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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-base
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following r... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal_tls-bert-base", "results": []}]} | SharpAI/mal-tls-bert-base | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T18:09:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal_tls-bert-base
This model is a fine-tuned version of [](URL 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
## Trai... | [
"# mal_tls-bert-base\n\nThis model is a fine-tuned version of [](URL 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 infor... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal_tls-bert-base\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mode... |
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. -->
# mini_model
This model is a fine-tuned version of [nreimers/BERT-Mini_L-4_H-256_A-4](https://huggingface.co/nreimers/BERT-Mini_L-... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "mini_model", "results": []}]} | srcocotero/mini-bert-qa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T18:12:01+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
# mini_model
This model is a fine-tuned version of nreimers/BERT-Mini_L-4_H-256_A-4 on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# mini_model\n\nThis model is a fine-tuned version of nreimers/BERT-Mini_L-4_H-256_A-4 on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"# mini_model\n\nThis model is a fine-tuned version of nreimers/BERT-Mini_L-4_H-256_A-4 on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended u... |
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. -->
# mini_model
This model is a fine-tuned version of [nreimers/BERT-Tiny_L-2_H-128_A-2](https://huggingface.co/nreimers/BERT-Tiny_L-... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "mini_model", "results": []}]} | srcocotero/tiny-bert-qa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T18:12:14+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
# mini_model
This model is a fine-tuned version of nreimers/BERT-Tiny_L-2_H-128_A-2 on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# mini_model\n\nThis model is a fine-tuned version of nreimers/BERT-Tiny_L-2_H-128_A-2 on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"# mini_model\n\nThis model is a fine-tuned version of nreimers/BERT-Tiny_L-2_H-128_A-2 on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended u... |
text-generation | transformers |
# GPT-2 Large
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-im... | {"language": "en", "license": "mit", "tags": ["conversational"]} | AriakimTaiyo/gpt2-chat | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"conversational",
"en",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T18:15:28+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #gpt2 #text-generation #conversational #en #arxiv-1910.09700 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GPT-2 Large
===========
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-------------
Model Descripti... | [
"#### Direct Use\n\n\nIn their model card about GPT-2, OpenAI wrote:\n\n\n\n> \n> The primary intended users of these models are AI researchers and practitioners.\n> \n> \n> We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constrain... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #gpt2 #text-generation #conversational #en #arxiv-1910.09700 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### Direct Use\n\n\nIn their model card about GPT-2, OpenAI wrote:\n\n\n\n> \n> The primary int... |
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-2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "m... | mariastull/Reinforce-2 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-27T18:16:19+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 ... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | SGme/pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-27T18:32:19+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
automatic-speech-recognition | transformers |
# IndicWav2Vec-Hindi
This is a [Wav2Vec2](https://arxiv.org/abs/2006.11477) style ASR model trained in [fairseq](https://github.com/facebookresearch/fairseq) and ported to Hugging Face.
More details on datasets, training-setup and conversion to HuggingFace format can be found in the [IndicWav2Vec](https://github.com... | {"language": "hi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "wav2vec2", "asr"], "metrics": ["wer", "cer"]} | ai4bharat/indicwav2vec-hindi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"asr",
"hi",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-27T18:43:11+00:00 | [
"2006.11477"
] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #asr #hi #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# IndicWav2Vec-Hindi
This is a Wav2Vec2 style ASR model trained in fairseq and ported to Hugging Face.
More details on datasets, training-setup and conversion to HuggingFace format can be found in the IndicWav2Vec repo.
