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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/1520796357149315073/VpjT... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/donalds28__-dril-kommmipakk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T14:45:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
owen & Ⓐju goblin & wint
@donalds28\_\_-dril-kommmipakk
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 re... | [] | [
"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": []}]} | Yuri/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 | 2022-08-07T15:01:49+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: 0.1608
* F1: 0.8593
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*... |
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/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-jrc1921-spicymoregano/1659888347907/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-jrc1921-spicymoregano | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T15:02:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & j & morg
@apesahoy-jrc1921-spicymoregano
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&... | [] | [
"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. -->
# bert-base-uncased_title_fine_tuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "recall", "precision", "f1"], "model-index": [{"name": "bert-base-uncased_title_fine_tuned", "results": []}]} | Izarel/bert-base-uncased_title_fine_tuned | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T15:18:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased\_title\_fine\_tuned
=====================================
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: 0.3368
* Accuracy: {'accuracy': 0.8810840405146455}
* Recall: {'recall': 0.8611674554879423}
* P... | [
"### 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\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/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-dril_gpt2-nigella_lawson/1659889467093/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-dril_gpt2-nigella_lawson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T15:22:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & Nigella Lawson & wint but Al
@apesahoy-dril\_gpt2-nigella\_lawson
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 wa... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 10k_MT5_small_sum-de_GNAD
This model is a fine-tuned version of [Einmalumdiewelt/10k_MT5_small_sum-de_GNAD](https://huggingface.... | {"language": ["de"], "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "model-index": [{"name": "10k_MT5_small_sum-de_GNAD", "results": []}]} | Einmalumdiewelt/10k_MT5_small_sum-de_GNAD | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"de",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T15:35:39+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #summarization #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 10k_MT5_small_sum-de_GNAD
This model is a fine-tuned version of Einmalumdiewelt/10k_MT5_small_sum-de_GNAD on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4403
- Rouge1: 24.9179
- Rouge2: 7.8694
- Rougel: 18.048
- Rougelsum: 21.9371
- Gen Len: 48.2693
## Model description... | [
"# 10k_MT5_small_sum-de_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/10k_MT5_small_sum-de_GNAD on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4403\n- Rouge1: 24.9179\n- Rouge2: 7.8694\n- Rougel: 18.048\n- Rougelsum: 21.9371\n- Gen Len: 48.2693",
"## Mo... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #summarization #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 10k_MT5_small_sum-de_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/10k_MT5_small_sum-de_GNAD on an unknown d... |
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. -->
# VanessaSchenkel/padrao-mbart-finetuned-news_commentary
This model is a fine-tuned version of [Narrativa/mbart-large-50-finetuned-opus-... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "VanessaSchenkel/padrao-mbart-finetuned-news_commentary", "results": []}]} | VanessaSchenkel/padrao-mbart-finetuned-news_commentary | null | [
"transformers",
"tf",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T15:44:17+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #mbart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| VanessaSchenkel/padrao-mbart-finetuned-news\_commentary
=======================================================
This model is a fine-tuned version of Narrativa/mbart-large-50-finetuned-opus-en-pt-translation on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.3464
* Valid... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #mbart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate':... |
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": []}]} | cataluna84/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 | 2022-08-07T15:47:10+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: 0.1608
* F1: 0.8593
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*... |
text-generation | transformers |
# <span style="color:red"><b>WARNING:</b> The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).</span>
# <p>BLOOM LM<br/> _BigScience Large Open-science Open-access Multilingual Language Model_ <br/>Model Card</p... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-7b1-intermediate | null | [
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"pytorch",
"bloom",
"text-generation",
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"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"... | null | 2022-08-07T16:04:54+00:00 | [
"1909.08053",
"2110.02861",
"2108.12409"
] | [
"ak",
"ar",
"as",
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... | TAGS
#transformers #pytorch #bloom #text-generation #ak #ar #as #bm #bn #ca #code #en #es #eu #fon #fr #gu #hi #id #ig #ki #kn #lg #ln #ml #mr #ne #nso #ny #or #pa #pt #rn #rw #sn #st #sw #ta #te #tn #ts #tum #tw #ur #vi #wo #xh #yo #zh #zhs #zht #zu #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #license-bigsci... | **WARNING:** The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).
===================================================================================================================================================... | [
"### Model Architecture and Objective\n\n\n* Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 176 bil... | [
"TAGS\n#transformers #pytorch #bloom #text-generation #ak #ar #as #bm #bn #ca #code #en #es #eu #fon #fr #gu #hi #id #ig #ki #kn #lg #ln #ml #mr #ne #nso #ny #or #pa #pt #rn #rw #sn #st #sw #ta #te #tn #ts #tum #tw #ur #vi #wo #xh #yo #zh #zhs #zht #zu #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-2108.12409 #license-... |
text-generation | transformers | Currently trained on the style of three authors: Robert Burns, Mary Seacole and Violet Jacobs.
To generate text in the style of these authors append \<RB\>, \<MS\> or \<VJ\> respectively in front of the context. | {} | rahulbaburajan/gpt-neo-125M-creative_writing | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-07T16:15:25+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Currently trained on the style of three authors: Robert Burns, Mary Seacole and Violet Jacobs.
