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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | SamuelMYoussef/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T15:07:48+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# bert-small-juman-bpe
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the foll... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-small-juman-bpe", "results": []}]} | schnell/bert-small-juman-bpe | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T15:12:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-small-juman-bpe
====================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Accuracy: 0.6317
* Loss: 1.7829
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* total\\_train\\_batch\\_size: 768\n* total\\_eval\\_batch\\_size: 24\n... | [
"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: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_... |
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/1492447040193900546/LtTd... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jockforbrains/1658852709222/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jockforbrains | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T15:23:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
JockForBrains (️ May contain morphs)
@jockforbrains
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 repor... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-wnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}... | aemami1/distilbert-base-uncased-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T15:36:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-wnli
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6950
* Accuracy: 0.5493
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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/1492447040193900546/LtTd... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bearfoothunter1-jockforbrains-recentrift/1658853737112/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/bearfoothunter1-jockforbrains-recentrift | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T15:37:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
JockForBrains (️ May contain morphs) & Demonic Executioner & the real bearfoothunter 🇺🇦🇺🇦🇺🇦
@bearfoothunter1-jockforbrains-recentrift
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... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1305228695444090882/aU_V... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/surlaroute/1658853747255/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/surlaroute | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T15:39:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Melody ️
@surlaroute
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers | # Model Description
The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Sto... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | phjhk/hklegal-xlm-r-base-t | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
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"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha... | null | 2022-07-26T15:41:57+00:00 | [
"1911.02116"
] | [
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"ar",
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"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"i... | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #p... | # Model Description
The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoB... | [
"# Model Description\n\nThe XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Faceboo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om ... |
automatic-speech-recognition | transformers |
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model has been trained on noisy data in order to make the acoustic model robust to noisy audio data.
This model has apostrophes and hyphens.
Th... | {"language": ["uk"], "license": "apache-2.0", "datasets": ["mozilla-foundation/common_voice_10_0"]} | Yehor/wav2vec2-xls-r-300m-uk-with-small-lm-noisy | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"uk",
"dataset:mozilla-foundation/common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T16:07:07+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us
| 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk
⭐ See other Ukrainian models - URL
This model has been trained on noisy data in order to make the acoustic model robust to noisy audio data.
This model has apostrophes and hyphens.
The language model is trained on the texts ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # Model Description
The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Sto... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | phjhk/hklegal-xlm-r-large-t | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha... | null | 2022-07-26T16:14:00+00:00 | [
"1911.02116"
] | [
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
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"ca",
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"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"i... | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #p... | # Model Description
The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoB... | [
"# Model Description\n\nThe XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Faceboo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om ... |
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/1541995552505831424/K1gt... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/hiddenlure/1658855843772/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/hiddenlure | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T16:14:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Hidden
@hiddenlure
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new5_0010
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new5_0010", "results": []}]} | bigmorning/distilgpt_new5_0010 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T16:19:19+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new5\_0010
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.4788
* Validation Loss: 2.3589
* Epoch: 9
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
null | null |
# Description
A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
# Model Overview
This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
## Da... | {"license": "apache-2.0", "tags": ["monai", "medical"]} | katielink/spleen_ct_segmentation_v0.1.0 | null | [
"monai",
"medical",
"arxiv:1811.12506",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-26T17:38:32+00:00 | [
"1811.12506"
] | [] | TAGS
#monai #medical #arxiv-1811.12506 #license-apache-2.0 #has_space #region-us
|
# Description
A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
# Model Overview
This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
## Da... | [
"# Description\nA pre-trained model for volumetric (3D) segmentation of the spleen from CT image.",
"# Model Overview\nThis model is trained using the runner-up [1] awarded pipeline of the \"Medical Segmentation Decathlon Challenge 2018\" using the UNet architecture [2] with 32 training images and 9 validation im... | [
"TAGS\n#monai #medical #arxiv-1811.12506 #license-apache-2.0 #has_space #region-us \n",
"# Description\nA pre-trained model for volumetric (3D) segmentation of the spleen from CT image.",
"# Model Overview\nThis model is trained using the runner-up [1] awarded pipeline of the \"Medical Segmentation Decathlon Ch... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": ["en"], "tags": ["albert", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-albert-base-v2-4096 | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"long context",
"custom_code",
"en",
"arxiv:2210.15497",
"arxiv:1909.11942",
"autotrain_compatible",
"region:us"
] | null | 2022-07-26T17:59:53+00:00 | [
"2210.15497",
"1909.11942"
] | [
"en"
] | TAGS
#transformers #pytorch #albert #fill-mask #long context #custom_code #en #arxiv-2210.15497 #arxiv-1909.11942 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
This model is adapted from AlBERT-base-v2 without additio... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n\n\nThis model is adapted from AlBERT-b... | [
"TAGS\n#transformers #pytorch #albert #fill-mask #long context #custom_code #en #arxiv-2210.15497 #arxiv-1909.11942 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper.... |
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"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | vijayrag/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T18:10:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #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: 0.2202
* Accuracy: 0.925
* F1: 0.9249
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperp... |
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. -->
# distilbert-base-uncased-mlm-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-mlm-finetuned-imdb", "results": []}]} | affahrizain/distilbert-base-uncased-mlm-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T18:19:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-mlm-finetuned-imdb
==========================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6271
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
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. -->
# distilgpt_new5_0020
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new5_0020", "results": []}]} | bigmorning/distilgpt_new5_0020 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T19:03:31+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new5\_0020
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.4736
* Validation Loss: 2.3530
* Epoch: 19
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003",... | ejin/bert-base-cased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T19:04:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-finetuned-ner