*Note: This model doesn't support inference with Language Model.*
## Script to Run Inference
... | [
"# IndicWav2Vec-Hindi\n\nThis is a Wav2Vec2 style ASR model trained in fairseq and ported to Hugging Face. \nMore details on datasets, training-setup and conversion to HuggingFace format can be found in the IndicWav2Vec repo. \n*Note: This model doesn't support inference with Language Model.*",
"## Script to Run... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #asr #hi #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# IndicWav2Vec-Hindi\n\nThis is a Wav2Vec2 style ASR model trained in fairseq and ported to Hugging Face. \nMore details on datasets... |
null | null |
# Graphcore/wav2vec2-ctc-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gr... | {"license": "apache-2.0"} | Graphcore/wav2vec2-ctc-base-ipu | null | [
"optimum_graphcore",
"arxiv:2006.11477",
"license:apache-2.0",
"region:us"
] | null | 2022-07-27T18:56:31+00:00 | [
"2006.11477"
] | [] | TAGS
#optimum_graphcore #arxiv-2006.11477 #license-apache-2.0 #region-us
|
# Graphcore/wav2vec2-ctc-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gr... | [
"# Graphcore/wav2vec2-ctc-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models... | [
"TAGS\n#optimum_graphcore #arxiv-2006.11477 #license-apache-2.0 #region-us \n",
"# Graphcore/wav2vec2-ctc-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of ... |
null | null | import requests
API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
headers = {"Authorization": f"Bearer {API_TOKEN}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json() | {"license": "cc-by-4.0"} | unclearsoup/creative | null | [
"license:cc-by-4.0",
"region:us"
] | null | 2022-07-27T18:58:27+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
| import requests
API_URL = "URL
headers = {"Authorization": f"Bearer {API_TOKEN}"}
def query(payload):
response = URL(API_URL, headers=headers, json=payload)
return URL() | [] | [
"TAGS\n#license-cc-by-4.0 #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | cjdentra/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T19:18:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the emotion 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 #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the ... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Yank2901/DialoGPT-small-Harry | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T19:25:55+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text2text-generation | transformers |
# NLLB-200 1.3B fine-tuned on Ascendance of a Bookworm
This model was fine-tuned on Ascendance of a Bookworm to translate the web novel in Japanese to English. | {"language": ["en", "ja"], "license": "cc-by-nc-4.0", "tags": ["nllb"]} | thefrigidliquidation/nllb-200-distilled-1.3B-bookworm | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"nllb",
"en",
"ja",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T19:39:08+00:00 | [] | [
"en",
"ja"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #nllb #en #ja #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# NLLB-200 1.3B fine-tuned on Ascendance of a Bookworm
This model was fine-tuned on Ascendance of a Bookworm to translate the web novel in Japanese to English. | [
"# NLLB-200 1.3B fine-tuned on Ascendance of a Bookworm\n\nThis model was fine-tuned on Ascendance of a Bookworm to translate the web novel in Japanese to English."
] | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #nllb #en #ja #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# NLLB-200 1.3B fine-tuned on Ascendance of a Bookworm\n\nThis model was fine-tuned on Ascendance of a Bookworm to translate the web novel in Japanese to En... |
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. -->
# Af-En_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-af-en](https://huggingface.co/Helsinki-NLP/opus-mt-af-e... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Af-En_update", "results": []}]} | kabelomalapane/Af-En_update | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T19:53:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Af-En\_update
=============
This model is a fine-tuned version of Helsinki-NLP/opus-mt-af-en on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7197
* Bleu: 55.3346
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: 15",
"### 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*... |
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-base-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-base-w8a8", "results": []}]} | SharpAI/mal-tls-bert-base-w8a8 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T20:02:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal-tls-bert-base-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
## Training... | [
"# mal-tls-bert-base-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 informati... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal-tls-bert-base-w8a8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model de... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-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": ["Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{"type": "m... | mariastull/Reinforce-3 | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-27T20:39:47+00:00 | [] | [] | TAGS
#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pong-PLE-v0
This is a trained model of a Reinforce agent playing Pong-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-generation | transformers |
# Baymax DialoGPT Model | {"tags": ["conversational"]} | lizz27/DialoGPT-small-baymax | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T20:53:20+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Baymax DialoGPT Model | [
"# Baymax DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Baymax DialoGPT Model"
] |
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... | dbarbedillo/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-27T21:24:45+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 | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta... | {"library_name": "keras"} | akraut/CDS_BERT_CLF | null | [
"keras",
"region:us"
] | null | 2022-07-27T22:06:07+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
text-generation | transformers |
#Jolyne DialoGPT Model | {"tags": ["conversational"]} | obl1t/DialoGPT-medium-Jolyne | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T22:36:02+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Jolyne DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
audio-to-audio | fairseq | ## xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022
Speech-to-speech translation model from fairseq S2UT ([paper](https://arxiv.org/abs/2204.02967)/[code](https://github.com/facebookresearch/fairseq/blob/main/examples/speech_to_speech/docs/enhanced_direct_s2st_discrete_units.md)):
- Spanish-English
- Trained on mTEDx,... | {"library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["mtedx", "covost2", "europarl_st", "voxpopuli"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transformer_600m-es_en-mult... | facebook/xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022 | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:mtedx",
"dataset:covost2",
"dataset:europarl_st",
"dataset:voxpopuli",
"arxiv:2204.02967",
"has_space",
"region:us"
] | null | 2022-07-27T22:38:17+00:00 | [
"2204.02967"
] | [] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-mtedx #dataset-covost2 #dataset-europarl_st #dataset-voxpopuli #arxiv-2204.02967 #has_space #region-us
| ## xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022
Speech-to-speech translation model from fairseq S2UT (paper/code):
- Spanish-English
- Trained on mTEDx, CoVoST 2, Europarl-ST and VoxPopuli