To generate text in the style of these authors append \<RB\>, \<MS\> or \<VJ\> respectively in front of the context. | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | cataluna84/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 | 2022-08-07T16:17:34+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: 0.2763
* F1: 0.8346
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... |
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. -->
# finetuned-marktextepoch-n200
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-marktextepoch-n200", "results": []}]} | leokai/finetuned-marktextepoch-n200 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T16:29:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned-marktextepoch-n200
============================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0880
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: 200",
"### 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: ... |
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... | cataluna84/xlm-roberta-base-finetuned-panx-it | null | [
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"license:mit",
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"endpoints_compatible",
"region:us"
] | null | 2022-08-07T16:37:54+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: 0.2630
* F1: 0.8124
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 |
# <span style="color:red"><b>WARNING:</b> The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).</span>
# <p>BLOOM LM<br/> _BigScience Large Open-science Open-access Multilingual Language Model_ <br/>Model Card</p... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-3b-intermediate | null | [
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... | null | 2022-08-07T16:47:50+00:00 | [
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===================================================================================================================================================... | [
"### Model Architecture and Objective\n\n\n* Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 176 bil... | [
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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... | cataluna84/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 | 2022-08-07T16:56:51+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: 0.4043
* F1: 0.6886
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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | cataluna84/xlm-roberta-base-finetuned-panx-all | null | [
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] | null | 2022-08-07T17:15:45+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1745
* F1: 0.8505
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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-generation | transformers |
# <span style="color:red"><b>WARNING:</b> The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).</span>
# <p>BLOOM LM<br/> _BigScience Large Open-science Open-access Multilingual Language Model_ <br/>Model Card</p... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-1b7-intermediate | null | [
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===================================================================================================================================================... | [
"### Model Architecture and Objective\n\n\n* Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 176 bil... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-qa-es
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert... | {"tags": ["generated_from_trainer"], "datasets": ["squad_es"], "model-index": [{"name": "bert-qa-es", "results": []}]} | srcocotero/bert-qa-es | null | [
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"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_es",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T17:16:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_es #endpoints_compatible #region-us
|
# bert-qa-es
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the squad_es dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# bert-qa-es\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the squad_es dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training... | [
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"## Model description\n\nMore information... |
text-generation | transformers |
# <span style="color:red"><b>WARNING:</b> The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).</span>
# <p>BLOOM LM<br/> _BigScience Large Open-science Open-access Multilingual Language Model_ <br/>Model Card</p... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-1b1-intermediate | null | [
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===================================================================================================================================================... | [
"### Model Architecture and Objective\n\n\n* Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 176 bil... | [
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text-generation | transformers |
# <span style="color:red"><b>WARNING:</b> The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).</span>
# <p>BLOOM LM<br/> _BigScience Large Open-science Open-access Multilingual Language Model_ <br/>Model Card</p... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-560m-intermediate | null | [
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===================================================================================================================================================... | [
"### Model Architecture and Objective\n\n\n* Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 176 bil... | [
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text-generation | transformers |
# Bakugou DialoGPT Model | {"tags": ["conversational"]} | notaproblem00/DialoGPT-small-bakugou | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T17:39:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Bakugou DialoGPT Model | [
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] |
sentence-similarity | sentence-transformers |
# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It has been fine-tuned on https://brain.louis030195.com using code from... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian | null | [
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"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-07T17:41:33+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It has been fine-tuned on URL using code from URL
## Usage (Sentence-Transformers)
Using thi... | [
"# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nIt has been fine-tuned on URL using code from URL",
"## Usage (Sentence-Transformers)\... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n",
"# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space an... |
null | transformers | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests
# load image from the IAM database (actually this model is meant to be used on printed text)
url = 'https://fki.tic.heia-fr.ch/static/img/a01-122... | {} | ycchen/TrOCR-base-ver021-v1 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T17:49:55+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us
| ### How to use
Here is how to use this model in PyTorch:
| [
"### How to use\n\nHere is how to use this model in PyTorch:"
] | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us \n",
"### How to use\n\nHere is how to use this model in PyTorch:"
] |
text2text-generation | transformers | See https://wandb.ai/yepster/long-t5-local-base?workspace=user-yepster for
logs | {} | yhavinga/long-t5-local-base-dutch-english | null | [
"transformers",
"jax",
"longt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T18:22:13+00:00 | [] | [] | TAGS
#transformers #jax #longt5 #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| See URL for
logs | [] | [
"TAGS\n#transformers #jax #longt5 #text2text-generation #autotrain_compatible #endpoints_compatible #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... | theicfire/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-07T18:47:53+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... |
null | null | 337.5K / 341B Tokens is the one used for bloom-7b1 | {} | bigscience/bloom-7b1-optimizer-states | null | [
"region:us"
] | null | 2022-08-07T19:14:27+00:00 | [] | [] | TAGS
#region-us
| 337.5K / 341B Tokens is the one used for bloom-7b1 | [] | [
"TAGS\n#region-us \n"
] |
document-question-answering | transformers |
# LayoutLM for Visual Question Answering
This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on documents. It has been fine-tuned using both the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) datasets.... | {"language": "en", "license": "mit", "tags": ["layoutlm", "document-question-answering", "pdf"], "pipeline_tag": "document-question-answering", "widget": [{"text": "What is the invoice number?", "src": "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"}, {"text"... | impira/layoutlm-document-qa | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"layoutlm",
"document-question-answering",
"pdf",
"en",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-07T20:07:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #layoutlm #document-question-answering #pdf #en #license-mit #endpoints_compatible #has_space #region-us
|
# LayoutLM for Visual Question Answering
This is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on documents. It has been fine-tuned using both the SQuAD2.0 and DocVQA datasets.
## Getting started with the model
To run these examples, you must have PIL, pytesseract, and Py... | [
"# LayoutLM for Visual Question Answering\n\nThis is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on documents. It has been fine-tuned using both the SQuAD2.0 and DocVQA datasets.",
"## Getting started with the model\n\nTo run these examples, you must have PIL, pytesse... | [
"TAGS\n#transformers #pytorch #tf #safetensors #layoutlm #document-question-answering #pdf #en #license-mit #endpoints_compatible #has_space #region-us \n",
