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summarization | transformers |
# Randeng-Pegasus-523M-Summary-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理摘要任务，在数个中文摘要数据集上微调后的，中文版的PAGASUS-large。
Good at solving text summarization tasks, after fine-tuning on multiple Chin... | {"language": "zh", "tags": ["summarization"], "inference": false} | IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"zh",
"arxiv:1912.08777",
"arxiv:2209.02970",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-06-30T06:07:59+00:00 | [
"1912.08777",
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #zh #arxiv-1912.08777 #arxiv-2209.02970 #autotrain_compatible #has_space #region-us
| Randeng-Pegasus-523M-Summary-Chinese
====================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理摘要任务，在数个中文摘要数据集上微调后的，中文版的PAGASUS-large。
Good at solving text summarization tasks, after fine-tuning on multiple Chinese text summarization ... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #zh #arxiv-1912.08777 #arxiv-2209.02970 #autotrain_compatible #has_space #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource fo... |
reinforcement-learning | stable-baselines3 |
# **RecurrentPPO** Agent playing **LunarLander-v2**
This is a trained model of a **RecurrentPPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for St... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "RecurrentPPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type"... | Corianas/ppo_lstm-LunarLander-v2.loadbest_ | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T06:21:01+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# RecurrentPPO Agent playing LunarLander-v2
This is a trained model of a RecurrentPPO agent playing LunarLander-v2
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents include... | [
"# RecurrentPPO Agent playing LunarLander-v2\nThis is a trained model of a RecurrentPPO agent playing LunarLander-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained age... | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# RecurrentPPO Agent playing LunarLander-v2\nThis is a trained model of a RecurrentPPO agent playing LunarLander-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a ... |
reinforcement-learning | stable-baselines3 |
# **RecurrentPPO** Agent playing **LunarLander-v2**
This is a trained model of a **RecurrentPPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for St... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "RecurrentPPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type"... | Corianas/ppo_lstm-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T06:21:53+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# RecurrentPPO Agent playing LunarLander-v2
This is a trained model of a RecurrentPPO agent playing LunarLander-v2
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents include... | [
"# RecurrentPPO Agent playing LunarLander-v2\nThis is a trained model of a RecurrentPPO agent playing LunarLander-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained age... | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# RecurrentPPO Agent playing LunarLander-v2\nThis is a trained model of a RecurrentPPO agent playing LunarLander-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a ... |
token-classification | transformers | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO | {"license": "afl-3.0"} | sumitrsch/muril_base_multiconer22_bn | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T06:24:11+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
URL | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# adeebt/opus-mt-en-ml-finetuned-en-to-ml
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ml](https://huggingface.co/Hels... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "adeebt/opus-mt-en-ml-finetuned-en-to-ml", "results": []}]} | adeebt/opus-mt-en-ml-finetuned-en-to-ml | null | [
"transformers",
"tf",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T06:32:50+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| adeebt/opus-mt-en-ml-finetuned-en-to-ml
=======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ml on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5102
* Validation Loss: 2.2650
* Train Bleu: 6.9525
* Train Gen Len: 22.354... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 0.0002, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",... | [
"TAGS\n#transformers #tf #tensorboard #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay... |
token-classification | transformers |
# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Named Entity Recognition.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [T... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "named entity recognition", "ner", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/ancora-ca-ner"], "metrics": ["f1"], "widget": [{"text": "Em dic Llu\u00efsa i visc a Santa Maria del Cam\u00ed."}, {"text": "L'Aina, la Berta i la Norma s... | projecte-aina/roberta-base-ca-v2-cased-ner | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"catalan",
"named entity recognition",
"ner",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:projecte-aina/ancora-ca-ner",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compa... | null | 2022-06-30T06:53:54+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #token-classification #catalan #named entity recognition #ner #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/ancora-ca-ner #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Named Entity Recognition.
=============================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Train... | [
"### Training data\n\n\nWe used the NER dataset in Catalan called AnCora-Ca-NER for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the correspon... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #catalan #named entity recognition #ner #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/ancora-ca-ner #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe us... |
question-answering | transformers |
# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Question Answering.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Traini... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "qa"], "datasets": ["projecte-aina/catalanqa", "projecte-aina/xquad-ca"], "metrics": ["f1", "exact match"], "widget": [{"text": "Quan va comen\u00e7ar el Super3?", "context": "El Super3 o Club Super3 \u00e9s un univers infantil catal\u00e0 creat a partir... | projecte-aina/roberta-base-ca-v2-cased-qa | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"catalan",
"qa",
"ca",
"dataset:projecte-aina/catalanqa",
"dataset:projecte-aina/xquad-ca",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-30T06:54:30+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #question-answering #catalan #qa #ca #dataset-projecte-aina/catalanqa #dataset-projecte-aina/xquad-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Question Answering.
=======================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training data
+ ... | [
"### Training data\n\n\nWe used the QA dataset in Catalan called CatalanQA for training and evaluation, and the XQuAD-ca test set for evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the do... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #catalan #qa #ca #dataset-projecte-aina/catalanqa #dataset-projecte-aina/xquad-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training data\n\n\nWe used the QA dataset in Catalan called CatalanQ... |
text-classification | transformers |
# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Textual Entailment.
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Tr... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "textual entailment", "teca", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/teca"], "metrics": ["accuracy"], "widget": [{"text": "M'agrades. T'estimo."}, {"text": "M'agrada el sol i la calor. A la Garrotxa plou molt."}, {"text": "El ll... | projecte-aina/roberta-base-ca-v2-cased-te | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"catalan",
"textual entailment",
"teca",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:projecte-aina/teca",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"regi... | null | 2022-06-30T06:54:58+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #text-classification #catalan #textual entailment #teca #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/teca #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Textual Entailment.
=======================================================================
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training data
+ Training procedure
* Evaluation
+ Var... | [
"### Training data\n\n\nWe used the TE dataset in Catalan called TE-ca for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding deve... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #catalan #textual entailment #teca #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/teca #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe used the TE datas... |
text-classification | transformers |
# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bia... | {"language": ["ca"], "tags": ["catalan", "text classification", "tecla", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/tecla"], "metrics": ["accuracy"], "widget": [{"text": "Els Pets presenten el seu nou treball al Palau Sant Jordi."}, {"text": "Els barcelonins incrementen un 23% l\u2019\u00fas del c... | projecte-aina/roberta-base-ca-v2-cased-tc | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"catalan",
"text classification",
"tecla",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:projecte-aina/tecla",
"arxiv:1907.11692",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T06:55:23+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #text-classification #catalan #text classification #tecla #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/tecla #arxiv-1907.11692 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification.
====================================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Tra... | [
"### Training data\n\n\nWe used the TC dataset in Catalan called TeCla for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training.",
"### Training procedure\n\n\nThe model was trained ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #catalan #text classification #tecla #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/tecla #arxiv-1907.11692 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe used the TC dataset in Catalan cal... |
text-classification | transformers |
# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Semantic Textual Similarity.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
-... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "semantic textual similarity", "sts-ca", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/sts-ca"], "metrics": ["combined_score"], "pipeline_tag": "text-classification", "model-index": [{"name": "roberta-base-ca-v2-cased-sts", "results": ... | projecte-aina/roberta-base-ca-v2-cased-sts | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"catalan",
"semantic textual similarity",
"sts-ca",
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"Catalan Textual Corpus",
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"dataset:projecte-aina/sts-ca",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compati... | null | 2022-06-30T06:55:48+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #text-classification #catalan #semantic textual similarity #sts-ca #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/sts-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Semantic Textual Similarity.