=============================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0919
* Precision: 0.8940
* Recall: 0.9009
* F1: 0.8974
* Accuracy: 0.9750
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
fill-mask | transformers |
# SchizophreniaRoberta model
is a model initialized with [roberta-large](https://huggingface.co/roberta-large) and trained with Schizophrenia Reddit, a subset of [Self-Reported Mental Health Diagnoses (SMHD) dataset](https://arxiv.org/pdf/1806.05258.pdf) which consists of Reddit posts by patients with schizophrenia o... | {"language": "en", "license": "apache-2.0", "tags": ["Transformers"], "datasets": ["SMHD", "Schizophrenia Reddit"]} | Amalq/schizophrenia-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Transformers",
"en",
"arxiv:1806.05258",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T19:07:00+00:00 | [
"1806.05258"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Transformers #en #arxiv-1806.05258 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# SchizophreniaRoberta model
is a model initialized with roberta-large and trained with Schizophrenia Reddit, a subset of Self-Reported Mental Health Diagnoses (SMHD) dataset which consists of Reddit posts by patients with schizophrenia only or schizophrenia with other mental disorders and matched control. We follow ... | [
"# SchizophreniaRoberta model\n\nis a model initialized with roberta-large and trained with Schizophrenia Reddit, a subset of Self-Reported Mental Health Diagnoses (SMHD) dataset which consists of Reddit posts by patients with schizophrenia only or schizophrenia with other mental disorders and matched control. We f... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #Transformers #en #arxiv-1806.05258 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# SchizophreniaRoberta model\n\nis a model initialized with roberta-large and trained with Schizophrenia Reddit, a subset of Self-Reported Mental H... |
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. -->
# pos_test_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "pos_test_model", "results": []}]} | natalierobbins/pos_test_model | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T19:18:52+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| pos\_test\_model
================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1533
* Accuracy: 0.9531
* F1: 0.9522
* Precision: 0.9577
* Recall: 0.9531
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
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/1533674187302346752/ZMki... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rubberpomade/1658868837178/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rubberpomade | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T19:51:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rocco (Comms 2/2)
@rubberpomade
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-to-image | transformers |
<img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/igpt-logo.png" width="400">
## Model description
**iGPT-fr** 🇫🇷 is a GPT model for French pre-trained incremental language model developped by the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We adapted [GPT-fr... | {"language": ["fr"], "license": "apache-2.0", "tags": ["tf", "pytorch", "gpt2", "text-to-image"], "thumbnail": "https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png"} | asi/igpt-fr-cased-base | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"tf",
"text-to-image",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T19:57:33+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #tf #text-to-image #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<img src="URL width="400">
## Model description
iGPT-fr 🇫🇷 is a GPT model for French pre-trained incremental language model developped by the Laboratoire de Linguistique Formelle (LLF). We adapted GPT-fr 🇫🇷 model to generate images conditionned by text inputs.
## Intended uses & limitations
The model can be le... | [
"## Model description\n\niGPT-fr 🇫🇷 is a GPT model for French pre-trained incremental language model developped by the Laboratoire de Linguistique Formelle (LLF). We adapted GPT-fr 🇫🇷 model to generate images conditionned by text inputs.",
"## Intended uses & limitations\n\nThe model can be leveraged for imag... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #tf #text-to-image #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model description\n\niGPT-fr 🇫🇷 is a GPT model for French pre-trained incremental language model developped by t... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | JoAmps/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T20:12:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1378
* F1: 0.8616
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-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/1544440184653156353/O0Kt... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/khorax/1658870136126/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/khorax | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T20:14:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Khorax "Kho" Lugnut
@khorax
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Jmolano/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T20:56:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0617
* Precision: 0.9327
* Recall: 0.9498
* F1: 0.9412
* Accuracy: 0.9861
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# thesis-audio-1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "thesis-audio-1", "results": []}]} | Perselope/thesis-audio-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T21:02:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| thesis-audio-1
==============
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4268
* Wer: 0.3395
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
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... | AlexChe/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T21:05:32+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 ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation-cvqualtrics-squad-V1
## Model description
This model is a fine-tuned version of [t5-base](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-end2end-questions-generation-cvqualtrics-squad-V1", "results": []}]} | wiselinjayajos/t5-end2end-questions-generation-cvqualtrics-squad-V1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T21:28:03+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-end2end-questions-generation-cvqualtrics-squad-V1
====================================================
Model description
-----------------
This model is a fine-tuned version of t5-base on the Custom Domain-Specific dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2337
### Framework... | [
"### Framework versions\n\n\n* Transformers 4.21.0\n* Pytorch 1.12.0+cu113\n* Datasets 2.4.0\n* Tokenizers 0.12.1\n\n\nTraining procedure\n------------------",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* e... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Framework versions\n\n\n* Transformers 4.21.0\n* Pytorch 1.12.0+cu113\n* Datasets 2.4.0\n* Tokenizers 0.12.1\n\n\nTraining... |
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. -->
# distilgpt_new5_0030
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new5_0030", "results": []}]} | bigmorning/distilgpt_new5_0030 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T21:50:41+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new5\_0030
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.4683
* Validation Loss: 2.3476
* Epoch: 29
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
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/1172553341190189057/lSrf... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/archdigest/1658876796142/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/archdigest | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T22:05:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Architectural Digest
@archdigest
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln58Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln58Paraphrase")
```
```
How To Make Prompt:
informal english: i am very ready to do... | {} | BigSalmon/InformalToFormalLincoln58Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T22:05:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
make longer
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #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. -->
# robertabaseproper-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "robertabaseproper-prop-16-train-set", "results": []}]} | ultra-coder54732/robertabaseproper-prop-16-train-set | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T23:02:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# robertabaseproper-prop-16-train-set
This model is a fine-tuned version of roberta-base 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 hyper... | [
"# robertabaseproper-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base 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 pro... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# robertabaseproper-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\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/1355511252177588225/INsK... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dream/1658880860354/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dream | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T23:12:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Dream
@dream
