cased' model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the provided... | [
"# General Information\n\nThis model is trained on journal publications of belonging to the domain: Artificial Intelligence.\n\nThis is an 'allenai/scibert_scivocab_cased' model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the ... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #regression #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# General Information\n\nThis model is trained on journal publications of belonging to the domain: Artificial Intelligence.\n\nThis is an 'allenai/scibert_scivocab_cas... |
text-classification | transformers |
# General Information
This model is trained on journal publications of belonging to the domain: **Biology and Medicine**.
This is an `allenai/scibert_scivocab_cased` model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the provide... | {"language": "en", "tags": ["bert", "regression", "pytorch"], "pipeline": ["text-classification"], "widget": [{"text": "We propose a new approach, based on Transformer-based encoding, to highlight extraction. To the best of our knowledge, this is the first attempt to use transformer architectures to address automatic h... | morenolq/thext-bio-scibert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"regression",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T06:57:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #regression #en #autotrain_compatible #endpoints_compatible #region-us
|
# General Information
This model is trained on journal publications of belonging to the domain: Biology and Medicine.
This is an 'allenai/scibert_scivocab_cased' model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the provided co... | [
"# General Information\n\nThis model is trained on journal publications of belonging to the domain: Biology and Medicine.\n\nThis is an 'allenai/scibert_scivocab_cased' model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the pro... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #regression #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# General Information\n\nThis model is trained on journal publications of belonging to the domain: Biology and Medicine.\n\nThis is an 'allenai/scibert_scivocab_cased'... |
text-classification | transformers |
# General Information
This model is trained on journal publications of belonging to the domain: **Computer Science**.
This is an `allenai/scibert_scivocab_cased` model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the provided co... | {"language": "en", "tags": ["bert", "regression", "pytorch"], "pipeline": ["text-classification"], "widget": [{"text": "We propose a new approach, based on Transformer-based encoding, to highlight extraction. To the best of our knowledge, this is the first attempt to use transformer architectures to address automatic h... | morenolq/thext-cs-scibert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"regression",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-13T06:58:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #regression #en #autotrain_compatible #endpoints_compatible #region-us
|
# General Information
This model is trained on journal publications of belonging to the domain: Computer Science.
This is an 'allenai/scibert_scivocab_cased' model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the provided contex... | [
"# General Information\n\nThis model is trained on journal publications of belonging to the domain: Computer Science.\n\nThis is an 'allenai/scibert_scivocab_cased' model trained in the scientific domain. The model is trained with regression objective to estimate the relevance of a sentence according to the provide... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #regression #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# General Information\n\nThis model is trained on journal publications of belonging to the domain: Computer Science.\n\nThis is an 'allenai/scibert_scivocab_cased' mod... |
null | null | me on a bike
going into the sunset
at night
with my dog running along side me | {} | loz/Test | null | [
"region:us"
] | null | 2022-07-13T07:08:54+00:00 | [] | [] | TAGS
#region-us
| me on a bike
going into the sunset
at night
with my dog running along side me | [] | [
"TAGS\n#region-us \n"
] |
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