================================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+... | [
"### Training data\n\n\nWe used the STS dataset in Catalan called STS-ca for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding de... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #catalan #semantic textual similarity #sts-ca #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/sts-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe used... |
token-classification | transformers |
# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Part-of-speech-tagging (POS)
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
-... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "part of speech tagging", "pos", "CaText", "Catalan Textual Corpus"], "datasets": ["universal_dependencies"], "metrics": ["f1"], "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": [{"text": "Em dic Llu\u00efsa i visc a Santa Maria ... | projecte-aina/roberta-base-ca-v2-cased-pos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"catalan",
"part of speech tagging",
"pos",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:universal_dependencies",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",... | null | 2022-06-30T06:56:13+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #token-classification #catalan #part of speech tagging #pos #CaText #Catalan Textual Corpus #ca #dataset-universal_dependencies #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Part-of-speech-tagging (POS)
================================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+... | [
"### Training data\n\n\nWe used the POS dataset in Catalan from the Universal Dependencies Treebank we refer to *Ancora-ca-pos* for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint u... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #catalan #part of speech tagging #pos #CaText #Catalan Textual Corpus #ca #dataset-universal_dependencies #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe used the ... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | ThomasSimonini/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-06-30T07:13:02+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
text-classification | null |
**task**: `text-classification`
Fixed parameters:
* **model_name_or_path**: `Bhumika/roberta-base-finetuned-sst2`
* **dataset**:
* **path**: `glue`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'sentence'}`
* **ref_keys**: `['label']`
* **name**: `sst2`
* **quantization_approach**: ... | {"tags": ["roberta"], "datasets": ["glue"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"} | fxmarty/donotdelete3 | null | [
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"region:us"
] | null | 2022-06-30T07:15:10+00:00 | [] | [] | TAGS
#tensorboard #roberta #text-classification #dataset-glue #region-us
|
task: 'text-classification'
Fixed parameters:
* model_name_or_path: 'Bhumika/roberta-base-finetuned-sst2'
* dataset:
* path: 'glue'
* eval_split: 'validation'
* data_keys: '{'primary': 'sentence'}'
* ref_keys: '['label']'
* name: 'sst2'
* quantization_approach: 'dynamic'
* node_exclusion: '[]'
* p... | [
"## Evaluation\nBelow, time metrics for\n* Batch size: 8\n* Input length: 128\n| operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) | | accuracy (original) | accuracy (optimized) |\n| :-------------------: | :-:... | [
"TAGS\n#tensorboard #roberta #text-classification #dataset-glue #region-us \n",
"## Evaluation\nBelow, time metrics for\n* Batch size: 8\n* Input length: 128\n| operators_to_quantize | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |... |
null | null | git lfs install
git clone https://huggingface.co/Mytios919/Mytios | {} | Mytios919/Mytios | null | [
"region:us"
] | null | 2022-06-30T07:31:02+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#region-us \n"
] |
null | null |
<iframe src="https://hf.space/embed/abidlabs/pytorch-image-classifier/+" frameBorder="0" width="100%" height="660px" title="Gradio app" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; m... | {} | osanseviero/test_nemo | null | [
"region:us"
] | null | 2022-06-30T07:51:22+00:00 | [] | [] | TAGS
#region-us
|
<iframe src="URL frameBorder="0" width="100%" height="660px" title="Gradio app" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment... | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
# CKIP BERT Base Han Chinese POS
This model provides part-of-speech (POS) tagging for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.
## Homepage
* [ckiplab/han-transformers](https://github.com/ckiplab/han-transformers)
## Training Datasets
The copyright of the datasets ... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/bert-base-han-chinese-pos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T08:10:32+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP BERT Base Han Chinese POS
This model provides part-of-speech (POS) tagging for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.
## Homepage
* ckiplab/han-transformers
## Training Datasets
The copyright of the datasets belongs to the Institute of Linguistics, Academ... | [
"# CKIP BERT Base Han Chinese POS\n\nThis model provides part-of-speech (POS) tagging for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.",
"## Homepage\n* ckiplab/han-transformers",
"## Training Datasets\nThe copyright of the datasets belongs to the Institute of Lin... | [
"TAGS\n#transformers #pytorch #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP BERT Base Han Chinese POS\n\nThis model provides part-of-speech (POS) tagging for the ancient Chinese language. Our training dataset covers four eras of the Chinese la... |
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. -->
# codet5-base-masked-buggy-code-repair
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Sales... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "codet5-base-masked-buggy-code-repair", "results": []}]} | alexjercan/codet5-base-masked-buggy-code-repair | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T08:17:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# codet5-base-masked-buggy-code-repair
This model is a fine-tuned version of Salesforce/codet5-base on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2876
- Precision: 0.1990
- Recall: 0.3
- F1: 0.2320
- Accuracy: 0.3
## Model description
More information needed
## Intende... | [
"# codet5-base-masked-buggy-code-repair\n\nThis model is a fine-tuned version of Salesforce/codet5-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2876\n- Precision: 0.1990\n- Recall: 0.3\n- F1: 0.2320\n- Accuracy: 0.3",
"## Model description\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# codet5-base-masked-buggy-code-repair\n\nThis model is a fine-tuned version of Salesforce/codet5-base on an unknown dataset.\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hamishm/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hamishm/distilbert-base-uncased-finetuned-squad", "results": []}]} | hamishm/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T08:41:52+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| hamishm/distilbert-base-uncased-finetuned-squad
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7763
* Validation Loss: 1.1324
* Epoch: 1
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 177048, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'n... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ms12345/roberta-base-squad2-finetuned-squad
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co... | {"license": "cc-by-4.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ms12345/roberta-base-squad2-finetuned-squad", "results": []}]} | ms12345/roberta-base-squad2-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T09:06:22+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #roberta #question-answering #generated_from_keras_callback #license-cc-by-4.0 #endpoints_compatible #region-us
| ms12345/roberta-base-squad2-finetuned-squad
===========================================
This model is a fine-tuned version of deepset/roberta-base-squad2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.3404
* Validation Loss: 1.0278
* Epoch: 0
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 46, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name'... | [
"TAGS\n#transformers #tf #tensorboard #roberta #question-answering #generated_from_keras_callback #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_nam... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnn-v2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnn-v2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dail... | ubikpt/t5-small-finetuned-cnn-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T09:12:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnn-v2
=========================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5474
* Rouge1: 35.154
* Rouge2: 18.683
* Rougel: 30.8481
* Rougelsum: 32.9638
Model description
-----------------
M... | [
"### 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 #t5 #text2text-generation #summarization #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameter... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22
This model is a fine-tuned version of [domenicrosati/deberta-v... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22", "results": []}]} | domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T09:25:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-dapt-scientific-papers-pubmed-finetuned-DAGPap22
=================================================================
This model is a fine-tuned version of domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed on an unknown dataset.
It achieves the following results on the evaluation set:
* Los... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Someman/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T09:53:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2186
* Accuracy: 0.9245
* F1: 0.9246
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
## Model information:
This model is the [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BER... | {"language": "en", "license": "cc", "tags": ["text classification"], "datasets": "MIMIC-III", "widget": [{"text": "This report discusses the diagnosis of lung cancer in a female patient who has never smoked."}]} | sarahmiller137/distilbert-base-uncased-ft-m3-lc | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"text classification",
"en",
"dataset:MIMIC-III",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T10:05:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #text classification #en #dataset-MIMIC-III #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
This model is the distilbert-base-uncased model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology rep... | [
"## Model information:\nThis model is the distilbert-base-uncased model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiolog... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #text classification #en #dataset-MIMIC-III #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\nThis model is the distilbert-base-uncased model that has been finetuned using radiology report tex... |
fill-mask | transformers |
# CKIP BERT Base Han Chinese
Pretrained model on Ancient Chinese language using a masked language modeling (MLM) objective.