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Jotaro DialoGPT Model | {"tags": ["conversational"]} | obl1t/DialoGPT-medium-Jotaro | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T23:25:29+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Jotaro DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #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. -->
# MiniLM-prop-16-train-set
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/micros... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "MiniLM-prop-16-train-set", "results": []}]} | ultra-coder54732/MiniLM-prop-16-train-set | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T23:40:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# MiniLM-prop-16-train-set
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# MiniLM-prop-16-train-set\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased 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",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# MiniLM-prop-16-train-set\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset.",
"## Model descrip... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the f... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds", "results": []}]} | mlegls/codeparrot-ds | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T23:42:34+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| codeparrot-ds
=============
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7958
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #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: 0.0005\n* train\\_bat... |
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. -->
# distilgpt_new5_0040
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new5_0040", "results": []}]} | bigmorning/distilgpt_new5_0040 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T00:33:32+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new5\_0040
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.4633
* Validation Loss: 2.3432
* Epoch: 39
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text2text-generation | speechbrain | # SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation
This repository provides all the necessary tools to perform English grapheme-to-phoneme conversion with a pretrained SoundChoice G2P model using SpeechBrain. It is trained on LibriG2P training data derived from [LibriSpeech Alignments](https://zeno... | {"language": "en", "license": "apache-2.0", "tags": ["G2P", "Grapheme-to-Phoneme", "speechbrain", "text2text-generation"], "datasets": ["Librispeech"], "metrics": ["Phone-Error-Rate"], "widget": [{"text": "English is tough. It can be understood through thorough thought though."}]} | speechbrain/soundchoice-g2p | null | [
"speechbrain",
"G2P",
"Grapheme-to-Phoneme",
"text2text-generation",
"en",
"dataset:Librispeech",
"arxiv:2106.04624",
"arxiv:2207.13703",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-27T00:56:13+00:00 | [
"2106.04624",
"2207.13703"
] | [
"en"
] | TAGS
#speechbrain #G2P #Grapheme-to-Phoneme #text2text-generation #en #dataset-Librispeech #arxiv-2106.04624 #arxiv-2207.13703 #license-apache-2.0 #has_space #region-us
| # SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation
This repository provides all the necessary tools to perform English grapheme-to-phoneme conversion with a pretrained SoundChoice G2P model using SpeechBrain. It is trained on LibriG2P training data derived from LibriSpeech Alignments and Google Wik... | [
"# SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation\n\nThis repository provides all the necessary tools to perform English grapheme-to-phoneme conversion with a pretrained SoundChoice G2P model using SpeechBrain. It is trained on LibriG2P training data derived from LibriSpeech Alignments and Goo... | [
"TAGS\n#speechbrain #G2P #Grapheme-to-Phoneme #text2text-generation #en #dataset-Librispeech #arxiv-2106.04624 #arxiv-2207.13703 #license-apache-2.0 #has_space #region-us \n",
"# SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation\n\nThis repository provides all the necessary tools to perform Eng... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | rwang5688/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T01:31:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7166
* Matthews Correlation: 0.5422
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-marktextepoch_35
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-marktextepoch_35", "results": []}]} | leokai/distilroberta-base-finetuned-marktextepoch_35 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T01:56:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-marktextepoch\_35
==============================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0029
Model description
-----------------
More information needed
Intend... | [
"### 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: 35",
"### Trainin... | [
"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: ... |
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. -->
# roberta-base-spanish-squades-becasIncentivos1
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-becasIncentivos1", "results": []}]} | Evelyn18/roberta-base-spanish-squades-becasIncentivos1 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T02:00:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-becasIncentivos1
=============================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1943
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 11\n* eval\\_batch\\_size: 11\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 11\n* eval\\_bat... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Lovesaif/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Lovesaif/bert-finetuned-squad", "results": []}]} | Lovesaif/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T02:19:59+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Lovesaif/bert-finetuned-squad
=============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5635
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 16638, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
automatic-speech-recognition | transformers | Model page for OIL_YT_fb_all
For further details please see Zara Maxwell-Smith and Ben Foley, (forthcoming), Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning, Field Matters Workshop, EACL 2023
How to cite this model.
Please use the following .bib to reference thi... | {"language": ["id", "en"], "license": "cc-by-nc-sa-4.0", "tags": ["wav2vec2"]} | ZMaxwell-Smith/OIL_YT_fb_all | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"id",
"en",
"doi:10.57967/hf/0491",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T02:27:06+00:00 | [] | [
"id",
"en"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #id #en #doi-10.57967/hf/0491 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| Model page for OIL_YT_fb_all
For further details please see Zara Maxwell-Smith and Ben Foley, (forthcoming), Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning, Field Matters Workshop, EACL 2023
How to cite this model.
Please use the following .bib to reference thi... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #id #en #doi-10.57967/hf/0491 #license-cc-by-nc-sa-4.0 #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="RajSang/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": "7.48 +/... | RajSang/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-27T02:38:05+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"
] |
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. -->
# roberta-base-spanish-squades-becasIncentivos2
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-becasIncentivos2", "results": []}]} | Evelyn18/roberta-base-spanish-squades-becasIncentivos2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-27T02:53:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #has_space #region-us
| roberta-base-spanish-squades-becasIncentivos2
=============================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.793
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 10\n*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# OMARS200/primer_modelo_hub
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "OMARS200/primer_modelo_hub", "results": []}]} | OMARS200/primer_modelo_hub | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T03:03:19+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| OMARS200/primer\_modelo\_hub
============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0892
* Validation Loss: 0.6573
* Epoch: 1
Model description
-----------------
More information needed
... | [
"### 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 #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_r... |
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-finelytuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finelytuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emoti... | NSUniversity/finelytuned-emotions | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T03:06:56+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-finelytuned-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: 0.2262
* Accuracy: 0.923
* F1: 0.9228
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# roberta-base-spanish-squades-becasIncentivos3
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-becasIncentivos3", "results": []}]} | Evelyn18/roberta-base-spanish-squades-becasIncentivos3 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T03:14:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-becasIncentivos3
=============================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7701
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-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: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* eval\\_batc... |
text-classification | transformers |
# Model Details
* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
* BERT based Text Classification.