"# LayoutLM for Visual Question Answering\n\nThis is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on documents. ... |
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. -->
# multi_news_article_title_25000_1
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "multi_news_article_title_25000_1", "results": []}]} | abdulmatinomotoso/multi_news_article_title_25000_1 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T22:09:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| multi\_news\_article\_title\_25000\_1
=====================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1973
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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #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: 1\n* eval... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | myodoctor/DIALOGPT-medium-HarryPotterBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T22:14:03+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
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. -->
# mDeBERTa-mnli-kaggle-contradictory-my-dear-watson
This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "mDeBERTa-mnli-kaggle-contradictory-my-dear-watson", "results": []}]} | diegopetrola/mDeBERTa-mnli-kaggle-contradictory-my-dear-watson | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T22:20:16+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| mDeBERTa-mnli-kaggle-contradictory-my-dear-watson
=================================================
This model is a fine-tuned version of MoritzLaurer/mDeBERTa-v3-base-mnli-xnli on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3225
* Accuracy: 0.8890
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\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: cosine\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-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: 1e-06\n* train\\_batch\\_size: 16\n* e... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln63Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T23:20:41+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Most likely outputs:
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relb... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"... | research-backup/roberta-large-conceptnet-average-no-mask-prompt-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T23:41:18+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset... | [
"# relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Questi... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning... |
image-to-text | transformers | A model that inputs chemistry journal article table of contents (ToC) images and generates appropriate titles. Trained on all JACS ToCs and titles. | {"license": "mit", "tags": ["image-to-text", "image-captioning"]} | yuewu/toc_titler | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-to-text",
"image-captioning",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-08T00:11:08+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #image-to-text #image-captioning #license-mit #endpoints_compatible #has_space #region-us
| A model that inputs chemistry journal article table of contents (ToC) images and generates appropriate titles. Trained on all JACS ToCs and titles. | [] | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #image-to-text #image-captioning #license-mit #endpoints_compatible #has_space #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-v2-finetuned-20
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model-index": [{"name": "scibert-lm-v2-finetuned-20", "results": []}]} | ariesutiono/scibert-lm-v2-finetuned-20 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T00:28:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us
| scibert-lm-v2-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: 15.7952
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### 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: 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: 8\n*... |
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"]} | jjjjjjjjjj/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-08T01:07:17+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... |
text2text-generation | transformers |
# t5-small-kw2email-v2
This model is a fine-tuned version of [postbot/t5-small-kw2email](https://huggingface.co/postbot/t5-small-kw2email) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information ne... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "email generation", "email"], "datasets": ["aeslc", "postbot/multi_emails_kw"], "widget": [{"text": "Thursday pay invoice need asap thanks Pierre good morning dear Harold", "example_title": "invoice"}, {"text": "dear elia when will space be ready need urgentl... | postbot/t5-small-kw2email-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"email generation",
"email",
"dataset:aeslc",
"dataset:postbot/multi_emails_kw",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T01:31:07+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #email generation #email #dataset-aeslc #dataset-postbot/multi_emails_kw #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-kw2email-v2
This model is a fine-tuned version of postbot/t5-small-kw2email on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# t5-small-kw2email-v2\n\nThis model is a fine-tuned version of postbot/t5-small-kw2email on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedu... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #email generation #email #dataset-aeslc #dataset-postbot/multi_emails_kw #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-kw2email-v2\n\nThis model is a fine-tuned ... |
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-PixelCopterV1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-... | jjjjjjjjjj/Reinforce-PixelCopterV1 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-08T02:22:44+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 ... |
image-classification | transformers |
# ViT Fine-tuned on Stanford Car Dataset
Base model: https://huggingface.co/google/vit-base-patch16-224
This achieves around 86% on the testing set, you can use it as a baseline for further tuning.
# Dataset Description
The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically ... | {"license": "apache-2.0"} | therealcyberlord/stanford-car-vit-patch16 | null | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-08T02:25:33+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #vit #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ViT Fine-tuned on Stanford Car Dataset
Base model: URL
This achieves around 86% on the testing set, you can use it as a baseline for further tuning.
# Dataset Description
The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Te... | [
"# ViT Fine-tuned on Stanford Car Dataset\n\nBase model: URL\n\nThis achieves around 86% on the testing set, you can use it as a baseline for further tuning.",
"# Dataset Description \n\nThe Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year... | [
"TAGS\n#transformers #pytorch #safetensors #vit #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ViT Fine-tuned on Stanford Car Dataset\n\nBase model: URL\n\nThis achieves around 86% on the testing set, you can use it as a baseline for further tun... |
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. -->
# finetuned-marktextepoch-n500
This model is a fine-tuned version of [leokai/finetuned-marktextepoch-n200](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-marktextepoch-n500", "results": []}]} | leokai/finetuned-marktextepoch-n500 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T03:03:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuned-marktextepoch-n500
This model is a fine-tuned version of leokai/finetuned-marktextepoch-n200 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.4281
- eval_runtime: 11.4175
- eval_samples_per_second: 279.571
- eval_steps_per_second: 34.946
- epoch: 218.0
- step:... | [
"# finetuned-marktextepoch-n500\n\nThis model is a fine-tuned version of leokai/finetuned-marktextepoch-n200 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 2.4281\n- eval_runtime: 11.4175\n- eval_samples_per_second: 279.571\n- eval_steps_per_second: 34.946\n- epoch: 218... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuned-marktextepoch-n500\n\nThis model is a fine-tuned version of leokai/finetuned-marktextepoch-n200 on the None dataset.\nIt achieves the ... |
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. -->
# multi_news_article_title_25000_2
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "multi_news_article_title_25000_2", "results": []}]} | abdulmatinomotoso/multi_news_article_title_25000_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T04:04:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| multi\_news\_article\_title\_25000\_2
=====================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1740
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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #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: 1\n* eval... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | amartyobanerjee/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T05:18:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0294
* Rouge1: 16.497
* Rouge2: 8.0618
* Rougel: 16.2979
* Rougelsum: 16.1465
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
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. -->
# sentcore
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended ... | {"language": ["zh"], "tags": ["zh", "zh-tw", "generated_from_trainer"], "model-index": [{"name": "sentcore", "results": []}]} | theta/sentcore | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"zh",
"zh-tw",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T05:44:21+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #token-classification #zh #zh-tw #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# sentcore
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters ... | [
"# sentcore\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n... | [
