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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | # Fake News Classification
# Dependencies
The project requires Python 3.6 and the latest version of PyTorch
The models were trained on Kaggle kernels with a GPU
# Data
The dataset consists of fake and true articles.
# Code
All the solution and notebook files (with cell outputs) are provided.
# Run
Use th... | {} | wasifa/fake_news_classifier | null | [
"region:us"
] | null | 2022-04-08T01:38:25+00:00 | [] | [] | TAGS
#region-us
| # Fake News Classification
# Dependencies
The project requires Python 3.6 and the latest version of PyTorch
The models were trained on Kaggle kernels with a GPU
# Data
The dataset consists of fake and true articles.
# Code
All the solution and notebook files (with cell outputs) are provided.
# Run
Use th... | [
"# Fake News Classification",
"# Dependencies\n\nThe project requires Python 3.6 and the latest version of PyTorch \n\nThe models were trained on Kaggle kernels with a GPU",
"# Data\n\nThe dataset consists of fake and true articles.",
"# Code\n\nAll the solution and notebook files (with cell outputs) are pro... | [
"TAGS\n#region-us \n",
"# Fake News Classification",
"# Dependencies\n\nThe project requires Python 3.6 and the latest version of PyTorch \n\nThe models were trained on Kaggle kernels with a GPU",
"# Data\n\nThe dataset consists of fake and true articles.",
"# Code\n\nAll the solution and notebook files (w... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds-sample-gpt-small-neo-10epoch1
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-sample-gpt-small-neo-10epoch1", "results": []}]} | Pavithra/codeparrot-ds-sample-gpt-small-neo-10epoch1 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T02:04:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| codeparrot-ds-sample-gpt-small-neo-10epoch1
===========================================
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5696
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-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: 0.0005\n* train\\_batch\\... |
null | null |
# Fatima Challenge
- Dataset: I'm using the `cats_vs_dogs` dataset from **kaggle** instead of huggingface `datasets` since `cats_vs_dogs` in `datasets` have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into `train` and `test` folders.
- Model: I'... | {"license": "mit"} | thainamhoang/resnet50_fatima_challenge | null | [
"license:mit",
"region:us"
] | null | 2022-04-08T03:01:50+00:00 | [] | [] | TAGS
#license-mit #region-us
|
# Fatima Challenge
- Dataset: I'm using the 'cats_vs_dogs' dataset from kaggle instead of huggingface 'datasets' since 'cats_vs_dogs' in 'datasets' have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into 'train' and 'test' folders.
- Model: I'm us... | [
"# Fatima Challenge\n\n- Dataset: I'm using the 'cats_vs_dogs' dataset from kaggle instead of huggingface 'datasets' since 'cats_vs_dogs' in 'datasets' have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into 'train' and 'test' folders.\n- Model:... | [
"TAGS\n#license-mit #region-us \n",
"# Fatima Challenge\n\n- Dataset: I'm using the 'cats_vs_dogs' dataset from kaggle instead of huggingface 'datasets' since 'cats_vs_dogs' in 'datasets' have checksum error. The original dataset is from Microsoft. However, some files are corrupted and it hasn't been divided into... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bert_Test
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "accuracy", "f1"], "model-index": [{"name": "Bert_Test", "results": []}]} | NoCaptain/BERT_Base_Finetuned_C19Vax | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T03:39:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Bert\_Test
==========
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1965
* Precision: 0.9332
* Accuracy: 0.9223
* F1: 0.9223
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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. -->
# librispeech-100h-supervised-meta
This model is a fine-tuned version of [Kuray107/librispeech-5h-supervised](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "librispeech-100h-supervised-meta", "results": []}]} | Kuray107/librispeech-100h-supervised-meta | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T04:15:08+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| librispeech-100h-supervised-meta
================================
This model is a fine-tuned version of Kuray107/librispeech-5h-supervised on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0965
* Wer: 0.0330
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #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: 32\n* eval\\_b... |
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/1641418276/tumblr_lule5c... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/onlinepete-utilitylimb/1649400369339/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/onlinepete-utilitylimb | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T05:45:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
bandit & im pete online
@onlinepete-utilitylimb
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.... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-indobert-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilled-indobert-classification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args... | afbudiman/distilled-indobert-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:indonlu",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T05:49:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-indonlu #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilled-indobert-classification