# Intended Use
* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
... | {"license": "apache-2.0"} | vikaskapur/sentimental | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T03:41:28+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Details
* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
* BERT based Text Classification.
# Intended Use
* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
... | [
"# Model Details\n* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.\n* BERT based Text Classification.",
"# Intended Use\n* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review... | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Details\n* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.\n* BERT based Text Classification.",
... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-eurosat
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagef... | rwang5688/vit-base-patch16-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T03:41:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-patch16-224-finetuned-eurosat
======================================
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0469
* Accuracy: 0.9856
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
automatic-speech-recognition | transformers | Model page for OIL_YT_ind_nlp_all
For further details please see Zara Maxwell-Smith and Ben Foley, (forthcoming), Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning, Field Matters Workshop, EACL 2023
How to cite this model.
Please use the following .bib to referenc... | {"language": ["id", "en"], "license": "cc-by-nc-sa-4.0", "tags": ["wav2vec2"]} | ZMaxwell-Smith/OIL_YT_ind_nlp_all | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"id",
"en",
"doi:10.57967/hf/0492",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T04:21:51+00:00 | [] | [
"id",
"en"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #id #en #doi-10.57967/hf/0492 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| Model page for OIL_YT_ind_nlp_all
For further details please see Zara Maxwell-Smith and Ben Foley, (forthcoming), Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning, Field Matters Workshop, EACL 2023
How to cite this model.
Please use the following .bib to referenc... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #id #en #doi-10.57967/hf/0492 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n"
] |
image-classification | transformers |
# pond_image_classification_2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/pond_image_classification_8 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T04:25:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pond_image_classification_2
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Algae
!Algae
#### Boiling
!Boiling
#### BoilingNight
!BoilingNight
#### Normal
!Normal... | [
"# pond_image_classification_2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Algae\n\n!Algae",
"#### Boiling\n\n!Boiling",
"#### BoilingNight\n\n!Boi... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pond_image_classification_2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
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. -->
# layoutlmv2-finetuned-SLR-test
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/m... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-SLR-test", "results": []}]} | sheikh/layoutlmv2-finetuned-SLR-test | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T04:47:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2-finetuned-SLR-test
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# layoutlmv2-finetuned-SLR-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased 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",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2-finetuned-SLR-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown datase... |
null | transformers | # Erlangshen-ZEN2-345M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLU任务，使用了N-gram编码增强文本语义，3.45亿参数量的ZEN2
ZEN2 model, which uses N-gram to enhance text semantic and has 345M parameters, is adep... | {"language": ["zh"], "license": "apache-2.0", "tags": ["ZEN", "chinese"], "inference": false} | IDEA-CCNL/Erlangshen-ZEN2-345M-Chinese | null | [
"transformers",
"pytorch",
"ZEN",
"chinese",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"region:us"
] | null | 2022-07-27T05:13:11+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #ZEN #chinese #zh #arxiv-2209.02970 #license-apache-2.0 #region-us
| Erlangshen-ZEN2-345M-Chinese
============================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理NLU任务，使用了N-gram编码增强文本语义，3.45亿参数量的ZEN2
ZEN2 model, which uses N-gram to enhance text semantic and has 345M parameters, is adept at NLU tasks.
模型分类 Model T... | [
"### 下游效果 Performance\n\n\n分类任务 Classification\n\n\n\n抽取任务 Extraction\n\n\n\n使用 Usage\n--------\n\n\n因为transformers库中是没有ZEN2相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。\n\n\nSince there is no structure of ZEN2 in transformers library, you can find the structure of ZEN2 and run the codes in Fengshenbang-LM.\n\n\n你可以从... | [
"TAGS\n#transformers #pytorch #ZEN #chinese #zh #arxiv-2209.02970 #license-apache-2.0 #region-us \n",
"### 下游效果 Performance\n\n\n分类任务 Classification\n\n\n\n抽取任务 Extraction\n\n\n\n使用 Usage\n--------\n\n\n因为transformers库中是没有ZEN2相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。\n\n\nSince there is no structure of ZEN2 in ... |
null | null |
# 3 Label Ventricular Segmentation
This network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although much of the training data is composed of ... | {"tags": ["MONAI"]} | NimaBoscarino/ventricular_short_axis_3label | null | [
"MONAI",
"region:us"
] | null | 2022-07-27T05:26:22+00:00 | [] | [] | TAGS
#MONAI #region-us
|
# 3 Label Ventricular Segmentation
This network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although much of the training data is composed of ... | [
"# 3 Label Ventricular Segmentation\n\nThis network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although much of the training data is compos... | [
"TAGS\n#MONAI #region-us \n",
"# 3 Label Ventricular Segmentation\n\nThis network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although muc... |
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": []}]} | wooihen/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T05:57:57+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*... |
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. -->
# xsum_headline_generator_depreciated
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/goo... | {"tags": ["generated_from_trainer"], "pipeline_tag": "summarization", "model-index": [{"name": "xsum_headline_generator_depreciated", "results": []}]} | valurank/xsum_headline_generator | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"summarization",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T06:29:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #summarization #autotrain_compatible #endpoints_compatible #region-us