- Speech synthesis with facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur
## Usage
| [
"## xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022\n\nSpeech-to-speech translation model from fairseq S2UT (paper/code):\n- Spanish-English\n- Trained on mTEDx, CoVoST 2, Europarl-ST and VoxPopuli\n- Speech synthesis with facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur",
"## Usage"
] | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-mtedx #dataset-covost2 #dataset-europarl_st #dataset-voxpopuli #arxiv-2204.02967 #has_space #region-us \n",
"## xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022\n\nSpeech-to-speech translation model from fairseq S2UT (paper/code):\n- Sp... |
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. -->
# vc-bantai-vit-withoutAMBI-adunest-trial
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vc-bantai-vit-withoutAMBI-adunest-trial", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "image... | AykeeSalazar/vc-bantai-vit-withoutAMBI-adunest-trial | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-27T23:29:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vc-bantai-vit-withoutAMBI-adunest-trial
=======================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4289
* Accuracy: 0.7798
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #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* learnin... |
text-generation | transformers |
# Josh DialoGPT Model | {"tags": ["conversational"]} | Jenwvwmabskvwh/DialoGPT-small-josh445 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T23:43:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Josh DialoGPT Model | [
"# Josh DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Josh DialoGPT Model"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# OMARS200/Traductor
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieve... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "OMARS200/Traductor", "results": []}]} | OMARS200/Traductor | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-27T23:52:51+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| OMARS200/Traductor
==================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.7234
* Validation Loss: 1.5128
* 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': 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 #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
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/1452082178741968901/oERk... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/penguinnnno/1658971968390/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/penguinnnno | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-28T00:07:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
penguino
@penguinnnno
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
<!-- 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. -->
# vc-bantai-vit-withoutAMBI-adunest-v1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vc-bantai-vit-withoutAMBI-adunest-v1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefol... | AykeeSalazar/vc-bantai-vit-withoutAMBI-adunest-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T00:15:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vc-bantai-vit-withoutAMBI-adunest-v1
====================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3318
* Accuracy: 0.9181
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\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: 200\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #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* learnin... |
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. -->
# finetuned-mt5-base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wmt16 ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "finetuned-mt5-base", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"}, "m... | Lvxue/finetuned-mt5-base | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-28T00:51:27+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# finetuned-mt5-base
This model is a fine-tuned version of google/mt5-base on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3594
- Bleu: 27.1659
- Gen Len: 43.9575
## Model description
More information needed
## Intended uses & limitations
More information needed
#... | [
"# finetuned-mt5-base\n\nThis model is a fine-tuned version of google/mt5-base on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.3594\n- Bleu: 27.1659\n- Gen Len: 43.9575",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore i... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# finetuned-mt5-base\n\nThis model is a fine-tuned version of google/mt5-base on the wmt1... |
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="jianzhnie/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | jianzhnie/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-28T00:57:36+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"
] |
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. -->
# finetuned-mt5-small
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "finetuned-mt5-small", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en"}, "... | Lvxue/finetuned-mt5-small | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-28T01:27:31+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# finetuned-mt5-small
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6328
- Bleu: 23.6759
- Gen Len: 43.6993
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# finetuned-mt5-small\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6328\n- Bleu: 23.6759\n- Gen Len: 43.6993",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# finetuned-mt5-small\n\nThis model is a fine-tuned version of google/mt5-small on the wm... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-cnn1_50k
This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn1_50k](https://huggingface.co/o... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-cnn1_50k", "results": []}]} | oMateos2020/pegasus-newsroom-cnn1_50k | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T02:07:03+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| pegasus-newsroom-cnn1\_50k
==========================
This model is a fine-tuned version of oMateos2020/pegasus-newsroom-cnn1\_50k on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1267
* Rouge1: 38.0081
* Rouge2: 16.5536
* Rougel: 26.4916
* Rougelsum: 35.1349
* Gen Len: 59.491... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_s... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | olpa/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T02:10:33+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7778
* Accuracy: 0.9168
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
null | transformers |
## Wav2Vec2-Large-XLSR-53 pretrained on Ainu language data
This is a [wav2vec-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model adapted for the Ainu language by performing continued pretraining for 100k steps on 234 hours of speech data in Hokkaido Ainu and Sakhalin Ainu.
For details, plea... | {"language": ["multilingual", "ain"], "license": "apache-2.0"} | karolnowakowski/wav2vec2-large-xlsr-53-pretrain-ain | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"multilingual",
"ain",
"arxiv:2301.07295",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T02:12:20+00:00 | [
"2301.07295"
] | [
"multilingual",
"ain"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #multilingual #ain #arxiv-2301.07295 #license-apache-2.0 #endpoints_compatible #region-us
|
## Wav2Vec2-Large-XLSR-53 pretrained on Ainu language data
This is a wav2vec-large-xlsr-53 model adapted for the Ainu language by performing continued pretraining for 100k steps on 234 hours of speech data in Hokkaido Ainu and Sakhalin Ainu.