"TAGS\n#transformers #pytorch #bert #token-classification #zh #zh-tw #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# sentcore\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... |
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. -->
# recipistil
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-disti... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipistil", "results": []}]} | paola-md/recipistil | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T05:51:20+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipistil
==========
This model is a fine-tuned version of paola-md/recipe-distilroberta-Is on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9743
* Rmse: 1.4051
* Mse: 1.9743
* Mae: 1.0578
Model description
-----------------
More information needed
Intended uses & limit... | [
"### 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: 15",
"### Train... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta... | {"library_name": "keras"} | osanseviero/osans | null | [
"keras",
"region:us"
] | null | 2022-08-08T06:04:37+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
summarization | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1228646724
- CO2 Emissions (in grams): 1368.8941
## Validation Metrics
- Loss: 2.319
- Rouge1: 43.703
- Rouge2: 16.106
- RougeL: 23.715
- RougeLsum: 38.984
- Gen Len: 141.091
## Usage
You can use cURL to access this model:
```
$ curl -X PO... | {"language": ["unk"], "tags": ["autotrain", "summarization"], "datasets": ["vishw2703/autotrain-data-unisumm_3"], "co2_eq_emissions": {"emissions": 1368.894142563709}} | vishw2703/unisumm_3-1228646724 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:vishw2703/autotrain-data-unisumm_3",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T06:14:24+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain #summarization #unk #dataset-vishw2703/autotrain-data-unisumm_3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1228646724
- CO2 Emissions (in grams): 1368.8941
## Validation Metrics
- Loss: 2.319
- Rouge1: 43.703
- Rouge2: 16.106
- RougeL: 23.715
- RougeLsum: 38.984
- Gen Len: 141.091
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1228646724\n- CO2 Emissions (in grams): 1368.8941",
"## Validation Metrics\n\n- Loss: 2.319\n- Rouge1: 43.703\n- Rouge2: 16.106\n- RougeL: 23.715\n- RougeLsum: 38.984\n- Gen Len: 141.091",
"## Usage\n\nYou can use cURL to access this... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain #summarization #unk #dataset-vishw2703/autotrain-data-unisumm_3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1228646724\n- CO2 Emissi... |
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... | naveenkb/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-08T06:34:35+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 |
# Elon DialoGPT Model | {"tags": ["conversational"]} | aniketface/DialoGPT-medium-elon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T06:40:10+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Elon DialoGPT Model | [
"# Elon DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Elon DialoGPT Model"
] |
image-classification | timm | # Model card for timm-mobilevitv2_050-beans
This model is a fine-tuned version of `mobilevitv2_050` (from timm) on the `beans` dataset. It achieves the following results on the validation set:
- Loss: 0.08228
- Accuracy: 0.9850
- F1Score: 0.9846
## Image normalization
Imagenet
```python
mean = [0.485, 0.456, 0.406... | {"tags": ["image-classification", "timm"], "datasets": ["beans"], "library_tag": "timm", "widget": [{"src": "https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg", "example_title": "Healthy"}, {"src": "https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_spot.jpeg", "example_title"... | Bingsu/timm-mobilevitv2_050-beans | null | [
"timm",
"pytorch",
"image-classification",
"dataset:beans",
"region:us"
] | null | 2022-08-08T06:40:55+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #dataset-beans #region-us
| # Model card for timm-mobilevitv2_050-beans
This model is a fine-tuned version of 'mobilevitv2_050' (from timm) on the 'beans' dataset. It achieves the following results on the validation set:
- Loss: 0.08228
- Accuracy: 0.9850
- F1Score: 0.9846
## Image normalization
Imagenet
| [
"# Model card for timm-mobilevitv2_050-beans\n\nThis model is a fine-tuned version of 'mobilevitv2_050' (from timm) on the 'beans' dataset. It achieves the following results on the validation set:\n\n- Loss: 0.08228\n- Accuracy: 0.9850\n- F1Score: 0.9846",
"## Image normalization\n\nImagenet"
] | [
"TAGS\n#timm #pytorch #image-classification #dataset-beans #region-us \n",
"# Model card for timm-mobilevitv2_050-beans\n\nThis model is a fine-tuned version of 'mobilevitv2_050' (from timm) on the 'beans' dataset. It achieves the following results on the validation set:\n\n- Loss: 0.08228\n- Accuracy: 0.9850\n- ... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# eliwill/distilgpt2-discursive-krishna
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "eliwill/distilgpt2-discursive-krishna", "results": []}]} | eliwill/distilgpt2-discursive-krishna | null | [
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T06:49:44+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| eliwill/distilgpt2-discursive-krishna
=====================================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.2503
* Validation Loss: 3.1371
* Epoch: 0
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'nam... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.5
This model is a distilled version of [Lvxue/finetuned-mt5-base](https://huggingface.co/Lvxue/finetuned-m... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"... | Lvxue/distilled-mt5-small-0.5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T07:12:07+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.5
This model is a distilled version of Lvxue/finetuned-mt5-base on google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7455
- Bleu: 1.2575
- Gen Len: 94.3597
## Model description
More information needed
## Intended uses & limitat... | [
"# distilled-mt5-small-0.5\n\nThis model is a distilled version of Lvxue/finetuned-mt5-base on google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.7455\n- Bleu: 1.2575\n- Gen Len: 94.3597",
"## Model description\n\nMore information needed",
"## Inten... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.5\n\nThis model is a distilled version of Lvxue/finetuned-mt5-bas... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.9
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.9", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"... | Lvxue/distilled-mt5-small-0.9 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T07:18:23+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.9
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4137
- Bleu: 2.6938
- Gen Len: 69.7484
## Model description
More information needed
## Intended uses & limitations
More information need... | [
"# distilled-mt5-small-0.9\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.4137\n- Bleu: 2.6938\n- Gen Len: 69.7484",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.9\n\nThis model is a fine-tuned version of google/mt5-small on th... |
question-answering | transformers |
# XLM RoBERTa for Czech+English Extractive Question Answering
This is the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model with a head for extractive question answering trained on a combination of [English SQuAD 1.1](https://huggingface.co/datasets/squad) and [Czech SQAD 3.0](https://lindat.cz/repo... | {"language": ["multilingual", "cs", "en"], "tags": ["exbert", "question-answering"]} | gaussalgo/xlm-roberta-large_extractive-QA_en-cs | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"question-answering",
"exbert",
"multilingual",
"cs",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T07:40:20+00:00 | [] | [
"multilingual",
"cs",
"en"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #question-answering #exbert #multilingual #cs #en #endpoints_compatible #region-us
|
# XLM RoBERTa for Czech+English Extractive Question Answering
This is the XLM-RoBERTa-large model with a head for extractive question answering trained on a combination of English SQuAD 1.1 and Czech SQAD 3.0 Question Answering datasets. For the Czech SQAD 3.0, original contexts (=whole Wikipedia websites) were limit... | [
"# XLM RoBERTa for Czech+English Extractive Question Answering\n\nThis is the XLM-RoBERTa-large model with a head for extractive question answering trained on a combination of English SQuAD 1.1 and Czech SQAD 3.0 Question Answering datasets. For the Czech SQAD 3.0, original contexts (=whole Wikipedia websites) were... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #exbert #multilingual #cs #en #endpoints_compatible #region-us \n",
"# XLM RoBERTa for Czech+English Extractive Question Answering\n\nThis is the XLM-RoBERTa-large model with a head for extractive question answering trained on a combinati... |
token-classification | transformers |
This is a model for named entity recognition of Japanese medical documents.
### How to use
Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- text.txt
- NER_medNLP.py
- predict.py
You can use this model by running predict.py.
```
python3 predict.py
```
### Input Exa... | {"language": ["ja"], "license": ["cc-by-4.0"], "tags": ["NER", "medical documents"], "datasets": ["MedTxt-CR-JA-training-v2.xml"], "metrics": ["NTCIR-16 Real-MedNLP subtask 1"]} | Tomohiro/RealMedNLP_CR_JA | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"medical documents",
"ja",
"dataset:MedTxt-CR-JA-training-v2.xml",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T07:55:23+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #medical documents #ja #dataset-MedTxt-CR-JA-training-v2.xml #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
This is a model for named entity recognition of Japanese medical documents.
### How to use
Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- URL
- NER_medNLP.py
- URL
You can use this model by running URL.