| xsum\_headline\_generator\_depreciated
======================================
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: 0.3521
Model description
-----------------
More information needed
Intended use... | [
"### 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 #summarization #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\\_... |
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/1234927574809182209/TTjR... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/lookinmyeyesboy-mcstoryfeed-mono93646057 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T07:10:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
MCStoryBot & Look Into My Eyes Boy & 𝐓𝐡𝐞 𝐌𝐞𝐠𝐚𝐥𝐢𝐭𝐡
@lookinmyeyesboy-mcstoryfeed-mono93646057
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 understan... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tst-translation
This model is a fine-tuned version of [anzorq/kbd_lat-835k_ru-3M_t5-small](https://huggingface.co/anzorq/kbd_lat... | {"language": ["ru", "kbd"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["anzorq/kbd_lat-ru"], "metrics": ["bleu"], "widget": [{"text": "ru->kbd: \u042f \u0438\u0434\u0443 \u0434\u043e\u043c\u043e\u0439.", "example_title": "\u042f \u0438\u0434\u0443 \u0434\u043e\u043c\u043e\u0439."}, {"text": "ru-... | anzorq/ru-kbd_lat-t5-small | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"ru",
"kbd",
"dataset:anzorq/kbd_lat-ru",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T07:27:57+00:00 | [] | [
"ru",
"kbd"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #generated_from_trainer #ru #kbd #dataset-anzorq/kbd_lat-ru #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# tst-translation
This model is a fine-tuned version of anzorq/kbd_lat-835k_ru-3M_t5-small on the anzorq/kbd_lat-ru anzorq--kbd-ru dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6000
- Bleu: 12.649
- Gen Len: 11.8018
## Model description
More information needed
## Intended uses & lim... | [
"# tst-translation\n\nThis model is a fine-tuned version of anzorq/kbd_lat-835k_ru-3M_t5-small on the anzorq/kbd_lat-ru anzorq--kbd-ru dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.6000\n- Bleu: 12.649\n- Gen Len: 11.8018",
"## Model description\n\nMore information needed",
"## I... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #generated_from_trainer #ru #kbd #dataset-anzorq/kbd_lat-ru #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# tst-translation\n\nThis model is a fine-tuned version of anzorq/kbd_l... |
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... | suvadityamuk/ppo-LunarLander-v2-practicecourse-1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T07:28:57+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... |
translation | transformers |
# Model Card for T5 Large
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3.... | {"language": ["en", "fa"], "license": "apache-2.0", "tags": ["translation"]} | Kamrani/t5-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"en",
"fa",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compati... | null | 2022-07-27T07:35:34+00:00 | [
"1805.12471",
"1708.00055",
"1704.05426",
"1606.05250",
"1808.09121",
"1810.12885",
"1905.10044",
"1910.09700"
] | [
"en",
"fa"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #en #fa #arxiv-1805.12471 #arxiv-1708.00055 #arxiv-1704.05426 #arxiv-1606.05250 #arxiv-1808.09121 #arxiv-1810.12885 #arxiv-1905.10044 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us... |
# Model Card for T5 Large
!model image
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Environmental Impact
7. Citation
8. Model Card Authors
9. How To Get Started With the Model
# Model Details
## Model Description
The developers of the Text-To-... | [
"# Model Card for T5 Large\n\n!model image",
"# Table of Contents\n\n1. Model Details\n2. Uses\n3. Bias, Risks, and Limitations\n4. Training Details\n5. Evaluation\n6. Environmental Impact\n7. Citation\n8. Model Card Authors\n9. How To Get Started With the Model",
"# Model Details",
"## Model Description\n\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #translation #en #fa #arxiv-1805.12471 #arxiv-1708.00055 #arxiv-1704.05426 #arxiv-1606.05250 #arxiv-1808.09121 #arxiv-1810.12885 #arxiv-1905.10044 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #reg... |
text-generation | transformers |
# a | {"tags": ["conversational"]} | trickstters/DialoGPT-small-evanbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T07:52:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# a | [
"# a"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# a"
] |
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-ft750_reg1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft750_reg1", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft750_reg1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T08:10:39+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-ft750\_reg1
=============================================
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: 0.9304
Model description
-----------------
More information needed
I... | [
"### 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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# textgenerator
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the followi... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "textgenerator", "results": []}]} | srivatsavaasista/textgenerator | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T08:12:36+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| textgenerator
=============
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 6.4579
* Validation Loss: 6.4893
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay... |
text2text-generation | transformers |
# Mengzi-T5-MT model
This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on [Mengzi-T5-base](https://huggingface.co/Langboat/mengzi-t5-base).
[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
## Usage
```python
f... | {"language": ["zh"], "license": "apache-2.0", "widget": [{"text": "\u201c\u623f\u95f4\u5f88\u4e00\u822c\uff0c\u5c0f\uff0c\u4e14\u8ba9\u4eba\u611f\u89c9\u810f\uff0c\u9694\u97f3\u6548\u679c\u5dee\uff0c\u80fd\u542c\u5230\u8d70\u5eca\u7684\u4eba\u8bb2\u8bdd\uff0c\u8d70\u5eca\u5149\u7ebf\u660f\u6697\uff0c\u65c1\u8fb9\u6ca1\... | Langboat/mengzi-t5-base-mt | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"zh",
"arxiv:2110.06696",
"doi:10.57967/hf/0026",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T08:13:27+00:00 | [
"2110.06696"
] | [
"zh"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #zh #arxiv-2110.06696 #doi-10.57967/hf/0026 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Mengzi-T5-MT model
This is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on Mengzi-T5-base.
Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
## Usage
If you find the technical report or resource is useful, please cite the following technical rep... | [
"# Mengzi-T5-MT model\nThis is a Multi-Task model trained on the multitask mixture of 27 datasets and 301 prompts, based on Mengzi-T5-base.\n\nMengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese",
"## Usage\n\n\nIf you find the technical report or resource is useful, please cite the following... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #zh #arxiv-2110.06696 #doi-10.57967/hf/0026 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Mengzi-T5-MT model\nThis is a Multi-Task model trained on the multitask mixture of 27 dataset... |
null | transformers | # Erlangshen-ZEN2-668M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLU任务，使用了N-gram编码增强文本语义，6.68亿参数量的ZEN2
ZEN2 model, which uses N-gram to enhance text semantic and has 668M parameters, is adept... | {"language": ["zh"], "license": "apache-2.0", "tags": ["ZEN", "chinese"], "inference": false} | IDEA-CCNL/Erlangshen-ZEN2-668M-Chinese | null | [
"transformers",
"pytorch",
"ZEN",
"chinese",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"region:us"
] | null | 2022-07-27T08:28:55+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #ZEN #chinese #zh #arxiv-2209.02970 #license-apache-2.0 #region-us
| Erlangshen-ZEN2-668M-Chinese
============================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理NLU任务，使用了N-gram编码增强文本语义，6.68亿参数量的ZEN2
ZEN2 model, which uses N-gram to enhance text semantic and has 668M parameters, is adept at NLU tasks.
模型分类 Model T... | [
"### 下游效果 Performance\n\n\n分类任务 Classification\n\n\n\n抽取任务 Extraction\n\n\n\n使用 Usage\n--------\n\n\n因为transformers库中是没有ZEN2相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。\n\n\nSince there is no structure of ZEN2 in transformers library, you can find the structure of ZEN2 and run the codes in Fengshenbang-LM.\n\n\n你可以从... | [
"TAGS\n#transformers #pytorch #ZEN #chinese #zh #arxiv-2209.02970 #license-apache-2.0 #region-us \n",
"### 下游效果 Performance\n\n\n分类任务 Classification\n\n\n\n抽取任务 Extraction\n\n\n\n使用 Usage\n--------\n\n\n因为transformers库中是没有ZEN2相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。\n\n\nSince there is no structure of ZEN2 in ... |
image-classification | transformers |
A model trained on the beans dataset, just for testing and having a really tiny model.
| {"license": "apache-2.0"} | fxmarty/resnet-tiny-beans | null | [
"transformers",
"pytorch",
"onnx",
"resnet",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-27T08:51:50+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #resnet #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
A model trained on the beans dataset, just for testing and having a really tiny model.
| [] | [
"TAGS\n#transformers #pytorch #onnx #resnet #image-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-panjabi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-panjabi-colab", "results": []}]} | AlphaNinja27/wav2vec2-large-xls-r-300m-panjabi-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T09:03:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-panjabi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# wav2vec2-large-xls-r-300m-panjabi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-panjabi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_... |
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/1538409928943083526/gilL... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jordo4today-paddedpossum-wrenfing/1658916978297/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jordo4today-paddedpossum-wrenfing | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T09:15:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Mr. Wolf Simp & Zoinks & Jordo MFF
@jordo4today-paddedpossum-wrenfing
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, ch... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | pannaga/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T09:22:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9701
* Wer: 1.0
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
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. -->
# 20split_dataset_version3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "20split_dataset_version3", "results": []}]} | Billwzl/20split_dataset_version3 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T10:21:44+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| 20split\_dataset\_version3
==========================
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: 2.8310
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: 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: 16",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval... |
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="jianzhnie/q_FrozenLake_v1_4x4_noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=Fals... | {"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": ... | jianzhnie/q_FrozenLake_v1_4x4_noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-27T10:49:50+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\nThis is a trained model of a Q-Learning agent playing FrozenLake-v1.",
"## 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\nThis is a trained model of a Q-Learning agent playing FrozenLake-v1.",
"## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft780_class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft780_class", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft780_class | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T10:55:12+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-ft780\_class
==============================================
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: 1.9843
* Accuracy: 0.2047
* F1: 0.1823
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# reformer-big_patent-wikipedia-arxiv-16384
This model is a fine-tuned version of [robingeibel/reformer-big_patent-wikipedia-arxiv... | {"tags": ["generated_from_trainer"], "datasets": ["big_patent"], "model-index": [{"name": "reformer-big_patent-wikipedia-arxiv-16384", "results": []}]} | robingeibel/reformer-big_patent-wikipedia-arxiv-16384 | null | [
"transformers",
"pytorch",
"tensorboard",
"reformer",
"fill-mask",
"generated_from_trainer",
"dataset:big_patent",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T11:05:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #reformer #fill-mask #generated_from_trainer #dataset-big_patent #autotrain_compatible #endpoints_compatible #region-us
| reformer-big\_patent-wikipedia-arxiv-16384
==========================================
This model is a fine-tuned version of robingeibel/reformer-big\_patent-wikipedia-arxiv-16384 on the big\_patent dataset.