For details, please refer to the paper.
A model fine-tuned for automatic t... | [
"## Wav2Vec2-Large-XLSR-53 pretrained on Ainu language data\n\nThis is a wav2vec-large-xlsr-53 model adapted for the Ainu language by performing continued pretraining for 100k steps on 234 hours of speech data in Hokkaido Ainu and Sakhalin Ainu.\nFor details, please refer to the paper.\n\nA model fine-tuned for au... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #multilingual #ain #arxiv-2301.07295 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Wav2Vec2-Large-XLSR-53 pretrained on Ainu language data\n\nThis is a wav2vec-large-xlsr-53 model adapted for the Ainu language by performing continued pretrainin... |
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-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | amartyobanerjee/distilbert-base-uncased-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 | 2022-07-28T04:27:01+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-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: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
"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... |
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. -->
# korean-aihub-learning-math-8batch
This model is a fine-tuned version of [kresnik/wav2vec2-large-xlsr-korean](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "korean-aihub-learning-math-8batch", "results": []}]} | jaeyeon/korean-aihub-learning-math-8batch | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T04:48:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| korean-aihub-learning-math-8batch
=================================
This model is a fine-tuned version of kresnik/wav2vec2-large-xlsr-korean on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1867
* Wer: 0.5315
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\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: 8... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | reachrkr/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-28T05:59:12+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
text-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-base-w1q8
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-base-w1q8", "results": []}]} | SharpAI/mal-tls-bert-base-w1q8 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T06:03:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal_tls-bert-base-w1q8
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
## Training... | [
"# mal_tls-bert-base-w1q8\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 informati... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal_tls-bert-base-w1q8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model de... |
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. -->
# korean-aihub-learning-math-16batch
This model is a fine-tuned version of [kresnik/wav2vec2-large-xlsr-korean](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "korean-aihub-learning-math-16batch", "results": []}]} | jaeyeon/korean-aihub-learning-math-16batch | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T06:10:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| korean-aihub-learning-math-16batch
==================================
This model is a fine-tuned version of kresnik/wav2vec2-large-xlsr-korean on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1497
* Wer: 0.5260
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\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: 8... |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-average-prompt-a-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) lib... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-average-prompt-a-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metr... | research-backup/roberta-large-conceptnet-average-prompt-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T06:15:54+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-average-prompt-a-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full r... | [
"# relbert/roberta-large-conceptnet-average-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (data... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-average-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done... |
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... | rlbsrn/rlexps | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-28T06:37:29+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
# a | {"tags": ["conversational"]} | trickstters/evbot2 | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T06:49:53+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #region-us
|
# a | [
"# a"
] | [
"TAGS\n#transformers #pytorch #conversational #endpoints_compatible #region-us \n",
"# a"
] |
null | transformers |
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | {"license": "mit", "tags": ["nowcasting", "forecasting", "timeseries", "remote-sensing"]} | openclimatefix/nowcasting_cnn_v4 | null | [
"transformers",
"pytorch",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T07:00:37+00:00 | [] | [] | TAGS
#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us
|
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | [
"# Nowcasting CNN",
"## Model description\n\n3d conv model, that takes in different data streams\n\n architecture is roughly\n 1. satellite image time series goes into many 3d convolution layers.\n 2. nwp time series goes into many 3d convolution layers.\n 3. Final convolutional layer ... | [
"TAGS\n#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us \n",
"# Nowcasting CNN",
"## Model description\n\n3d conv model, that takes in different data streams\n\n architecture is roughly\n 1. satellite image time series goes i... |
text-generation | null |
# RWKV-4 169M
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
## Model Description
RWKV-4 169M is a L12-D768 causal... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["the_pile"]} | BlinkDL/rwkv-4-pile-169m | null | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"dataset:the_pile",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-28T07:36:14+00:00 | [] | [
"en"
] | TAGS
#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us
|
# RWKV-4 169M
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
## Model Description
RWKV-4 169M is a L12-D768 causal... | [
"# RWKV-4 169M",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"## Model Description\n\nRWKV-4 1... | [
"TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us \n",
"# RWKV-4 169M",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
... |
null | null | # w1-speech-recognition
## Prerequisites
* libsndfile
`brew install libsndfile`
* python 3
`brew install python`
## Prepare local environment
In project root directory:
Create virtual environment:
`python3 -m venv venv`
Activate it:
`source venv/bin/activate`
## Run flask app:
```bash
./run.sh
```