### Input Example
### Output Example
| [
"### How to use\n\nDownload the following five files and put into the same folder.\n- id_to_tags.pkl\n- key_attr.pkl\n- URL\n- NER_medNLP.py\n- URL\n\nYou can use this model by running URL.",
"### Input Example",
"### Output Example"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #medical documents #ja #dataset-MedTxt-CR-JA-training-v2.xml #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\nDownload the following five files and put into the same folder.\n- id_to_tags.pkl\n- key_att... |
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="Mahmoud7/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | Mahmoud7/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-08T08:14:25+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"
] |
automatic-speech-recognition | transformers | 使用seq-seq模型
encoder_id = "wbbbbb/wav2vec2-large-chinese-zh-cn"
decoder_id = "IDEA-CCNL/Randeng-BART-139M
wer=68.3 | {"license": "apache-2.0"} | wbbbbb/chinese-speech-rec | null | [
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T08:20:53+00:00 | [] | [] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #license-apache-2.0 #endpoints_compatible #region-us
| 使用seq-seq模型
encoder_id = "wbbbbb/wav2vec2-large-chinese-zh-cn"
decoder_id = "IDEA-CCNL/Randeng-BART-139M
wer=68.3 | [] | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #license-apache-2.0 #endpoints_compatible #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="Mahmoud7/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | Mahmoud7/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-08T08:21:45+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"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | mohammadhadiarabi/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-08T08:22:10+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
sentence-similarity | sentence-transformers |
# osanseviero/distilroberta-base-sentence-transformer
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-Transformer... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["embedding-data/QQP_triplets"], "pipeline_tag": "sentence-similarity"} | osanseviero/distilroberta-base-sentence-transformer | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:embedding-data/QQP_triplets",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T08:33:42+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-embedding-data/QQP_triplets #endpoints_compatible #region-us
|
# osanseviero/distilroberta-base-sentence-transformer
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-tr... | [
"# osanseviero/distilroberta-base-sentence-transformer\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 s... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #dataset-embedding-data/QQP_triplets #endpoints_compatible #region-us \n",
"# osanseviero/distilroberta-base-sentence-transformer\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_finetuned_astronomicalNER
This model is a fine-tuned version of [xlm-roberta-large-finetuned-conll03-english](https://hu... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "8Agos", "results": []}]} | mazzaqq/roberta_finetuned_astronomicalNER | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T09:22:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| roberta\_finetuned\_astronomicalNER
===================================
This model is a fine-tuned version of xlm-roberta-large-finetuned-conll03-english for NER on astronomical objects.
The dataset comes from the Shared Task DEAL: Detecting Entities in the Astrophysics Literature
The model achieves the following r... | [
"### 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 #xlm-roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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-optimised-finetuned-financial-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-optimised-finetuned-financial-sentiment", "results": []}]} | hazrulakmal/distilbert-optimised-finetuned-financial-sentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T09:48:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-optimised-finetuned-financial-sentiment
==================================================
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: 0.3112
* Accuracy: 0.8582
* F1: 0.8581
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 #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... |
null | transformers |
# T5(v1.1)-SLED (SLiding-Encoder and Decoder, base-sized model)
SLED models use pretrained, short-range encoder-decoder models, and apply them over
long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder
## Model description
This SLED mod... | {"language": "en", "license": "mit"} | tau/t5-v1_1-base-sled | null | [
"transformers",
"tau/sled",
"en",
"arxiv:2208.00748",
"arxiv:1910.10683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T09:51:50+00:00 | [
"2208.00748",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #tau/sled #en #arxiv-2208.00748 #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us
|
# T5(v1.1)-SLED (SLiding-Encoder and Decoder, base-sized model)
SLED models use pretrained, short-range encoder-decoder models, and apply them over
long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder
## Model description
This SLED mod... | [
"# T5(v1.1)-SLED (SLiding-Encoder and Decoder, base-sized model) \n\nSLED models use pretrained, short-range encoder-decoder models, and apply them over \nlong-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder",
"## Model description\n\n... | [
"TAGS\n#transformers #tau/sled #en #arxiv-2208.00748 #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us \n",
"# T5(v1.1)-SLED (SLiding-Encoder and Decoder, base-sized model) \n\nSLED models use pretrained, short-range encoder-decoder models, and apply them over \nlong-text inputs by splitting the inp... |
null | null | qweqwasdo | {} | qwqwew/asdasasd | null | [
"region:us"
] | null | 2022-08-08T10:29:18+00:00 | [] | [] | TAGS
#region-us
| qweqwasdo | [] | [
"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-ft1500_norm500_aug1
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm500_aug1", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm500_aug1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T10:37:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm500\_aug1
=======================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9086
* Mse: 3.6357
* Mae: 1.0762
* R2: 0.2894
* Accu... | [
"### 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: 5",
"### Training... | [
"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... |
text2text-generation | transformers |
# mT5-base for Prime Czech+English Generative Question Answering
This is the [mt5-base](https://huggingface.co/google/mt5-base) model with an LM head for a generation of extractive answers,
given a small set of 2-5 demonstrations (i.e. primes).
## Priming
Note that **this is a priming model** that expects a **set ... | {"language": ["multilingual", "cs", "en"], "tags": ["generation"], "widget": [{"text": "Ot\u00e1zka: Jak\u00fd je d\u016fvod dotazu z\u00e1kazn\u00edka?\nKontext: Dobr\u00fd den, \u017d\u00e1d\u00e1me zasl\u00e1n\u00ed nov\u00e9 smlouvy kv\u016fli \u0159e\u0161en\u00ed pojistn\u00e9 ud\u00e1losti. Za\u0161lete na tento... | gaussalgo/mt5-base-priming-QA_en-cs | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generation",
"multilingual",
"cs",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T11:00:27+00:00 | [] | [
"multilingual",
"cs",
"en"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generation #multilingual #cs #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5-base for Prime Czech+English Generative Question Answering
This is the mt5-base model with an LM head for a generation of extractive answers,
given a small set of 2-5 demonstrations (i.e. primes).