It achieves the following results on the evaluation set:
* Loss: 5.8649
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-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* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #reformer #fill-mask #generated_from_trainer #dataset-big_patent #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-06\n* train\\_batch\\_siz... |
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-base-ibn-Shaddad-v6
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It... | {"license": "apache-2.0", "tags": ["Poet", "generated_from_trainer"], "model-index": [{"name": "t5-base-ibn-Shaddad-v6", "results": []}]} | Ahmed007/t5-base-ibn-Shaddad-v6 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"Poet",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T11:09:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #Poet #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-ibn-Shaddad-v6
======================
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.2957
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: 5.6e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #Poet #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
automatic-speech-recognition | nemo | # NVIDIA Streaming Citrinet 1024 (uk)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Langu... | {"language": ["uk"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva"], "datasets": ["mozilla-foundation/common_voice_10_0"], "model-index": [{"name": "stt_uk_citrinet_1024_gamma_... | nvidia/stt_uk_citrinet_1024_gamma_0_25 | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Citrinet",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"Riva",
"uk",
"dataset:mozilla-foundation/common_voice_10_0",
"arxiv:2104.01721",
"license:cc-by-4.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-07-27T11:37:21+00:00 | [
"2104.01721"
] | [
"uk"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #uk #dataset-mozilla-foundation/common_voice_10_0 #arxiv-2104.01721 #license-cc-by-4.0 #model-index #has_space #region-us
| NVIDIA Streaming Citrinet 1024 (uk)
===================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lowercase Ukrainian alp... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample.\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 kHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speec... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #uk #dataset-mozilla-foundation/common_voice_10_0 #arxiv-2104.01721 #license-cc-by-4.0 #model-index #has_space #region-us \n",
"### Automatically instantiate the model",
"### Transcribi... |
text2text-generation | transformers |
Bart + Gartner = Bartner | {"license": "mit"} | diwank/bartner | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T11:56:32+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Bart + Gartner = Bartner | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | masterdezign/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-27T12:13:39+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 ... |
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-synthetic-paraphrase-only
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "deberta-v3-large-finetuned-synthetic-paraphrase-only", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-synthetic-paraphrase-only | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T12:31:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-finetuned-synthetic-paraphrase-only
====================================================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0120
* F1: 0.9768
* Precision: 0.9961
* Recall: 0.9583
... | [
"### 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 #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: 6e-06\n* train\\_batch\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-wikiann-hi
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-wikiann-hi", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "ty... | Someman/xlm-roberta-base-finetuned-wikiann-hi | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T12:32:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-wikiann #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-wikiann-hi
=====================================
This model is a fine-tuned version of xlm-roberta-base on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3097
* F1: 1.0
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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-wikiann #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used durin... |
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="suvadityamuk/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | suvadityamuk/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-27T13:16:57+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="suvadityamuk/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False et... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/... | suvadityamuk/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-27T13:25:17+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-generation | transformers |
# bot | {"tags": ["conversational"]} | trickstters/evanbot-gpt | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T13:41:58+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #region-us
|
# bot | [
"# bot"
] | [
"TAGS\n#transformers #pytorch #conversational #endpoints_compatible #region-us \n",
"# bot"
] |
reinforcement-learning | stable-baselines3 |
# **SAC** Agent playing **seals/Walker2d-v0**
This is a trained model of a **SAC** agent playing **seals/Walker2d-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselin... | {"library_name": "stable-baselines3", "tags": ["seals/Walker2d-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "SAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Walker2d-v0", "type": "... | HumanCompatibleAI/sac-seals-Walker2d-v0 | null | [
"stable-baselines3",
"seals/Walker2d-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T14:06:52+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Walker2d-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# SAC Agent playing seals/Walker2d-v0
This is a trained model of a SAC agent playing seals/Walker2d-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage... | [
"# SAC Agent playing seals/Walker2d-v0\nThis is a trained model of a SAC agent playing seals/Walker2d-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included... | [
"TAGS\n#stable-baselines3 #seals/Walker2d-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# SAC Agent playing seals/Walker2d-v0\nThis is a trained model of a SAC agent playing seals/Walker2d-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training ... |
reinforcement-learning | stable-baselines3 |
# **SAC** Agent playing **seals/Hopper-v0**
This is a trained model of a **SAC** agent playing **seals/Hopper-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
... | {"library_name": "stable-baselines3", "tags": ["seals/Hopper-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "SAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Hopper-v0", "type": "seal... | HumanCompatibleAI/sac-seals-Hopper-v0 | null | [
"stable-baselines3",
"seals/Hopper-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T14:08:02+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Hopper-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# SAC Agent playing seals/Hopper-v0
This is a trained model of a SAC agent playing seals/Hopper-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (wi... | [
"# SAC Agent playing seals/Hopper-v0\nThis is a trained model of a SAC agent playing seals/Hopper-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
... | [
"TAGS\n#stable-baselines3 #seals/Hopper-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# SAC Agent playing seals/Hopper-v0\nThis is a trained model of a SAC agent playing seals/Hopper-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framew... |
reinforcement-learning | stable-baselines3 |
# **SAC** Agent playing **seals/HalfCheetah-v0**
This is a trained model of a **SAC** agent playing **seals/HalfCheetah-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable B... | {"library_name": "stable-baselines3", "tags": ["seals/HalfCheetah-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "SAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/HalfCheetah-v0", "ty... | HumanCompatibleAI/sac-seals-HalfCheetah-v0 | null | [
"stable-baselines3",
"seals/HalfCheetah-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T14:08:43+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/HalfCheetah-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# SAC Agent playing seals/HalfCheetah-v0
This is a trained model of a SAC agent playing seals/HalfCheetah-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
##... | [
"# SAC Agent playing seals/HalfCheetah-v0\nThis is a trained model of a SAC agent playing seals/HalfCheetah-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents in... | [
"TAGS\n#stable-baselines3 #seals/HalfCheetah-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# SAC Agent playing seals/HalfCheetah-v0\nThis is a trained model of a SAC agent playing seals/HalfCheetah-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a ... |
reinforcement-learning | stable-baselines3 |
# **SAC** Agent playing **seals/Ant-v0**
This is a trained model of a **SAC** agent playing **seals/Ant-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinfo... | {"library_name": "stable-baselines3", "tags": ["seals/Ant-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "SAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Ant-v0", "type": "seals/Ant-... | HumanCompatibleAI/sac-seals-Ant-v0 | null | [