## Homepage
* [ckiplab/han-transformers](https://github.com/ckiplab/han-transformers)
## Training Datasets
The copyright of the datasets belongs to the Institute of Linguistics, Academia Sinic... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/bert-base-han-chinese | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"lm-head",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T10:19:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP BERT Base Han Chinese
Pretrained model on Ancient Chinese language using a masked language modeling (MLM) objective.
## Homepage
* ckiplab/han-transformers
## Training Datasets
The copyright of the datasets belongs to the Institute of Linguistics, Academia Sinica.
* 中央研究院上古漢語標記語料庫
* 中央研究院中古漢語語料庫
* 中央研究院近代漢語語... | [
"# CKIP BERT Base Han Chinese\n\nPretrained model on Ancient Chinese language using a masked language modeling (MLM) objective.",
"## Homepage\n* ckiplab/han-transformers",
"## Training Datasets\nThe copyright of the datasets belongs to the Institute of Linguistics, Academia Sinica.\n* 中央研究院上古漢語標記語料庫\n* 中央研究院中古... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP BERT Base Han Chinese\n\nPretrained model on Ancient Chinese language using a masked language modeling (MLM) objective.",
"## Homepage\n* ckiplab/han-transformers",
... |
fill-mask | transformers |
**AlephBERT-base-finetuned-for-shut**
**Hebrew Language Model**
Based on alephbert-base: https://huggingface.co/onlplab/alephbert-base#alephbert
**How to use:**
from transformers import AutoModelForMaskedLM, AutoTokenizer
checkpoint = 'ysnow9876/alephbert-base-finetuned-for-shut'
tokenizer = AutoTokenizer.from_... | {"language": ["he"], "tags": ["language model"], "datasets": ["responsa"]} | ysnow9876/alephbert-base-finetuned-for-shut | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"language model",
"he",
"dataset:responsa",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T10:28:30+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #bert #fill-mask #language model #he #dataset-responsa #autotrain_compatible #endpoints_compatible #region-us
|
AlephBERT-base-finetuned-for-shut
Hebrew Language Model
Based on alephbert-base: URL
How to use:
from transformers import AutoModelForMaskedLM, AutoTokenizer
checkpoint = 'ysnow9876/alephbert-base-finetuned-for-shut'
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model= AutoModelForMaskedLM.from_pretrai... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #language model #he #dataset-responsa #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | emen/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T10:35:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2181
* Accuracy: 0.9295
* F1: 0.9298
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | igpaub/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T10:49:44+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-classification | transformers |
## Model information:
This model is the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the cl... | {"language": "en", "license": "cc", "tags": ["text classification"], "datasets": ["MIMIC-III\u00a0"], "widget": [{"text": "This report discusses the diagnosis of lung cancer in a female patient who has never smoked."}]} | sarahmiller137/bert-base-uncased-ft-m3-lc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text classification",
"en",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T10:55:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #text classification #en #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
This model is the bert-base-uncased model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology report te... | [
"## Model information:\nThis model is the bert-base-uncased model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology repo... | [
"TAGS\n#transformers #pytorch #bert #text-classification #text classification #en #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\nThis model is the bert-base-uncased model that has been finetuned using radiology report texts from the MIMIC-III database. The task per... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-triviaqa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-triviaqa", "results": []}]} | FabianWillner/bert-base-uncased-finetuned-triviaqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T11:10:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-triviaqa
====================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9252
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | pannaga/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T11:12:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5480
* Wer: 0.3437
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1060936832
- CO2 Emissions (in grams): 3.841483701875158
## Validation Metrics
- Loss: 0.5115200877189636
- Rouge1: 27.3016
- Rouge2: 10.4762
- RougeL: 27.3016
- RougeLsum: 27.1111
- Gen Len: 14.3619
## Usage
You can use cURL to access this... | {"language": "unk", "tags": "autotrain", "datasets": ["zhifei/autotrain-data-chinese-title-summarization"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.841483701875158} | zhifei/autotrain-chinese-title-summarization-1060936832 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"unk",
"dataset:zhifei/autotrain-data-chinese-title-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T11:20:46+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #unk #dataset-zhifei/autotrain-data-chinese-title-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1060936832
- CO2 Emissions (in grams): 3.841483701875158
## Validation Metrics
- Loss: 0.5115200877189636
- Rouge1: 27.3016
- Rouge2: 10.4762
- RougeL: 27.3016
- RougeLsum: 27.1111
- Gen Len: 14.3619
## Usage
You can use cURL to access this... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1060936832\n- CO2 Emissions (in grams): 3.841483701875158",
"## Validation Metrics\n\n- Loss: 0.5115200877189636\n- Rouge1: 27.3016\n- Rouge2: 10.4762\n- RougeL: 27.3016\n- RougeLsum: 27.1111\n- Gen Len: 14.3619",
"## Usage\n\nYou ca... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #unk #dataset-zhifei/autotrain-data-chinese-title-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1060836848
- CO2 Emissions (in grams): 0.2263611804615655
## Validation Metrics
- Loss: 2.3939340114593506
- Rouge1: 0.3375
- Rouge2: 0.0
- RougeL: 0.3375
- RougeLsum: 0.3375
- Gen Len: 11.4395
## Usage
You can use cURL to access this model... | {"language": "unk", "tags": "autotrain", "datasets": ["dddb/autotrain-data-mt5_chinese_small_finetune"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.2263611804615655} | dddb/title_generator | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"unk",
"dataset:dddb/autotrain-data-mt5_chinese_small_finetune",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T12:00:22+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #unk #dataset-dddb/autotrain-data-mt5_chinese_small_finetune #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1060836848
- CO2 Emissions (in grams): 0.2263611804615655
## Validation Metrics
- Loss: 2.3939340114593506
- Rouge1: 0.3375
- Rouge2: 0.0
- RougeL: 0.3375
- RougeLsum: 0.3375
- Gen Len: 11.4395
## Usage
You can use cURL to access this model... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1060836848\n- CO2 Emissions (in grams): 0.2263611804615655",
"## Validation Metrics\n\n- Loss: 2.3939340114593506\n- Rouge1: 0.3375\n- Rouge2: 0.0\n- RougeL: 0.3375\n- RougeLsum: 0.3375\n- Gen Len: 11.4395",
"## Usage\n\nYou can use ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #unk #dataset-dddb/autotrain-data-mt5_chinese_small_finetune #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1... |
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-pysentimiento-war-tweets
This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggi... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-pysentimiento-war-tweets", "results": []}]} | emegona/finetuning-pysentimiento-war-tweets | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T12:03:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-pysentimiento-war-tweets
This model is a fine-tuned version of finiteautomata/beto-sentiment-analysis on a dataset of 1500 tweets from Peruvian accounts. It achieves the following results on the evaluation set:
- Loss: 1.7689
- Accuracy: 0.7378
- F1: 0.7456
## Model description
This model in a fine-t... | [
"# finetuning-pysentimiento-war-tweets\n\nThis model is a fine-tuned version of finiteautomata/beto-sentiment-analysis on a dataset of 1500 tweets from Peruvian accounts. It achieves the following results on the evaluation set:\n- Loss: 1.7689\n- Accuracy: 0.7378\n- F1: 0.7456",
"## Model description\n\nThis mode... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-pysentimiento-war-tweets\n\nThis model is a fine-tuned version of finiteautomata/beto-sentiment-analysis on a dataset of 1500 tweets from Peruvian... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 9215210
- CO2 Emissions (in grams): 0.2757084122251468
## Validation Metrics
- Loss: 0.1699502319097519
- Accuracy: 0.9372
- Precision: 0.9277551659361303
- Recall: 0.94824
- AUC: 0.9837227744
- F1: 0.9378857414147808
## Usage
You c... | {"language": "en", "tags": "autotrain", "datasets": ["abhishek/autotrain-data-imdbtestmodel"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.2757084122251468} | abhishek/autotrain-imdbtestmodel-9215210 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:abhishek/autotrain-data-imdbtestmodel",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T12:07:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-abhishek/autotrain-data-imdbtestmodel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 9215210
- CO2 Emissions (in grams): 0.2757084122251468
## Validation Metrics
- Loss: 0.1699502319097519
- Accuracy: 0.9372
- Precision: 0.9277551659361303
- Recall: 0.94824
- AUC: 0.9837227744
- F1: 0.9378857414147808
## Usage
You c... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 9215210\n- CO2 Emissions (in grams): 0.2757084122251468",
"## Validation Metrics\n\n- Loss: 0.1699502319097519\n- Accuracy: 0.9372\n- Precision: 0.9277551659361303\n- Recall: 0.94824\n- AUC: 0.9837227744\n- F1: 0.93788574141478... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-abhishek/autotrain-data-imdbtestmodel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 9215210\n- CO2 Emissions (in g... |
fill-mask | transformers |
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority ... | {"language": "en", "license": "mit", "tags": ["deberta", "deberta-v3", "fill-mask"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | abhishek/deberta-v3-base-autotrain | null | [