=================================
This model is a fine-tuned version of distilbert-base-uncased on the indonlu dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6015
* Accuracy: 0.9016
* F1: 0.9015
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-indonlu #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... |
token-classification | transformers | ## Model Description
Fine-tuning of [XLM-RoBERTa-Uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model on [synthetic morphological dataset](https://huggingface.co/datasets/ukr-models/Ukr-Synth), returns both UPOS and morphological features (joined by double underscore symbol)
## How to Use
Huggingface pipe... | {"language": ["uk"], "license": "mit", "tags": ["ukrainian"], "widget": [{"text": "\u041c\u043e\u0433\u0438\u043b\u0430 \u0422\u0430\u0440\u0430\u0441\u0430 \u0428\u0435\u0432\u0447\u0435\u043d\u043a\u0430 \u2014 \u043c\u0456\u0441\u0446\u0435 \u043f\u043e\u0445\u043e\u0432\u0430\u043d\u043d\u044f \u0432\u0438\u0434\u0... | ukr-models/uk-morph | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"ukrainian",
"uk",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T06:14:02+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## Model Description
Fine-tuning of XLM-RoBERTa-Uk model on synthetic morphological dataset, returns both UPOS and morphological features (joined by double underscore symbol)
## How to Use
Huggingface pipeline way (returns tokens with labels):
If you wish to get predictions split by words, not by tokens, you may us... | [
"## Model Description\nFine-tuning of XLM-RoBERTa-Uk model on synthetic morphological dataset, returns both UPOS and morphological features (joined by double underscore symbol)",
"## How to Use\n\nHuggingface pipeline way (returns tokens with labels):\n\n\nIf you wish to get predictions split by words, not by tok... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #token-classification #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Description\nFine-tuning of XLM-RoBERTa-Uk model on synthetic morphological dataset, returns both UPOS and morphological features (joined ... |
null | null | This is the repository for the upside-down image classification model.
It is a torch model | {} | bebouky/flipvision | null | [
"region:us"
] | null | 2022-04-08T06:15:50+00:00 | [] | [] | TAGS
#region-us
| This is the repository for the upside-down image classification model.
It is a torch model | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-sst2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the glue datas... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args": "sst2"}, "metrics": [{"type":... | philschmid/roberta-large-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T06:27:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-sst2
==================
This model is a fine-tuned version of roberta-large on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1400
* Accuracy: 0.9644
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: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 256\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3... |
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-child-en-tokenizer-4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-child-en-tokenizer-4", "results": []}]} | jaeyeon/wav2vec2-child-en-tokenizer-4 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T06:33:44+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-child-en-tokenizer-4
=============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4709
* Wer: 0.3769
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 24\n* eval\\_b... |
text2text-generation | transformers |
# T5 for Generative Question Answering
This model is the result produced by Christian Di Maio and Giacomo Nunziati for the Language Processing Technologies exam.
Reference for [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [DuoRC](https://huggingface.co/dataset... | {"language": "en", "tags": ["Generative Question Answering"], "datasets": ["duorc"], "widget": [{"text": "question: Is Giacomo Italian? context: Giacomo is 25 years old and he was born in Tuscany"}, {"text": "question: Where does Christian come from? context: Christian is a student of UNISI but he come from Caserta"}, ... | MaRiOrOsSi/t5-base-finetuned-question-answering | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"Generative Question Answering",
"en",
"dataset:duorc",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T06:36:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #Generative Question Answering #en #dataset-duorc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5 for Generative Question Answering
====================================
This model is the result produced by Christian Di Maio and Giacomo Nunziati for the Language Processing Technologies exam.
Reference for Google's T5 fine-tuned on DuoRC for Generative Question Answering by just prepending the *question* to the ... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #Generative Question Answering #en #dataset-duorc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
reinforcement-learning | transformers |
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=879.00 +/- 327.25983560467665
## Usage (with ... | {"tags": ["SpaceInvadersNoFrameskip-v4", "reinforcement-learning"]} | osanseviero/TEST_VM_ppo-SpaceInvadersNoFrameskip-v43 | null | [
"transformers",
"SpaceInvadersNoFrameskip-v4",
"reinforcement-learning",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T07:09:58+00:00 | [] | [] | TAGS
#transformers #SpaceInvadersNoFrameskip-v4 #reinforcement-learning #model-index #endpoints_compatible #region-us