## Priming
Note that this is a priming model that expects a set of demonstrations of your task of interest,
sim... | [
"# mT5-base for Prime Czech+English Generative Question Answering\n\nThis is the mt5-base model with an LM head for a generation of extractive answers, \ngiven a small set of 2-5 demonstrations (i.e. primes).",
"## Priming\n\nNote that this is a priming model that expects a set of demonstrations of your task of i... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generation #multilingual #cs #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5-base for Prime Czech+English Generative Question Answering\n\nThis is the mt5-base model with an LM head for a generation of extr... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** 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 complete tutor... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | mrm8488/Worm | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-08-08T11:36:58+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us
|
# ppo Agent playing Worm
This is a trained model of a ppo agent playing Worm 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 training
#... | [
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm 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 training\... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us \n",
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\... |
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. -->
# multi_news_article_title_12000_2
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "multi_news_article_title_12000_2", "results": []}]} | abdulmatinomotoso/multi_news_article_title_12000_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T11:42:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| multi\_news\_article\_title\_12000\_2
=====================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1917
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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #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: 1\n* eval... |
text2text-generation | transformers |
# Mt5-base for Czech+English Generative Question Answering
This is the [mt5-base](https://huggingface.co/google/mt5-base) model with an LM head for a generation of extractive answers. In contrary to our [mt5-base-priming](https://huggingface.co/gaussalgo/mt5-base-priming-QA_en-cs/edit/main/README.md), this is a tradi... | {"language": ["multilingual", "cs", "en"], "tags": ["generation"]} | gaussalgo/mt5-base-generative-QA_en-cs | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generation",
"multilingual",
"cs",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T12:25:20+00:00 | [] | [
"multilingual",
"cs",
"en"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generation #multilingual #cs #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mt5-base for Czech+English Generative Question Answering
This is the mt5-base model with an LM head for a generation of extractive answers. In contrary to our mt5-base-priming, this is a traditional sequence2sequence model without priming, though can also be used on other Text extraction tasks, such as Named Entity... | [
"# Mt5-base for Czech+English Generative Question Answering\n\nThis is the mt5-base model with an LM head for a generation of extractive answers. In contrary to our mt5-base-priming, this is a traditional sequence2sequence model without priming, though can also be used on other Text extraction tasks, such as Named ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generation #multilingual #cs #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mt5-base for Czech+English Generative Question Answering\n\nThis is the mt5-base model with an LM head for a generation of extractive... |
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="sofiaoliveira/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"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": ... | sofiaoliveira/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-08T12:49:17+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 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="sofiaoliveira/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type": ... | sofiaoliveira/q-FrozenLake-v1-8x8-noSlippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-08T13:08:41+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-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-v1-8x8-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="sofiaoliveira/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | sofiaoliveira/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-08T13:35:35+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 | 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... | neskue/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-08T13:56:33+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... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-finetuned-squad", "results": []}]} | harveyagraphcore/bert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T13:58:15+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# bert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descriptio... |
null | null |
This repository houses an extended version of the [ViT Base/16 model from 🤗 Transformers](https://huggingface.co/docs/transformers/main/en/model_doc/vit). In particular, it provides the following:
* A `SavedModel` that has the preprocessing and postprocessing operations embedded inside the computation graph of the m... | {"license": "apache-2.0"} | deploy-hf-tf-vit/vit-base16-extended | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-08T13:59:35+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
This repository houses an extended version of the ViT Base/16 model from Transformers. In particular, it provides the following:
* A 'SavedModel' that has the preprocessing and postprocessing operations embedded inside the computation graph of the model.
* A 'tar' archive of the SavedModel.
Please refer to the foll... | [] | [
"TAGS\n#license-apache-2.0 #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... | LilOpa/LunarLanderPPO | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-08T14:13:56+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-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | lauer/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T14:14:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2133
* Accuracy: 0.9305
* F1: 0.9306
Model description
-----------------
Mo... | [
"### 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... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | AntiSquid/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-08T14:15:52+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
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. -->
# TwitchLeagueBert-1000k-finetuned-highlight-detection
This model is a fine-tuned version of [Epidot/TwitchLeagueBert-1000k](https... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "f1", "recall"], "model-index": [{"name": "TwitchLeagueBert-1000k-finetuned-highlight-detection", "results": []}]} | Epidot/TwitchLeagueBert-1000k-finetuned-highlight-detection | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T14:24:46+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| TwitchLeagueBert-1000k-finetuned-highlight-detection
====================================================
This model is a fine-tuned version of Epidot/TwitchLeagueBert-1000k on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1146
* Precision: 0.4420
* F1: 0.3977
* Recall: 0.3614... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_si... |
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-ft1500_norm500_aug2
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm500_aug2", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm500_aug2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T14:25:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm500\_aug2
=======================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6847
* Mse: 2.7387
* Mae: 1.0099
* R2: 0.4647
* Accu... | [
"### 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: 1",
"### Training... | [
"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... |
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. -->
# DNA_bert_3-finetuned
This model is a fine-tuned version of [armheb/DNA_bert_3](https://huggingface.co/armheb/DNA_bert_3) on an u... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNA_bert_3-finetuned", "results": []}]} | Mozart-coder/DNA_bert_3-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T14:29:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DNA\_bert\_3-finetuned
======================
This model is a fine-tuned version of armheb/DNA\_bert\_3 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5788
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_si... |
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-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | Jinchen/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T14:30:12+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5273