"transformers",
"pytorch",
"deberta-v2",
"deberta",
"deberta-v3",
"fill-mask",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T12:07:51+00:00 | [
"2006.03654",
"2111.09543"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #deberta #deberta-v3 #fill-mask #en #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #endpoints_compatible #region-us
| DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
----------------------------------------------------------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. Wit... | [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.\n\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MNLI tasks.",
"#### Fine-tuning with HF transformers\n\n\nIf you find DeBERTa useful for your work, please cite the following papers:"
] | [
"TAGS\n#transformers #pytorch #deberta-v2 #deberta #deberta-v3 #fill-mask #en #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #endpoints_compatible #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 2.0 and MNLI tasks.\n\n\n\nWe present the dev results on SQuAD 1.1/2.0 and MN... |
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... | WJRG/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T12:28:45+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
# Model Card of `lmqg/mbart-large-cc25-itquad-qg`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.co... | {"language": "it", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_itquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere t... | research-backup/mbart-large-cc25-itquad-qg | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"question generation",
"it",
"dataset:lmqg/qg_itquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T12:54:30+00:00 | [
"2210.03992"
] | [
"it"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #it #dataset-lmqg/qg_itquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/mbart-large-cc25-itquad-qg'
===============================================
This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg\_itquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: facebook/mbart-large-cc25
* Language... | [
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: it\n* Training data: lmqg/qg\\_itquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #question generation #it #dataset-lmqg/qg_itquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: it\n* Training data:... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": ... | haesun/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T13:17:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1338
* F1: 0.8657
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
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. -->
# deberta-v3-large-dapt-scientific-papers-pubmed-tapt
This model is a fine-tuned version of [domenicrosati/deberta-v3-large-dapt-s... | {"license": "mit", "tags": ["fill-mask", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large-dapt-scientific-papers-pubmed-tapt", "results": []}]} | domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-tapt | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T13:29:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-dapt-scientific-papers-pubmed-tapt
===================================================
This model is a fine-tuned version of domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4429
* Accuracy: 0.5915
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n*... |
fill-mask | transformers |
## RoBERTa Catalan base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* [wiki40b/ca](https://www.tensorflow.org/datasets/catalog/wiki4... | {"language": "ca", "license": "cc-by-sa-4.0", "datasets": ["wikipedia", "cc100"], "widget": [{"text": "\u00c9s molt <mask> per a mi."}, {"text": "Vas jugar a <mask>."}, {"text": "Ell est\u00e0 una mica <mask>."}, {"text": "\u00c9s un bon <mask>."}, {"text": "M'agradaria menjar una <mask>."}]} | ClassCat/roberta-base-catalan | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ca",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T13:32:46+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ca #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## RoBERTa Catalan base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* wiki40b/ca (Catalan Wikipedia)
* Subset of CC-100/ca : Monolin... | [
"## RoBERTa Catalan base model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses RoBERTa base setttings except vocabulary size.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Training Data \n\n* wiki40b/ca (Catalan Wikipedia)\n* ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ca #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## RoBERTa Catalan base model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses RoBERTa b... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="RicardFos/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | RicardFos/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-30T13:33:55+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1062136864
- CO2 Emissions (in grams): 141.11976199388627
## Validation Metrics
- Loss: 0.10147109627723694
- Accuracy: 0.9859325979151907
- Macro F1: 0.9715036017680622
- Micro F1: 0.9859325979151907
- Weighted F1: 0.98590705414... | {"language": "unk", "tags": "autotrain", "datasets": ["Maxbnza/autotrain-data-address-training"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 141.11976199388627} | Maxbnza/country-recognition | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:Maxbnza/autotrain-data-address-training",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T13:59:15+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-Maxbnza/autotrain-data-address-training #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1062136864
- CO2 Emissions (in grams): 141.11976199388627
## Validation Metrics
- Loss: 0.10147109627723694
- Accuracy: 0.9859325979151907
- Macro F1: 0.9715036017680622
- Micro F1: 0.9859325979151907
- Weighted F1: 0.98590705414... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1062136864\n- CO2 Emissions (in grams): 141.11976199388627",
"## Validation Metrics\n\n- Loss: 0.10147109627723694\n- Accuracy: 0.9859325979151907\n- Macro F1: 0.9715036017680622\n- Micro F1: 0.9859325979151907\n- Weighted... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-Maxbnza/autotrain-data-address-training #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1062136864\n- C... |
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/1211957929915629569/5woq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/orangebook_/1656601586971/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/orangebook_ | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T14:02:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Orange Book
@orangebook\_
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# destillbert-statementsaboutfuture
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "destillbert-statementsaboutfuture", "results": []}]} | jonaskoenig/destillbert-statementsaboutfuture | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T14:49:17+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# destillbert-statementsaboutfuture
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
... | [
"# destillbert-statementsaboutfuture\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# destillbert-statementsaboutfuture\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the fol... |
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="RicardFos/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 +/... | RicardFos/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-30T14:53:17+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **basic** Agent playing **LunarLander-v2**
This is a trained model of a **basic** 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_... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "basic", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "Luna... | SylvLej/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T15:03:45+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# basic Agent playing LunarLander-v2
This is a trained model of a basic agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# basic Agent playing LunarLander-v2\nThis is a trained model of a basic 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",
"# basic Agent playing LunarLander-v2\nThis is a trained model of a basic agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add... |
text2text-generation | transformers |
# mT5-small based spanish paraphraser
### Original model
- [Google's mT5](https://huggingface.co/google/mt5-small)
### Datasets used for training:
- spanish [PAWS-X](https://huggingface.co/datasets/paws-x)
- Custom database: "Poor-man's" translation of [duplicated questions in Quora](https://huggingface.co/dataset... | {"license": "apache-2.0"} | pserna/mt5-small-spanish-paraphraser | null | [
"transformers",
"pytorch",
"tf",
"mt5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T15:07:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #mt5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5-small based spanish paraphraser
### Original model
- Google's mT5
### Datasets used for training:
- spanish PAWS-X
- Custom database: "Poor-man's" translation of duplicated questions in Quora (translated with Helsinki-NLP/opus-mt-en-es)
| [
"# mT5-small based spanish paraphraser",
"### Original model\n- Google's mT5",
"### Datasets used for training:\n- spanish PAWS-X\n- Custom database: \"Poor-man's\" translation of duplicated questions in Quora (translated with Helsinki-NLP/opus-mt-en-es)"
] | [
"TAGS\n#transformers #pytorch #tf #mt5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5-small based spanish paraphraser",
"### Original model\n- Google's mT5",
"### Datasets used for training:\n- spanish PAWS-X\n- Custom da... |
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. -->
This model is a fine-tuned version of [vinai/bertweet-covid19-base-uncased](https://huggingface.co/vinai/bertweet-covid19-base-uncased)... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "US_politicians_covid_skepticism", "results": []}]} | z-dickson/US_politicians_covid_skepticism | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T15:17:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
This model is a fine-tuned version of vinai/bertweet-covid19-base-uncased on a dataset of 10k tweets about COVID-19 policies from US legislators in the House and Senate.