You can ac... | {} | MartaKozina/w1-speech-recognition | null | [
"region:us"
] | null | 2022-07-28T08:19:51+00:00 | [] | [] | TAGS
#region-us
| # w1-speech-recognition
## Prerequisites
* libsndfile
'brew install libsndfile'
* python 3
'brew install python'
## Prepare local environment
In project root directory:
Create virtual environment:
'python3 -m venv venv'
Activate it:
'source venv/bin/activate'
## Run flask app:
You can access the app on loca... | [
"# w1-speech-recognition",
"## Prerequisites\n* libsndfile\n\n'brew install libsndfile'\n\n* python 3\n\n'brew install python'",
"## Prepare local environment\nIn project root directory:\n\nCreate virtual environment:\n\n'python3 -m venv venv'\n\nActivate it:\n\n'source venv/bin/activate'",
"## Run flask app:... | [
"TAGS\n#region-us \n",
"# w1-speech-recognition",
"## Prerequisites\n* libsndfile\n\n'brew install libsndfile'\n\n* python 3\n\n'brew install python'",
"## Prepare local environment\nIn project root directory:\n\nCreate virtual environment:\n\n'python3 -m venv venv'\n\nActivate it:\n\n'source venv/bin/activat... |
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-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "... | AlbertShu/Reinforce-v0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-28T08:22:20+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-generation | transformers |
<!-- 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. -->
# nealtao/gpt2-chinese-scifi
This model is a fine-tuned version of [uer/gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-ch... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "nealtao/gpt2-chinese-scifi", "results": []}]} | nealtao/gpt2-chinese-scifi | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-28T08:27:14+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| nealtao/gpt2-chinese-scifi
==========================
This model is a fine-tuned version of uer/gpt2-chinese-cluecorpussmall on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8822
* Validation Loss: 2.9110
* Epoch: 2
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #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\... |
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... | butchland/Optuna-ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-28T08:34:51+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... |
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-whole-word-word-ids-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-whole-word-word-ids-finetuned-imdb", "results": []}]} | amartyobanerjee/distilbert-base-uncased-whole-word-word-ids-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 | 2022-07-28T08:53:39+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-whole-word-word-ids-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: 0.6573
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: 3.0\n* mixed\\_pr... | [
"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... |
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-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | ParkSaeroyi/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T09:00:51+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-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 8.3687
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | jianzhnie/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-28T09:11:13+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config"... | mayank-01/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T09:41:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3045
- Accuracy: 0.88
- F1: 0.8831
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3045\n- Accuracy: 0.88\n- F1: 0.8831",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
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-Zu_update
This model is a fine-tuned version of [kabelomalapane/test_model1.2_updated](https://huggingface.co/kabelomalapane/... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Zu_update", "results": []}]} | kabelomalapane/En-Zu_update | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T09:55:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| En-Zu\_update
=============
This model is a fine-tuned version of kabelomalapane/test\_model1.2\_updated on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7101
* Bleu: 11.8551
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*... |
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. -->
# camembert-base-finetuned-ft750_reg2
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "camembert-base-finetuned-ft750_reg2", "results": []}]} | dminiotas05/camembert-base-finetuned-ft750_reg2 | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T10:03:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-ft750\_reg2
====================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6449
* Mse: 0.6449
* Mae: 0.6171
* R2: 0.3929
* Accuracy: 0.504
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #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: 2e-05\n* train\\_batch\\_s... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-j-roman-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-j-roman-colab", "results": []}]} | pinot/wav2vec2-large-xls-r-300m-j-roman-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T10:22:22+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-j-roman-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2233
* Wer: 0.1437
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "wnli", "sp... | jinghan/bert-base-uncased-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T10:31:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-wnli
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6917
* Accuracy: 0.5634
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: 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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\\_rat... |
text2text-generation | transformers |
# 英語+日本語T5事前学習済みモデル
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on English and Japanese balanced corpus.
次の日本語コーパス（約500GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。
* [Wikipedia](https://en.wikipedia.org)の英語ダンプデータ (2022年6月27日時点のもの)
* [Wikipedia](https://ja.wikipedia.org)の日本語ダン... | {"language": ["multilingual", "en", "ja"], "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"], "datasets": ["wikipedia", "oscar", "cc100"]} | sonoisa/t5-base-english-japanese | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"multilingual",
"en",
"ja",
"dataset:wikipedia",
"dataset:oscar",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-28T10:31:28+00:00 | [] | [
"multilingual",
"en",
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #multilingual #en #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 英語+日本語T5事前学習済みモデル
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on English and Japanese balanced corpus.