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: IPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_s... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #t5 #text2text-generation #generated_from_trainer #dataset-xsum #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\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-cnn-adam8bit-bs16x64acc
This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn_full-adafactor-... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-cnn-adam8bit-bs16x64acc", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailym... | oMateos2020/pegasus-newsroom-cnn-adam8bit-bs16x64acc | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T15:00:55+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-newsroom-cnn-adam8bit-bs16x64acc
========================================
This model is a fine-tuned version of oMateos2020/pegasus-newsroom-cnn\_full-adafactor-bs6 on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8664
* Rouge1: 44.1788
* Rouge2: 21.453
* Rou... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #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: 6.4e-05\n* train\... |
text2text-generation | transformers |
# ViSpell v1
- model-name: vispell-small-v1
- date-created: 08-08-22
- version: 1.2 | {"language": ["vi"], "library_name": "transformers", "tags": ["spelling"], "pipeline_tag": "text2text-generation"} | ademax/vispell-small-v1 | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"spelling",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T15:20:22+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #marian #text2text-generation #spelling #vi #autotrain_compatible #endpoints_compatible #region-us
|
# ViSpell v1
- model-name: vispell-small-v1
- date-created: 08-08-22
- version: 1.2 | [
"# ViSpell v1\n- model-name: vispell-small-v1\n- date-created: 08-08-22\n- version: 1.2"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #spelling #vi #autotrain_compatible #endpoints_compatible #region-us \n",
"# ViSpell v1\n- model-name: vispell-small-v1\n- date-created: 08-08-22\n- version: 1.2"
] |
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. -->
# finetuned-marktextepoch-n600
This model is a fine-tuned version of [leokai/finetuned-marktextepoch-n500](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-marktextepoch-n600", "results": []}]} | leokai/finetuned-marktextepoch-n600 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T15:24:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned-marktextepoch-n600
============================
This model is a fine-tuned version of leokai/finetuned-marktextepoch-n500 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6814
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### 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: 182",
"### 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: ... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **HumanoidFlagrunBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HumanoidFlagrunBulletEnv-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... | {"library_name": "stable-baselines3", "tags": ["HumanoidFlagrunBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HumanoidFlagrunBull... | abcp4/a2c-HumanoidFlagrunBulletEnv-v0 | null | [
"stable-baselines3",
"HumanoidFlagrunBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-08T15:25:26+00:00 | [] | [] | TAGS
#stable-baselines3 #HumanoidFlagrunBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing HumanoidFlagrunBulletEnv-v0
This is a trained model of a A2C agent playing HumanoidFlagrunBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing HumanoidFlagrunBulletEnv-v0\nThis is a trained model of a A2C agent playing HumanoidFlagrunBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #HumanoidFlagrunBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing HumanoidFlagrunBulletEnv-v0\nThis is a trained model of a A2C agent playing HumanoidFlagrunBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage ... |
token-classification | stanza | # Stanza model for Buryat (bxr)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](ht... | {"language": "bxr", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-bxr | null | [
"stanza",
"token-classification",
"bxr",
"license:apache-2.0",
"region:us"
] | null | 2022-08-08T15:34:18+00:00 | [] | [
"bxr"
] | TAGS
#stanza #token-classification #bxr #license-apache-2.0 #region-us
| # Stanza model for Buryat (bxr)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in our website and ... | [
"# Stanza model for Buryat (bxr)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about it in our websi... | [
"TAGS\n#stanza #token-classification #bxr #license-apache-2.0 #region-us \n",
"# Stanza model for Buryat (bxr)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-th... |
token-classification | stanza | # Stanza model for Upper_Sorbian (hsb)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our webs... | {"language": "hsb", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-hsb | null | [
"stanza",
"token-classification",
"hsb",
"license:apache-2.0",
"region:us"
] | null | 2022-08-08T15:35:00+00:00 | [] | [
"hsb"
] | TAGS
#stanza #token-classification #hsb #license-apache-2.0 #region-us
| # Stanza model for Upper_Sorbian (hsb)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in our websi... | [
"# Stanza model for Upper_Sorbian (hsb)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about it in ou... | [
"TAGS\n#stanza #token-classification #hsb #license-apache-2.0 #region-us \n",
"# Stanza model for Upper_Sorbian (hsb)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings stat... |
token-classification | stanza | # Stanza model for Kurmanji (kmr)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](... | {"language": "kmr", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-kmr | null | [
"stanza",
"token-classification",
"kmr",
"license:apache-2.0",
"region:us"
] | null | 2022-08-08T15:35:35+00:00 | [] | [
"kmr"
] | TAGS
#stanza #token-classification #kmr #license-apache-2.0 #region-us
| # Stanza model for Kurmanji (kmr)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in our website an... | [
"# Stanza model for Kurmanji (kmr)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about it in our web... | [
"TAGS\n#stanza #token-classification #kmr #license-apache-2.0 #region-us \n",
"# Stanza model for Kurmanji (kmr)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-... |
token-classification | stanza | # Stanza model for Kazakh (kk)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](htt... | {"language": "kk", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-kk | null | [
"stanza",
"token-classification",
"kk",
"license:apache-2.0",
"region:us"
] | null | 2022-08-08T15:36:10+00:00 | [] | [
"kk"
] | TAGS
#stanza #token-classification #kk #license-apache-2.0 #region-us
| # Stanza model for Kazakh (kk)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in our website and o... | [
"# Stanza model for Kazakh (kk)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about it in our websit... | [
"TAGS\n#stanza #token-classification #kk #license-apache-2.0 #region-us \n",
"# Stanza model for Kazakh (kk)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-... |
token-classification | stanza | # Stanza model for Ligurian (lij)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](... | {"language": "lij", "license": "apache-2.0", "library_name": "stanza", "tags": ["stanza", "token-classification"]} | stanfordnlp/stanza-lij | null | [
"stanza",
"token-classification",
"lij",
"license:apache-2.0",
"region:us"
] | null | 2022-08-08T15:36:50+00:00 | [] | [
"lij"
] | TAGS
#stanza #token-classification #lij #license-apache-2.0 #region-us
| # Stanza model for Ligurian (lij)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in our website an... | [
"# Stanza model for Ligurian (lij)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.\nFind more about it in our web... | [
"TAGS\n#stanza #token-classification #lij #license-apache-2.0 #region-us \n",
"# Stanza model for Ligurian (lij)\nStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-... |
token-classification | flair |
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-english-ontonotes-large")
# make example sentence
sentence = Sente... | {"tags": ["flair", "token-classification", "sequence-tagger-model"]} | osanseviero/flair_test | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"region:us"
] | null | 2022-08-08T15:55:04+00:00 | [] | [] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #region-us
|
### Demo: How to use in Flair
Requires:
- Flair ('pip install flair')
| [
"### Demo: How to use in Flair\n\nRequires: \n- Flair ('pip install flair')"
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #region-us \n",
"### Demo: How to use in Flair\n\nRequires: \n- Flair ('pip install flair')"
] |
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... | keljai/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-08T16:13:37+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... |