The model is intended to identify skepticism of COVID-19 policies (i.e. masks, social distancing, lockdowns, vaccines etc.).
It's a pretty ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft500
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": "distilbert-base-uncased-finetuned-ft500", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft500 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T15:20:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft500
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1340
* Accuracy: 0.5433
* F1: 0.5118
Model description
-----------------
More in... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# s288cExpressionPrediction_k4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "s288cExpressionPrediction_k4", "results": []}]} | zluvolyote/s288cExpressionPrediction_k4 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T15:44:32+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# s288cExpressionPrediction_k4
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# s288cExpressionPrediction_k4\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# s288cExpressionPrediction_k4\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\n... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **BeamRiderNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BeamRiderNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for St... | {"library_name": "stable-baselines3", "tags": ["BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BeamRiderNoFrameskip-v4... | danieladejumo/dqn-BeamRiderNoFrameskip-v4 | null | [
"stable-baselines3",
"BeamRiderNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T16:04:45+00:00 | [] | [] | TAGS
#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing BeamRiderNoFrameskip-v4
This is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents include... | [
"# DQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained age... | [
"TAGS\n#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL ... |
null | null | Trying the model for the first time | {} | Cathyhuang/Trial1 | null | [
"region:us"
] | null | 2022-06-30T16:14:36+00:00 | [] | [] | TAGS
#region-us
| Trying the model for the first time | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# kmkarakaya/turkishReviews-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achie... | {"license": "mit", "tags": ["generated_from_keras_callback"], "datasets": "kmkarakaya/turkishReviews-ds", "model-index": [{"name": "kmkarakaya/turkishReviews-ds", "results": []}]} | kmkarakaya/turkishReviews-ds | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"dataset:kmkarakaya/turkishReviews-ds",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T16:31:33+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #dataset-kmkarakaya/turkishReviews-ds #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| kmkarakaya/turkishReviews-ds
============================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.4254
* Validation Loss: 5.4114
* Epoch: 4
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #dataset-kmkarakaya/turkishReviews-ds #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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/1438687954285707265/aEtA... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/codyko-thenoelmiller/1656610826736/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/codyko-thenoelmiller | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T16:39:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
codyko & Noel Miller
@codyko-thenoelmiller
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.
Tr... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | dperezjr/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T16:48:36+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-turkish-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: 0.3783
* Wer: 0.3036
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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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... |
image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | tmoodley/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T18:11:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-dapt-tapt-scientific-papers-pubmed-finetuned-DAGPap22
This model is a fine-tuned version of [domenicrosati/debe... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-v3-large-dapt-tapt-scientific-papers-pubmed-finetuned-DAGPap22", "results": []}]} | domenicrosati/deberta-v3-large-dapt-tapt-scientific-papers-pubmed-finetuned-DAGPap22 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T18:28:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-dapt-tapt-scientific-papers-pubmed-finetuned-DAGPap22
======================================================================
This model is a fine-tuned version of domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-tapt on an unknown dataset.
It achieves the following results on the evaluati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_... |
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. -->
# new_exper3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patc... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "new_exper3", "results": []}]} | sudo-s/new_exper3 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T18:43:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| new\_exper3
===========
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3000
* Accuracy: 0.9298
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-es-clinical-trials-ner
This medical named entity recognition model detects 4 types of semantic groups from the Unified M... | {"language": ["es"], "license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "El ensayo cl\u00ednico con vacunas promete buenos resultados para la infecci\u00f3n por SARS-CoV-2."}, {"text": "El paciente toma aspirina para el dolor de cabez... | lcampillos/roberta-es-clinical-trials-ner | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"es",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-30T19:14:09+00:00 | [
"1910.09700"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #es #arxiv-1910.09700 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| roberta-es-clinical-trials-ner
==============================
This medical named entity recognition model detects 4 types of semantic groups from the Unified Medical Language System (UMLS) (Bodenreider 2004):
* ANAT: body parts and anatomy (e.g. *garganta*, 'throat')
* CHEM: chemical entities and pharmacological su... | [
"### 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #generated_from_trainer #es #arxiv-1910.09700 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-generation | transformers |
# Spanish GPT-2 as backbone
Fine-tuned model on Spanish language using [Opensubtitle](https://opus.nlpl.eu/OpenSubtitles-v2018.php) dataset. The original GPT-2
model was used as backbone which has been trained from scratch on the Spanish portion of OSCAR dataset, according to the [Flax/Jax](https://huggingface.co/fl... | {"language": ["es"], "license": "gpl-3.0", "tags": ["conversational", "gpt2"], "datasets": ["open_subtitles"], "widget": [{"text": "Me gusta el deporte", "example_title": "Interacci\u00f3n"}, {"text": "Hola", "example_title": "Saludo"}, {"text": "\u00bfComo estas?", "example_title": "Pregunta"}]} | erikycd/chatbot_hadita | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"es",
"dataset:open_subtitles",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T19:14:31+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #es #dataset-open_subtitles #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Spanish GPT-2 as backbone
Fine-tuned model on Spanish language using Opensubtitle dataset. The original GPT-2
model was used as backbone which has been trained from scratch on the Spanish portion of OSCAR dataset, according to the Flax/Jax
Community by HuggingFace.
## Model description and fine tunning
First, t... | [
"# Spanish GPT-2 as backbone\n\nFine-tuned model on Spanish language using Opensubtitle dataset. The original GPT-2 \nmodel was used as backbone which has been trained from scratch on the Spanish portion of OSCAR dataset, according to the Flax/Jax \nCommunity by HuggingFace.",
"## Model description and fine tunni... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #es #dataset-open_subtitles #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Spanish GPT-2 as backbone\n\nFine-tuned model on Spanish language using Opensubtitle dataset. The... |
text-classification | transformers | Bert Base Uncased Contract model trained on CUAD Dataset
The Dataset can be downloaded from [Here](https://www.atticusprojectai.org/cuad). | {} | amanbawa96/bert-base-uncase-contracts | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T19:28:21+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Bert Base Uncased Contract model trained on CUAD Dataset
The Dataset can be downloaded from Here. | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1433787116471869441/tk0v... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/enusec-lewisnwatson/1656621875256/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/enusec-lewisnwatson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T19:42:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Edinburgh Napier University Security Society & Lewis N Watson 🇺🇦
@enusec-lewisnwatson
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 mo... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/ml-latest-small-user-model-32 | null | [
"keras",
"region:us"
] | null | 2022-06-30T19:49:43+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/ml-latest-small-movie-model-32 | null | [
"keras",
"region:us"
] | null | 2022-06-30T19:50:00+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **PPO** agent playing **MountainCarContinuous-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 ...