"stable-baselines3",
"seals/Ant-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T14:10:06+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Ant-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# SAC Agent playing seals/Ant-v0
This is a trained model of a SAC agent playing seals/Ant-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3... | [
"# SAC Agent playing seals/Ant-v0\nThis is a trained model of a SAC agent playing seals/Ant-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## ... | [
"TAGS\n#stable-baselines3 #seals/Ant-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# SAC Agent playing seals/Ant-v0\nThis is a trained model of a SAC agent playing seals/Ant-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for S... |
reinforcement-learning | stable-baselines3 |
# **SAC** Agent playing **seals/Humanoid-v0**
This is a trained model of a **SAC** agent playing **seals/Humanoid-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselin... | {"library_name": "stable-baselines3", "tags": ["seals/Humanoid-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "SAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Humanoid-v0", "type": "... | HumanCompatibleAI/sac-seals-Humanoid-v0 | null | [
"stable-baselines3",
"seals/Humanoid-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T14:12:08+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Humanoid-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# SAC Agent playing seals/Humanoid-v0
This is a trained model of a SAC agent playing seals/Humanoid-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage... | [
"# SAC Agent playing seals/Humanoid-v0\nThis is a trained model of a SAC agent playing seals/Humanoid-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included... | [
"TAGS\n#stable-baselines3 #seals/Humanoid-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# SAC Agent playing seals/Humanoid-v0\nThis is a trained model of a SAC agent playing seals/Humanoid-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training ... |
reinforcement-learning | stable-baselines3 |
# **SAC** Agent playing **seals/Swimmer-v0**
This is a trained model of a **SAC** agent playing **seals/Swimmer-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines... | {"library_name": "stable-baselines3", "tags": ["seals/Swimmer-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "SAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Swimmer-v0", "type": "se... | HumanCompatibleAI/sac-seals-Swimmer-v0 | null | [
"stable-baselines3",
"seals/Swimmer-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T14:12:31+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/Swimmer-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# SAC Agent playing seals/Swimmer-v0
This is a trained model of a SAC agent playing seals/Swimmer-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (... | [
"# SAC Agent playing seals/Swimmer-v0\nThis is a trained model of a SAC agent playing seals/Swimmer-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included."... | [
"TAGS\n#stable-baselines3 #seals/Swimmer-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# SAC Agent playing seals/Swimmer-v0\nThis is a trained model of a SAC agent playing seals/Swimmer-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training fra... |
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/1544346507578589184/x9UR... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/interiordesign/1658935819881/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/interiordesign | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T14:21:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Interior Design
@interiordesign
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# roberta-base-spanish-squades-becasIncentivos4
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-becasIncentivos4", "results": []}]} | Evelyn18/roberta-base-spanish-squades-becasIncentivos4 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T14:56:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us
| roberta-base-spanish-squades-becasIncentivos4
=============================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7734
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 6\n* eval\\_batc... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_Mod_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "BERT_Mod_1", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"... | Go2Heart/BERT_Mod_1 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T15:07:33+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| BERT\_Mod\_1
============
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1787
* Matthews Correlation: 0.5419
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# En-Af_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-af](https://huggingface.co/Helsinki-NLP/opus-mt-en-a... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Af_update", "results": []}]} | kabelomalapane/En-Af_update | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T15:11:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| En-Af\_update
=============
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-af on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8089
* Bleu: 45.1780
Model description
-----------------
More information needed
Intended uses & limitations
--------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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 #tensorboard #marian #text2text-generation #translation #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*... |
fill-mask | transformers | ClinicBERT has the same architecture of RoBERTa model. It has been trained on clinical text and can be used for feature extraction from textual data.
## How to use
### Feature Extraction
```
from transformers import RobertaModel, RobertaTokenizer
model = RobertaModel.from_pretrained("tdobrxl/ClinicBERT")
tokenizer =... | {} | tdobrxl/ClinicBERT | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T15:18:35+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ClinicBERT has the same architecture of RoBERTa model. It has been trained on clinical text and can be used for feature extraction from textual data.
## How to use
### Feature Extraction
### Masked Word Prediction
| [
"## How to use",
"### Feature Extraction",
"### Masked Word Prediction"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## How to use",
"### Feature Extraction",
"### Masked Word Prediction"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "m... | mariastull/Reinforce-1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-27T15:29:03+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
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... | heriosousa/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T16:02:08+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 ... |
null | null |
# ICON Clothed Human Digitization
### ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)
<table>
<th>
<ul>
<li><strong>Homepage</strong> <a href="http://icon.is.tue.mpg.de">icon.is.tue.mpg.de</a></li>
<li><strong>Code</strong> <a href="https://github.com/YuliangXiu/ICON">YuliangXiu/ICON</a></li>
<li><st... | {"title": "ICON - Clothed Human Digitization", "metaTitle": "Making yourself an ICON, by Yuliang Xiu", "emoji": "\ud83e\udd3c", "colorFrom": "indigo", "colorTo": "yellow", "sdk": "gradio", "sdk_version": "3.27.0", "app_file": "app.py", "pinned": true, "python_version": "3.8.13"} | Yuliang/ICON | null | [
"arxiv:2112.09127",
"doi:10.57967/hf/0021",
"has_space",
"region:us"
] | null | 2022-07-27T16:23:34+00:00 | [
"2112.09127"
] | [] | TAGS
#arxiv-2112.09127 #doi-10.57967/hf/0021 #has_space #region-us
| ICON Clothed Human Digitization
===============================
### ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)
| [
"### ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)"
] | [
"TAGS\n#arxiv-2112.09127 #doi-10.57967/hf/0021 #has_space #region-us \n",
"### ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)"
] |
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... | jaybeeja/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T16:25:38+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... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-korean-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-korean-demo-colab", "results": []}]} | jungjongho/wav2vec2-large-xlsr-korean-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T16:26:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-korean-demo-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4534
* Wer: 0.3272
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mal-tls-bert-large-w8a8
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal-tls-bert-large-w8a8", "results": []}]} | SharpAI/mal-tls-bert-large-w8a8 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T16:48:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal-tls-bert-large-w8a8
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Trainin... | [
"# mal-tls-bert-large-w8a8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informat... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal-tls-bert-large-w8a8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model d... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "co... | curtsmith/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T17:30:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8123
* Matthews Correlation: 0.5364
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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