token-classification | transformers |
license: apache-2.0
datasets:
- wnut_17
Number of classes:
- 12 classed
Tags
- 0 = 'O',
- 1 = 'B-corporation',
- 2 = 'I-corporation',
- 3 = 'B-creative-work',
- 4 = 'I-creative-work',
- 5 = 'B-group',
- 6 = 'I-group',
- 7 = 'B-location',
- 8 = 'I-location',
- 9 = 'B-person',
- 10 = 'I-person',
- 11 = 'B-... | {"tags": ["NER", "Name Entity Recognition"], "datasets": ["wnut_17"]} | Waleed-bin-Qamar/NER-distilbert-base-uncased-wnut_17 | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"NER",
"Name Entity Recognition",
"dataset:wnut_17",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T16:26:38+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #NER #Name Entity Recognition #dataset-wnut_17 #autotrain_compatible #endpoints_compatible #region-us
|
license: apache-2.0
datasets:
- wnut_17
Number of classes:
- 12 classed
Tags
- 0 = 'O',
- 1 = 'B-corporation',
- 2 = 'I-corporation',
- 3 = 'B-creative-work',
- 4 = 'I-creative-work',
- 5 = 'B-group',
- 6 = 'I-group',
- 7 = 'B-location',
- 8 = 'I-location',
- 9 = 'B-person',
- 10 = 'I-person',
- 11 = 'B-... | [] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #token-classification #NER #Name Entity Recognition #dataset-wnut_17 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
# MarianMT exported to the ONNX format
## Install Optimum
```bash
pip install optimum
```
## Usage example
```python
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("icon-it-tdtu/mt-en-vi-optimum")
model = ORTModelForSeq2SeqLM.fro... | {"language": ["en", "vi"], "license": "apache-2.0", "tags": ["translation"]} | icon-it-tdtu/mt-en-vi-optimum | null | [
"transformers",
"onnx",
"marian",
"text2text-generation",
"translation",
"en",
"vi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-08T17:04:51+00:00 | [] | [
"en",
"vi"
] | TAGS
#transformers #onnx #marian #text2text-generation #translation #en #vi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# MarianMT exported to the ONNX format
## Install Optimum
## Usage example
| [
"# MarianMT exported to the ONNX format",
"## Install Optimum",
"## Usage example"
] | [
"TAGS\n#transformers #onnx #marian #text2text-generation #translation #en #vi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MarianMT exported to the ONNX format",
"## Install Optimum",
"## Usage example"
] |
translation | transformers |
# MarianMT exported to the ONNX format
## Install Optimum
```bash
pip install optimum
```
## Usage example
```python
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("icon-it-tdtu/mt-vi-en-optimum")
model = ORTModelForSeq2SeqLM.fro... | {"language": ["vi", "en"], "license": "apache-2.0", "tags": ["translation"]} | icon-it-tdtu/mt-vi-en-optimum | null | [
"transformers",
"onnx",
"marian",
"text2text-generation",
"translation",
"vi",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-08T17:10:52+00:00 | [] | [
"vi",
"en"
] | TAGS
#transformers #onnx #marian #text2text-generation #translation #vi #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# MarianMT exported to the ONNX format
## Install Optimum
## Usage example
| [
"# MarianMT exported to the ONNX format",
"## Install Optimum",
"## Usage example"
] | [
"TAGS\n#transformers #onnx #marian #text2text-generation #translation #vi #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MarianMT exported to the ONNX format",
"## Install Optimum",
"## Usage example"
] |
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. -->
# deberta-v3-large-finetuned-dagpap22-only-and-real
This model is a fine-tuned version of [domenicrosati/deberta-v3-large-finetune... | {"tags": ["text-classification", "generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "deberta-v3-large-finetuned-dagpap22-only-and-real", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-dagpap22-only-and-real | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T17:16:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-finetuned-dagpap22-only-and-real
=================================================
This model is a fine-tuned version of domenicrosati/deberta-v3-large-finetuned-dagpap22-only-and-real on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0008
* F1: 0.9999
* Precis... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* ev... |
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. -->
# great-books-bot-4
This model is a fine-tuned version of [erikanesse/great-books-bot](https://huggingface.co/erikanesse/great-boo... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "great-books-bot-4", "results": []}]} | erikanesse/great-books-bot-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T17:19:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| great-books-bot-4
=================
This model is a fine-tuned version of erikanesse/great-books-bot on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9485
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 5\n* total\\_train\\_batch\\_size: 5\n* optimizer: Adam with betas... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batc... |
text2text-generation | transformers | # t5-base-qa-ner-conll
Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
t5-base model tuned on conll2003 dataset.
https://github.com/ovbystrova/InstructionNER
## Inference
```shell
git clone https://github.com/ovbystrova/InstructionNER
cd InstructionNER
```
```python
from in... | {"language": ["en"], "license": "mit", "tags": ["pytorch", "ner", "text generation", "seq2seq"], "datasets": ["conll2003"], "metrics": ["f1"], "inference": false} | olgaduchovny/t5-base-ner-mit-movie | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ner",
"text generation",
"seq2seq",
"en",
"dataset:conll2003",
"arxiv:2203.03903",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T17:21:54+00:00 | [
"2203.03903"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ner #text generation #seq2seq #en #dataset-conll2003 #arxiv-2203.03903 #license-mit #autotrain_compatible #text-generation-inference #region-us
| # t5-base-qa-ner-conll
Unofficial implementation of InstructionNER.
t5-base model tuned on conll2003 dataset.
URL
## Inference
| [
"# t5-base-qa-ner-conll\n\nUnofficial implementation of InstructionNER.\nt5-base model tuned on conll2003 dataset.\n\nURL",
"## Inference"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ner #text generation #seq2seq #en #dataset-conll2003 #arxiv-2203.03903 #license-mit #autotrain_compatible #text-generation-inference #region-us \n",
"# t5-base-qa-ner-conll\n\nUnofficial implementation of InstructionNER.\nt5-base model tuned on conll2003 da... |
text2text-generation | transformers | # t5-base-qa-ner-conll
Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
t5-base model tuned on conll2003 dataset.
https://github.com/ovbystrova/InstructionNER
## Inference
```shell
git clone https://github.com/ovbystrova/InstructionNER
cd InstructionNER
```
```python
from in... | {"language": ["en"], "license": "mit", "tags": ["pytorch", "ner", "text generation", "seq2seq"], "datasets": ["conll2003"], "metrics": ["f1"], "inference": false} | olgaduchovny/t5-base-ner-mit-restaurant | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ner",
"text generation",
"seq2seq",
"en",
"dataset:conll2003",
"arxiv:2203.03903",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T17:29:13+00:00 | [
"2203.03903"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ner #text generation #seq2seq #en #dataset-conll2003 #arxiv-2203.03903 #license-mit #autotrain_compatible #text-generation-inference #region-us
| # t5-base-qa-ner-conll
Unofficial implementation of InstructionNER.
t5-base model tuned on conll2003 dataset.
URL
## Inference
| [
"# t5-base-qa-ner-conll\n\nUnofficial implementation of InstructionNER.\nt5-base model tuned on conll2003 dataset.\n\nURL",
"## Inference"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ner #text generation #seq2seq #en #dataset-conll2003 #arxiv-2203.03903 #license-mit #autotrain_compatible #text-generation-inference #region-us \n",
"# t5-base-qa-ner-conll\n\nUnofficial implementation of InstructionNER.\nt5-base model tuned on conll2003 da... |
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"]} | andres-hsn/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-08T17:39:50+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... |
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. -->
# usv3_usdc_predictor_0
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "usv3_usdc_predictor_0", "results": []}]} | mlegls/usv3_usdc_predictor_0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T17:40:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# usv3_usdc_predictor_0
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follow... | [
"# usv3_usdc_predictor_0\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Traini... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# usv3_usdc_predictor_0\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore informati... |
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