f... | {"library_name": "stable-baselines3", "tags": ["MountainCarContinuous-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCarContinuous-... | danieladejumo/ppo-mountain_car | null | [
"stable-baselines3",
"MountainCarContinuous-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T19:53:22+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCarContinuous-v0
This is a trained model of a PPO agent playing MountainCarContinuous-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a PPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a PPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library.",
"## Usage (with Sta... |
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/1509825675821301790/FCFa... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lewisnwatson/1656622460314/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lewisnwatson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T19:53:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Lewis N Watson 🇺🇦
@lewisnwatson
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **PPO** agent playing **MountainCarContinuous-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for ... | {"library_name": "stable-baselines3", "tags": ["MountainCarContinuous-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCarContinuous-... | danieladejumo/ppo-MountainCarContinuous-v0 | null | [
"stable-baselines3",
"MountainCarContinuous-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T20:11:53+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCarContinuous-v0
This is a trained model of a PPO agent playing MountainCarContinuous-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents inclu... | [
"# PPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a PPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained a... | [
"TAGS\n#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a PPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLM-RoBERTa-xtreme-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the x... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme_en"], "metrics": ["accuracy", "f1"], "widget": [{"text": "My name is Julia, I study at Imperial College, in London", "example_title": "Example 1"}, {"text": "My name is Sarah and I live in Paris", "example_title": "Example 2"}, {"text": "My nam... | arize-ai/XLM-RoBERTa-xtreme-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme_en",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T21:23:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme_en #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLM-RoBERTa-xtreme-en
=====================
This model is a fine-tuned version of xlm-roberta-base on the xtreme\_en dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2838
* Accuracy: 0.9109
* F1: 0.7544
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme_en #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **QbertNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Bas... | {"library_name": "stable-baselines3", "tags": ["QbertNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "QbertNoFrameskip-v4", "type... | danieladejumo/dqn-QbertNoFrameskip-v4 | null | [
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-30T21:44:34+00:00 | [] | [] | TAGS
#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing QbertNoFrameskip-v4
This is a trained model of a DQN agent playing QbertNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## U... | [
"# DQN Agent playing QbertNoFrameskip-v4\nThis is a trained model of a DQN agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents incl... | [
"TAGS\n#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing QbertNoFrameskip-v4\nThis is a trained model of a DQN agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a tra... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# SepFormer trained on WHAM! for speech enhancement (16k sampling frequency)
This repository provides all the... | {"language": "en", "license": "apache-2.0", "tags": ["audio-to-audio", "Speech Enhancement", "WHAM!", "SepFormer", "Transformer", "pytorch", "speechbrain"], "datasets": ["WHAM!"], "metrics": ["SI-SNR", "PESQ"]} | speechbrain/sepformer-wham16k-enhancement | null | [
"speechbrain",
"audio-to-audio",
"Speech Enhancement",
"WHAM!",
"SepFormer",
"Transformer",
"pytorch",
"en",
"arxiv:2010.13154",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-30T22:05:07+00:00 | [
"2010.13154",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #audio-to-audio #Speech Enhancement #WHAM! #SepFormer #Transformer #pytorch #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
SepFormer trained on WHAM! for speech enhancement (16k sampling frequency)
==========================================================================
This repository provides all the necessary tools to perform speech enhancement (denoising) with a SepFormer model, implemented with SpeechBrain, and pretrained ... | [
"### Perform speech enhancement on your own audio file",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe training script is currently being worked on an ongoing pull-request.\n\n\nWe will update... | [
"TAGS\n#speechbrain #audio-to-audio #Speech Enhancement #WHAM! #SepFormer #Transformer #pytorch #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Perform speech enhancement on your own audio file",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_o... |
text2text-generation | transformers |
# Model Card of `lmqg/mbart-large-cc25-dequad-qg`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.co... | {"language": "de", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_dequad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Empfangs- und Sendeantenne sollen in ihrer Polarisation \u00fcbereinstimmen, ande... | research-backup/mbart-large-cc25-dequad-qg | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"question generation",
"de",
"dataset:lmqg/qg_dequad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T23:22:58+00:00 | [
"2210.03992"
] | [
"de"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #de #dataset-lmqg/qg_dequad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/mbart-large-cc25-dequad-qg'
===============================================
This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg\_dequad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: facebook/mbart-large-cc25
* Language... | [
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: de\n* Training data: lmqg/qg\\_dequad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #question generation #de #dataset-lmqg/qg_dequad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: de\n* Training data:... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLM-RoBERTa-xtreme-en-token-drift
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-ba... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme_en_token_drift"], "metrics": ["accuracy", "f1"], "widget": [{"text": "My name is Julia, I study at Imperial College, in London", "example_title": "Example 1"}, {"text": "My name is Sarah and I live in Paris", "example_title": "Example 2"}, {"te... | arize-ai/XLM-RoBERTa-xtreme-en-token-drift | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme_en_token_drift",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-30T23:35:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme_en_token_drift #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLM-RoBERTa-xtreme-en-token-drift
=================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme\_en\_token\_drift dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2802
* Accuracy: 0.9089
* F1: 0.7613
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme_en_token_drift #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln53")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln53")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln53 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-30T23:50:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ner
This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mon... | {"language": ["mn"], "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-ner", "results": []}]} | bayartsogt/roberta-base-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"mn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T00:15:27+00:00 | [] | [
"mn"
] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #mn #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-ner
================
This model is a fine-tuned version of bayartsogt/mongolian-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1328
* Precision: 0.9248
* Recall: 0.9325
* F1: 0.9286
* Accuracy: 0.9805
Model description
-----------------
More inf... | [
"### 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 #roberta #token-classification #generated_from_trainer #mn #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: 16\n*... |
text-generation | transformers | For the detail, see [github:mmdjiji/bert-chinese-idioms](https://github.com/mmdjiji/bert-chinese-idioms).