次の日本語コーパス（約500GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。
* Wikipediaの英語ダンプデータ (2022年6月27日時点のもの)
* Wikipediaの日本語ダンプデータ (2022年6月27日時点のもの)
* OSCARの日本語コーパス
* CC-100の英語コーパス
*... | [
"# 英語+日本語T5事前学習済みモデル\n\nThis is a T5 (Text-to-Text Transfer Transformer) model pretrained on English and Japanese balanced corpus.\n\n次の日本語コーパス（約500GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。 \n\n* Wikipediaの英語ダンプデータ (2022年6月27日時点のもの)\n* Wikipediaの日本語ダンプデータ (2022年6月27日時点のもの)\n* OSCARの日本語コーパス\n* CC... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #multilingual #en #ja #dataset-wikipedia #dataset-oscar #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 英語+日本語T5事前学習済みモデル\n\nThis is a T5 (Text-to-Text Transfer Transform... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ivan-savchuk/msmarco-distilbert-dot-v5-tuned-full-v1 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-28T10:47:03+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like... |
text-classification | null |
# Lyrics Classifier
This submission uses [CatBoost](https://catboost.ai/).
CatBoost was chosen for its listed benefits, mainly in requiring less hyperparameter tuning and preprocessing of categorical and text features. It is also fast and fairly easy to set up.
<img src="http://s4.thingpic.com/images/Yx/zFbS5iJFJMY... | {"language": ["en"], "license": "gpl-3.0", "tags": ["text-classification", "lyrics", "catboost"], "datasets": ["data"], "metrics": ["accuracy"], "thumbnail": "http://s4.thingpic.com/images/Yx/zFbS5iJFJMYNxDp9HTR7TQtT.png", "widget": [{"text": "I know when that hotline bling, that can only mean one thing"}]} | yukseltron/lyrics-classifier | null | [
"tensorboard",
"text-classification",
"lyrics",
"catboost",
"en",
"dataset:data",
"license:gpl-3.0",
"region:us"
] | null | 2022-07-28T11:48:01+00:00 | [] | [
"en"
] | TAGS
#tensorboard #text-classification #lyrics #catboost #en #dataset-data #license-gpl-3.0 #region-us
|
# Lyrics Classifier
This submission uses CatBoost.
CatBoost was chosen for its listed benefits, mainly in requiring less hyperparameter tuning and preprocessing of categorical and text features. It is also fast and fairly easy to set up.
<img src="URL
alt="Markdown Monster icon"
style="float: left; margin... | [
"# Lyrics Classifier\n\n\nThis submission uses CatBoost.\nCatBoost was chosen for its listed benefits, mainly in requiring less hyperparameter tuning and preprocessing of categorical and text features. It is also fast and fairly easy to set up.\n\n<img src=\"URL\n alt=\"Markdown Monster icon\"\n style=\"flo... | [
"TAGS\n#tensorboard #text-classification #lyrics #catboost #en #dataset-data #license-gpl-3.0 #region-us \n",
"# Lyrics Classifier\n\n\nThis submission uses CatBoost.\nCatBoost was chosen for its listed benefits, mainly in requiring less hyperparameter tuning and preprocessing of categorical and text features. It... |
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. -->
# my_first_model
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the im... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "my_first_model", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "defau... | AlexKolosov/my_first_model | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T11:48:11+00:00 | [] | [] | TAGS
#transformers #pytorch #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| my\_first\_model
================
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6853
* Accuracy: 0.6
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #resnet #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* learning\\_rate: ... |
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. -->
# ES_corlec_DeepESP-gpt2-spanish
This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "ES_corlec_DeepESP-gpt2-spanish", "results": []}]} | maesneako/ES_corlec_DeepESP-gpt2-spanish | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-28T11:58:13+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ES\_corlec\_DeepESP-gpt2-spanish
================================
This model is a fine-tuned version of DeepESP/gpt2-spanish on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0360
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"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: 1e-06\n* train\\_batc... |
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-cartpoleModel", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": ... | Dugerij/Reinforce-cartpoleModel | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-28T12:25:18+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 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | Al020198zee/ppo-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-28T12:36:30+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
translation | transformers | # Model Details
- **Model Description:**
This model has been pre-trained for English-Chinese Translation, and use datasets of THUOCL to fine tune the model.
- **source group**: English
- **target group**: Chinese
- **Parent Model:** Helsinki-NLP/opus-mt-en-zh, see https://huggingface.co/Helsinki-NLP/opus-mt-en-zh
- *... | {"language": ["en", "zh"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["THUOCL\u6e05\u534e\u5927\u5b66\u5f00\u653e\u4e2d\u6587\u8bcd\u5e93"], "metrics": ["bleu"], "thumbnail": "url to a thumbnail used in social sharing"} | BubbleSheep/Hgn_trans_en2zh | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-28T13:03:50+00:00 | [] | [
"en",
"zh"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Model Details
- Model Description:
This model has been pre-trained for English-Chinese Translation, and use datasets of THUOCL to fine tune the model.