| {"license": "gpl-3.0"} | mmdjiji/gpt2-chinese-idioms | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T00:47:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| For the detail, see github:mmdjiji/bert-chinese-idioms.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becas-3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becas-3", "results": []}]} | Evelyn18/distilbert-base-uncased-becas-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T00:58:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becas-3
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 5.9817
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 20\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\... |
summarization | transformers |
# Randeng-Pegasus-238M-Summary-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理摘要任务，在数个中文摘要数据集上微调后的，中文版的PAGASUS-base。
Good at solving text summarization tasks, after fine-tuning on multiple Chine... | {"language": "zh", "tags": ["summarization", "chinese"], "inference": false} | IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"chinese",
"zh",
"arxiv:1912.08777",
"arxiv:2209.02970",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-07-01T01:02:17+00:00 | [
"1912.08777",
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #chinese #zh #arxiv-1912.08777 #arxiv-2209.02970 #autotrain_compatible #has_space #region-us
| Randeng-Pegasus-238M-Summary-Chinese
====================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理摘要任务，在数个中文摘要数据集上微调后的，中文版的PAGASUS-base。
Good at solving text summarization tasks, after fine-tuning on multiple Chinese text summarization d... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #chinese #zh #arxiv-1912.08777 #arxiv-2209.02970 #autotrain_compatible #has_space #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the re... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becas-4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becas-4", "results": []}]} | Evelyn18/distilbert-base-uncased-becas-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T01:20:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becas-4
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1357
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: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becas-5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becas-5", "results": []}]} | Evelyn18/distilbert-base-uncased-becas-5 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T01:29:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becas-5
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 4.8805
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #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: 3e-05\n* train\\_batch\\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-becas-6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "distilbert-base-uncased-becas-6", "results": []}]} | Evelyn18/distilbert-base-uncased-becas-6 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T01:37:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-becas-6
===============================
This model is a fine-tuned version of distilbert-base-uncased on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 4.4429
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: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-becasv2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
text2text-generation | transformers | # A Vietnamese-English Neural Machine Translation System
Our pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in [our paper](https://openre... | {} | vinai/vinai-translate-vi2en | null | [
"transformers",
"pytorch",
"tf",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-01T02:29:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| # A Vietnamese-English Neural Machine Translation System
Our pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in our paper:
@inprocee... | [
"# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in our paper:\n\n\n ... | [
"TAGS\n#transformers #pytorch #tf #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and Engli... |
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. -->
# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53
This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53", "results": []}]} | gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T02:42:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_singing3\_ft\_wav2vec2-large-xlsr-53
====================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing3\_ft\_wav2vec2-large-xlsr-53 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evaluation set... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #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: 5e-06\n* ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ner-demo
This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsog... | {"language": ["mn"], "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-ner-demo", "results": []}]} | bayartsogt/roberta-base-ner-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"mn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T02:49:12+00:00 | [] | [
"mn"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #token-classification #generated_from_trainer #mn #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-ner-demo
=====================
This model is a fine-tuned version of bayartsogt/mongolian-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0833
* Precision: 0.8885
* Recall: 0.9070
* F1: 0.8976
* Accuracy: 0.9752
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: 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #token-classification #generated_from_trainer #mn #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ner-demo
This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsog... | {"language": ["mn"], "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-ner-demo", "results": []}]} | Buyandelger/roberta-base-ner-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"mn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T02:49:28+00:00 | [] | [
"mn"
] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #mn #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-ner-demo
=====================
This model is a fine-tuned version of bayartsogt/mongolian-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0771
* Precision: 0.8802
* Recall: 0.8951
* F1: 0.8876
* Accuracy: 0.9798
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: 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #mn #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: 16\n*... |
text2text-generation | transformers | # A Vietnamese-English Neural Machine Translation System
Our pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in [our paper](https://openre... | {} | vinai/vinai-translate-en2vi | null | [
"transformers",
"pytorch",
"tf",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-01T03:17:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| # A Vietnamese-English Neural Machine Translation System
Our pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in our paper:
@inprocee... | [
"# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in our paper:\n\n\n ... | [
"TAGS\n#transformers #pytorch #tf #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and Engli... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1065437005
- CO2 Emissions (in grams): 1.854603770877255
## Validation Metrics
- Loss: 2.017435312271118
- Rouge1: 23.4405
- Rouge2: 10.6415
- RougeL: 23.1304
- RougeLsum: 23.0871
- Gen Len: 16.8351
## Usage
You can use cURL to access this ... | {"language": "ja", "tags": "autotrain", "datasets": ["kzkymn/autotrain-data-livedoor_news_summarization"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.854603770877255} | kzkymn/autotrain-livedoor_news_summarization-1065437005 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"ja",
"dataset:kzkymn/autotrain-data-livedoor_news_summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T03:52:31+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #ja #dataset-kzkymn/autotrain-data-livedoor_news_summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1065437005
- CO2 Emissions (in grams): 1.854603770877255
## Validation Metrics
- Loss: 2.017435312271118
- Rouge1: 23.4405
- Rouge2: 10.6415
- RougeL: 23.1304
- RougeLsum: 23.0871
- Gen Len: 16.8351
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1065437005\n- CO2 Emissions (in grams): 1.854603770877255",
"## Validation Metrics\n\n- Loss: 2.017435312271118\n- Rouge1: 23.4405\n- Rouge2: 10.6415\n- RougeL: 23.1304\n- RougeLsum: 23.0871\n- Gen Len: 16.8351",
"## Usage\n\nYou can... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #ja #dataset-kzkymn/autotrain-data-livedoor_news_summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID:... |
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. -->
# long-t5-local-base-finetuned
This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/lon... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "long-t5-local-base-finetuned", "results": []}]} | saekomdalkom/long-t5-local-base-finetuned | null | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T04:40:08+00:00 | [] | [] | TAGS
#transformers #pytorch #longt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| long-t5-local-base-finetuned
============================
This model is a fine-tuned version of google/long-t5-local-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 9.2722
* Rouge1: 3.8848
* Rouge2: 0.5914
* Rougel: 3.5038
* Rougelsum: 3.7022
* Gen Len: 19.0
Model desc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50000",
"### Trai... | [
"TAGS\n#transformers #pytorch #longt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 3\n... |
translation | transformers |
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1066237031
- CO2 Emissions (in grams): 30.068537136776726
## Validation Metrics
- Loss: 2.461327075958252
- SacreBLEU: 13.8452
- Gen len: 13.2313 | {"language": ["en", "hi"], "tags": ["autotrain", "translation"], "datasets": ["Tritkoman/autotrain-data-rusynpann"], "co2_eq_emissions": 30.068537136776726} | Tritkoman/EN-ROM | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"translation",
"en",
"hi",
"dataset:Tritkoman/autotrain-data-rusynpann",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T04:43:35+00:00 | [] | [
"en",
"hi"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #translation #en #hi #dataset-Tritkoman/autotrain-data-rusynpann #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1066237031
- CO2 Emissions (in grams): 30.068537136776726
## Validation Metrics
- Loss: 2.461327075958252
- SacreBLEU: 13.8452
- Gen len: 13.2313 | [
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1066237031\n- CO2 Emissions (in grams): 30.068537136776726",
"## Validation Metrics\n\n- Loss: 2.461327075958252\n- SacreBLEU: 13.8452\n- Gen len: 13.2313"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #translation #en #hi #dataset-Tritkoman/autotrain-data-rusynpann #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID:... |
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... | Matveic/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-01T05:00:53+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | dbarbedillo/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-01T05:33:30+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
image-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. -->
# YKXBCi/vit-base-patch16-224-in21k-ucSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "YKXBCi/vit-base-patch16-224-in21k-ucSat", "results": []}]} | YKXBCi/vit-base-patch16-224-in21k-ucSat | null | [
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T05:42:08+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| YKXBCi/vit-base-patch16-224-in21k-ucSat
=======================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.3216
* Train Accuracy: 0.9960
* Train Top-3-accuracy: 1.0
* Validati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'clas... |
sentence-similarity | sentence-transformers |
# svalabs/german-gpl-adapted-covid
This is a german on covid adapted [sentence-transformers](https://www.SBERT.net) model:
It is adapted on covid related documents using the [GPL](https://github.com/UKPLab/gpl) integration of [Haystack](https://github.com/deepset-ai/haystack). We used the [svalabs/cross-electra-ms-m... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | svalabs/german-gpl-adapted-covid | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T06:36:20+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# svalabs/german-gpl-adapted-covid
This is a german on covid adapted sentence-transformers model:
It is adapted on covid related documents using the GPL integration of Haystack. We used the svalabs/cross-electra-ms-marco-german-uncased as CrossEncoder and svalabs/mt5-large-german-query-gen-v1 for query generation.