- source group: English
- target group: Chinese
- Parent Model: Helsinki-NLP/opus-mt-en-zh, see URL
- Model Type: Translation
#### Training Data
- 清华大学中文开放词库(THUOCL)... | [
"# Model Details\n- Model Description:\nThis model has been pre-trained for English-Chinese Translation, and use datasets of THUOCL to fine tune the model.\n- source group: English \n- target group: Chinese \n- Parent Model: Helsinki-NLP/opus-mt-en-zh, see URL\n- Model Type: Translation",
"#### Training Data\n- 清... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #en #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Details\n- Model Description:\nThis model has been pre-trained for English-Chinese Translation, and use datasets of THUOCL to fine tu... |
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-xlsr-530-serbian-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xlsr-530-serbian-colab", "results": []}]} | dnikolic/wav2vec2-xlsr-530-serbian-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T13:06:36+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xlsr-530-serbian-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tr... | [
"# wav2vec2-xlsr-530-serbian-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m 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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-xlsr-530-serbian-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.",
"## Model description\n\nMore ... |
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. -->
# bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news
This model is a fine-tuned version of [mrm8488/b... | {"license": "apache-2.0", "tags": ["summarisation", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news", "results": []}]} | Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarisation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T13:37:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarisation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small2bert-small-finetuned-cnn\_daily\_mail-summarization-finetuned-bbc-news
=================================================================================
This model is a fine-tuned version of mrm8488/bert-small2bert-small-finetuned-cnn\_daily\_mail-summarization on an unknown dataset.
It achieves the follow... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarisation #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\\_rat... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | jirtan/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-28T13:42:17+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pixelcopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | Dugerij/Reinforce-pixelcopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-28T13:45:39+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 ... |
token-classification | spacy |
NER4Archives pipeline optimized for CPU and specialized on French National Archives findings aids (XML-EAD) - Corpus V2. Components: tok2vec, ner. Base default CNN architecture.
| Feature | Description |
| --- | --- |
| **Name** | `fr_ner4archives_default_test` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.3.1,<3.4... | {"language": ["fr"], "tags": ["spacy", "token-classification"], "widget": [{"text": "415 Lyon Lettres de r\u00e9mission accord\u00e9es \u00e0 Denis Fromant, marinier, pour meurtre commis \u00e0 Saint-Haon 1, au pays de Roannais, sur la personne de Driet Cantin qui l'accusait d'avoir maltrait\u00e9 un de ses pages et de... | ner4archives/fr_ner4archives_default_test | null | [
"spacy",
"token-classification",
"fr",
"model-index",
"region:us"
] | null | 2022-07-28T13:55:57+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #model-index #region-us
| NER4Archives pipeline optimized for CPU and specialized on French National Archives findings aids (XML-EAD) - Corpus V2. Components: tok2vec, ner. Base default CNN architecture.
### Label Scheme
View label scheme (5 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (5 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fr #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (5 labels for 1 components)",
"### Accuracy"
] |
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-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | jperezv/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T14:00:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-beans", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "config": ... | espejelomar/vit-base-beans | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T14:06:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-beans
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0637
* Accuracy: 0.9850
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
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. -->
# bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-old
This model is a fine-tuned version of [mrm84... | {"license": "apache-2.0", "tags": ["summarisation", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-old", "results": []}]} | Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-old | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarisation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T14:24:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarisation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-small2bert-small-finetuned-cnn\_daily\_mail-summarization-finetuned-bbc-news-old
=====================================================================================
This model is a fine-tuned version of mrm8488/bert-small2bert-small-finetuned-cnn\_daily\_mail-summarization on an unknown dataset.
It achieves th... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarisation #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\\_rat... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-ema-anime-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hugg... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/selfie2anime", "metrics": []} | mrm8488/ddpm-ema-anime-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/selfie2anime",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-28T15:24:40+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/selfie2anime #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-anime-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/selfie2anime' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: ... | [
"# ddpm-ema-anime-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/selfie2anime' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/selfie2anime #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-ema-anime-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/selfie2anime' dataset.",
"## Intended uses & limitations",
... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 1
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_1"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_1 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_1",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:49:55+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_1 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 1
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 1\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_1 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 1\n\nThis model is part of our r... |
text-generation | transformers |
# Josh DialoGPT Model | {"tags": ["conversational"]} | Jenwvwmabskvwh/DialoGPT-small-josh450 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-28T15:50:24+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Josh DialoGPT Model | [
"# Josh DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Josh DialoGPT Model"
] |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 2
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_2"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_2 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_2",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:50:41+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_2 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 2
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 2\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_2 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 2\n\nThis model is part of our r... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 3
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_3"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_3 | null | [
"transformers",
"pytorch",
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"fill-mask",
"roberta-base",
"roberta-base-epoch_3",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:51:25+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_3 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 3
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 3\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_3 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 3\n\nThis model is part of our r... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 4
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_4"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_4 | null | [
"transformers",
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"fill-mask",
"roberta-base",
"roberta-base-epoch_4",
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"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:52:11+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_4 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 4
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 4\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_4 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 4\n\nThis model is part of our r... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 5
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_5"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_5 | null | [
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"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_5",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:53:10+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_5 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 5
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 5\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_5 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 5\n\nThis model is part of our r... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 6
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_6"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_6 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_6",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:53:54+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_6 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 6
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 6\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_6 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 6\n\nThis model is part of our r... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 7
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_7"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_7 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_7",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:54:40+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_7 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 7
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 7\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_7 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 7\n\nThis model is part of our r... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 8
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_8"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_8 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_8",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:55:28+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_8 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 8
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 8\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_8 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 8\n\nThis model is part of our r... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 9
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_9"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_9 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_9",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:56:14+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_9 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 9
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the trai... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 9\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights before... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_9 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 9\n\nThis model is part of our r... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 10
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_10"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_10 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_10",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:56:57+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_10 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 10
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 10\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_10 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 10\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 11
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_11"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_11 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_11",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:57:41+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_11 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 11
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 11\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_11 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 11\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 12
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_12"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_12 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_12",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:59:07+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_12 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 12
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 12\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_12 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 12\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 13
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_13"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_13 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_13",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T15:59:49+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_13 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 13
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 13\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_13 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 13\n\nThis model is part of our... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | qinzhen4/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:00:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
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: 0.5962
- Accuracy: 0.72
- F1: 0.0
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5962\n- Accuracy: 0.72\n- F1: 0.0",
"## Model description\n\nMore information needed",
"## Intended uses & limit... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 14
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_14"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_14 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_14",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:00:38+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_14 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 14
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 14\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_14 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 14\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 15
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_15"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_15 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_15",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:01:23+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_15 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 15
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 15\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_15 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 15\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 16
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_16"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_16 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_16",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:02:05+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_16 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 16
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 16\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_16 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 16\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 17
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_17"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_17 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_17",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:02:47+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_17 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 17
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 17\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_17 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 17\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 18
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_18"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_18 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_18",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:03:29+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_18 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 18
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 18\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_18 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 18\n\nThis model is part of our... |
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. -->
# DNADebertaSentencepiece30k
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves th... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaSentencepiece30k", "results": []}]} | Vlasta/DNADebertaSentencepiece30k | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:03:58+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DNADebertaSentencepiece30k
==========================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.3257
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* ... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 19
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_19"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_19 | null | [
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"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:04:23+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_19 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 19
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 19\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_19 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 19\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 20
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_20"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_20 | null | [
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"fill-mask",
"roberta-base",
"roberta-base-epoch_20",
"en",
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"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:05:11+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_20 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 20
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 20\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_20 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 20\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 21
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_21"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_21 | null | [
"transformers",
"pytorch",
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"fill-mask",
"roberta-base",
"roberta-base-epoch_21",
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"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:06:01+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_21 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 21
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 21\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_21 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 21\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 22
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_22"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_22 | null | [
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"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:06:52+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_22 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 22
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 22\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_22 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 22\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 23
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_23"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_23 | null | [
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"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_23",
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"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:07:39+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_23 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 23
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 23\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_23 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 23\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 24
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_24"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_24 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_24",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:08:20+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_24 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 24
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 24\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_24 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 24\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 25
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_25"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_25 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_25",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:09:03+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_25 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 25
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 25\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_25 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 25\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 26
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_26"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_26 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_26",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:09:55+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_26 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 26
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 26\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_26 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 26\n\nThis model is part of our... |
fill-mask | transformers |
# RoBERTa, Intermediate Checkpoint - Epoch 27
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | {"language": "en", "license": "mit", "tags": ["roberta-base", "roberta-base-epoch_27"], "datasets": ["wikipedia", "bookcorpus"]} | yanaiela/roberta-base-epoch_27 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_27",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-28T16:10:38+00:00 | [
"1907.11692",
"2207.14251"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_27 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa, Intermediate Checkpoint - Epoch 27
This model is part of our reimplementation of the RoBERTa model,
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the tra... | [
"# RoBERTa, Intermediate Checkpoint - Epoch 27\n\nThis model is part of our reimplementation of the RoBERTa model, \ntrained on Wikipedia and the Book Corpus only.\nWe train this model for almost 100K steps, corresponding to 83 epochs.\nWe provide the 84 checkpoints (including the randomly initialized weights befor... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #roberta-base #roberta-base-epoch_27 #en #dataset-wikipedia #dataset-bookcorpus #arxiv-1907.11692 #arxiv-2207.14251 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa, Intermediate Checkpoint - Epoch 27\n\nThis model is part of our... |
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