... | [
"# svalabs/german-gpl-adapted-covid\n\nThis is a german on covid adapted sentence-transformers model: \nIt is adapted on covid related documents using the GPL integration of Haystack. We used the svalabs/cross-electra-ms-marco-german-uncased as CrossEncoder and svalabs/mt5-large-german-query-gen-v1 for query genera... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# svalabs/german-gpl-adapted-covid\n\nThis is a german on covid adapted sentence-transformers model: \nIt is adapted on covid related documents using the GPL integration... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1067437104
- CO2 Emissions (in grams): 29.54716889998106
## Validation Metrics
- Loss: 0.5487185120582581
- Rouge1: 77.4054
- Rouge2: 74.6166
- RougeL: 77.1503
- RougeLsum: 76.8399
- Gen Len: 42.0326
## Usage
You can use cURL to access this... | {"language": "en", "tags": "autotrain", "datasets": ["scaccomatto/autotrain-data-60-50"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 29.54716889998106} | scaccomatto/autotrain-60-50-1067437104 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"en",
"dataset:scaccomatto/autotrain-data-60-50",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T07:04:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-60-50 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1067437104
- CO2 Emissions (in grams): 29.54716889998106
## Validation Metrics
- Loss: 0.5487185120582581
- Rouge1: 77.4054
- Rouge2: 74.6166
- RougeL: 77.1503
- RougeLsum: 76.8399
- Gen Len: 42.0326
## Usage
You can use cURL to access this... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1067437104\n- CO2 Emissions (in grams): 29.54716889998106",
"## Validation Metrics\n\n- Loss: 0.5487185120582581\n- Rouge1: 77.4054\n- Rouge2: 74.6166\n- RougeL: 77.1503\n- RougeLsum: 76.8399\n- Gen Len: 42.0326",
"## Usage\n\nYou ca... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-60-50 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1067437104\n- CO2 Emissions (in grams):... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-triviaqa-finetuned-squad
This model is a fine-tuned version of [FabianWillner/bert-base-uncased-fine... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-finetuned-triviaqa-finetuned-squad", "results": []}]} | FabianWillner/bert-base-uncased-finetuned-triviaqa-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T07:29:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-triviaqa-finetuned-squad
====================================================
This model is a fine-tuned version of FabianWillner/bert-base-uncased-finetuned-triviaqa on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9981
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 #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1067537149
- CO2 Emissions (in grams): 0.11973630108906597
## Validation Metrics
- Loss: 0.4912683367729187
- Rouge1: 80.211
- Rouge2: 77.7552
- RougeL: 79.5359
- RougeLsum: 79.7243
- Gen Len: 87.0
## Usage
You can use cURL to access this m... | {"language": "en", "tags": "autotrain", "datasets": ["scaccomatto/autotrain-data-120-50"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.11973630108906597} | scaccomatto/autotrain-120-50-1067537149 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"en",
"dataset:scaccomatto/autotrain-data-120-50",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T07:34:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-120-50 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1067537149
- CO2 Emissions (in grams): 0.11973630108906597
## Validation Metrics
- Loss: 0.4912683367729187
- Rouge1: 80.211
- Rouge2: 77.7552
- RougeL: 79.5359
- RougeLsum: 79.7243
- Gen Len: 87.0
## Usage
You can use cURL to access this m... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1067537149\n- CO2 Emissions (in grams): 0.11973630108906597",
"## Validation Metrics\n\n- Loss: 0.4912683367729187\n- Rouge1: 80.211\n- Rouge2: 77.7552\n- RougeL: 79.5359\n- RougeLsum: 79.7243\n- Gen Len: 87.0",
"## Usage\n\nYou can ... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-120-50 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1067537149\n- CO2 Emissions (in grams)... |
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. -->
# finetuned-mt5-small-10epoch
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["translation", "wmt16", "Lvxue"], "datasets": ["wmt16"], "metrics": ["sacrebleu", "bleu"], "model-index": [{"name": "Lvxue/finetuned-mt5-small-10epoch", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16", "type": ... | Lvxue/finetuned-mt5-small-10epoch | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"translation",
"wmt16",
"Lvxue",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T07:41:13+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #translation #wmt16 #Lvxue #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# finetuned-mt5-small-10epoch
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: 1.7274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation ... | [
"# finetuned-mt5-small-10epoch\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: 1.7274",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #wmt16 #Lvxue #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# finetuned-mt5-small-10epoch\n\nThis model is a fine-tuned version of google/mt5-smal... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1067937173
- CO2 Emissions (in grams): 0.08625442844190523
## Validation Metrics
- Loss: 0.502437174320221
- Rouge1: 83.7457
- Rouge2: 81.1714
- RougeL: 83.2649
- RougeLsum: 83.3018
- Gen Len: 78.7059
## Usage
You can use cURL to access thi... | {"language": "en", "tags": "autotrain", "datasets": ["scaccomatto/autotrain-data-120-0"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.08625442844190523} | scaccomatto/autotrain-120-0-1067937173 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"en",
"dataset:scaccomatto/autotrain-data-120-0",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T07:59:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-120-0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1067937173
- CO2 Emissions (in grams): 0.08625442844190523
## Validation Metrics
- Loss: 0.502437174320221
- Rouge1: 83.7457
- Rouge2: 81.1714
- RougeL: 83.2649
- RougeLsum: 83.3018
- Gen Len: 78.7059
## Usage
You can use cURL to access thi... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1067937173\n- CO2 Emissions (in grams): 0.08625442844190523",
"## Validation Metrics\n\n- Loss: 0.502437174320221\n- Rouge1: 83.7457\n- Rouge2: 81.1714\n- RougeL: 83.2649\n- RougeLsum: 83.3018\n- Gen Len: 78.7059",
"## Usage\n\nYou c... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-120-0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1067937173\n- CO2 Emissions (in grams):... |
token-classification | transformers |
# CKIP BERT Base Han Chinese WS
This model provides word segmentation for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.
## Homepage
* [ckiplab/han-transformers](https://github.com/ckiplab/han-transformers)
## Training Datasets
The copyright of the datasets belongs to t... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"} | ckiplab/bert-base-han-chinese-ws | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T08:13:43+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# CKIP BERT Base Han Chinese WS
This model provides word segmentation for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.
## Homepage
* ckiplab/han-transformers
## Training Datasets
The copyright of the datasets belongs to the Institute of Linguistics, Academia Sinica.
*... | [
"# CKIP BERT Base Han Chinese WS\n\nThis model provides word segmentation for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.",
"## Homepage\n* ckiplab/han-transformers",
"## Training Datasets\nThe copyright of the datasets belongs to the Institute of Linguistics, Ac... | [
"TAGS\n#transformers #pytorch #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CKIP BERT Base Han Chinese WS\n\nThis model provides word segmentation for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.",
... |
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/1320863459953750016/NlmH... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tacticalmaid-the_ironsheik/1656668488177/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tacticalmaid-the_ironsheik | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-01T08:39:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
The Iron Sheik & Maid POLadin
@tacticalmaid-the\_ironsheik
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... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1068537269
- CO2 Emissions (in grams): 19.045065953636296
## Validation Metrics
- Loss: 0.42951640486717224
- Rouge1: 85.4322
- Rouge2: 82.999
- RougeL: 84.8782
- RougeLsum: 85.1256
- Gen Len: 169.2895
## Usage
You can use cURL to access th... | {"language": "en", "tags": "autotrain", "datasets": ["scaccomatto/autotrain-data-260-0"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 19.045065953636296} | scaccomatto/autotrain-260-0-1068537269 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"en",
"dataset:scaccomatto/autotrain-data-260-0",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-01T08:53:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-260-0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1068537269
- CO2 Emissions (in grams): 19.045065953636296
## Validation Metrics
- Loss: 0.42951640486717224
- Rouge1: 85.4322
- Rouge2: 82.999
- RougeL: 84.8782
- RougeLsum: 85.1256
- Gen Len: 169.2895
## Usage
You can use cURL to access th... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1068537269\n- CO2 Emissions (in grams): 19.045065953636296",
"## Validation Metrics\n\n- Loss: 0.42951640486717224\n- Rouge1: 85.4322\n- Rouge2: 82.999\n- RougeL: 84.8782\n- RougeLsum: 85.1256\n- Gen Len: 169.2895",
"## Usage\n\nYou ... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #en #dataset-scaccomatto/autotrain-data-260-0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1068537269\n- CO2 Emissions (in grams):... |
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