|
# PPO Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.
## Evaluation Results
mean_reward=879.00 +/- 327.25983560467665
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\n This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.\n\n ## Evaluation Results\n \n mean_reward=879.00 +/- 327.25983560467665\n \n ## Usage (with Stable-baselines3)\n\n TODO: Add your code"
] | [
"TAGS\n#transformers #SpaceInvadersNoFrameskip-v4 #reinforcement-learning #model-index #endpoints_compatible #region-us \n",
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\n This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.\n\n ## Evaluation Result... |
null | transformers |
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=1050.00 +/- 350.1642471755219
## Usage (with ... | {"tags": ["SpaceInvadersNoFrameskip-v4"]} | osanseviero/TEST_VM_ppo-SpaceInvadersNoFrameskip-v44 | null | [
"transformers",
"SpaceInvadersNoFrameskip-v4",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T07:16:44+00:00 | [] | [] | TAGS
#transformers #SpaceInvadersNoFrameskip-v4 #endpoints_compatible #region-us
|
# PPO Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.
## Evaluation Results
mean_reward=1050.00 +/- 350.1642471755219
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\n This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.\n\n ## Evaluation Results\n \n mean_reward=1050.00 +/- 350.1642471755219\n \n ## Usage (with Stable-baselines3)\n\n TODO: Add your code"
] | [
"TAGS\n#transformers #SpaceInvadersNoFrameskip-v4 #endpoints_compatible #region-us \n",
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\n This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library.\n\n ## Evaluation Results\n \n mean_reward=1050.00 +/- 350.... |
question-answering | transformers | # RoBERTa base Japanese - JaQuAD
## Description
A Japanese Question Answering model fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD).
Please refer [RoBERTa base Japanese](https://huggingface.co/rinna/japanese-roberta-base) for details about the pre-training model.
The codes for the fine-tun... | {"language": "ja", "license": "cc-by-sa-3.0", "tags": ["question-answering", "extractive-qa"], "datasets": ["SkelterLabsInc/JaQuAD"], "metrics": ["Exact match", "F1 score"], "pipeline_tag": ["None"]} | ybelkada/japanese-roberta-question-answering | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"extractive-qa",
"ja",
"dataset:SkelterLabsInc/JaQuAD",
"license:cc-by-sa-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T07:52:22+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #question-answering #extractive-qa #ja #dataset-SkelterLabsInc/JaQuAD #license-cc-by-sa-3.0 #endpoints_compatible #region-us
| # RoBERTa base Japanese - JaQuAD
## Description
A Japanese Question Answering model fine-tuned on JaQuAD.
Please refer RoBERTa base Japanese for details about the pre-training model.
The codes for the fine-tuning are available on this notebook
## Usage
## License
The fine-tuned model is licensed under the CC BY-SA 3... | [
"# RoBERTa base Japanese - JaQuAD",
"## Description\nA Japanese Question Answering model fine-tuned on JaQuAD.\nPlease refer RoBERTa base Japanese for details about the pre-training model.\nThe codes for the fine-tuning are available on this notebook",
"## Usage",
"## License\n\nThe fine-tuned model is licens... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #extractive-qa #ja #dataset-SkelterLabsInc/JaQuAD #license-cc-by-sa-3.0 #endpoints_compatible #region-us \n",
"# RoBERTa base Japanese - JaQuAD",
"## Description\nA Japanese Question Answering model fine-tuned on JaQuAD.\nPlease refer RoBERTa base Japan... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-test-headline
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-test-headline", "results": []}]} | lucypallent/distilbert-base-uncased-finetuned-test-headline | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T09:22:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-test-headline
===============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0992
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
null | null |
# Graphcore/wav2vec2-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphc... | {"license": "apache-2.0"} | Graphcore/wav2vec2-base-ipu | null | [
"optimum_graphcore",
"arxiv:2006.11477",
"license:apache-2.0",
"region:us"
] | null | 2022-04-08T09:45:29+00:00 | [
"2006.11477"
] | [] | TAGS
#optimum_graphcore #arxiv-2006.11477 #license-apache-2.0 #region-us
|
# Graphcore/wav2vec2-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphc... | [
"# Graphcore/wav2vec2-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | [
"TAGS\n#optimum_graphcore #arxiv-2006.11477 #license-apache-2.0 #region-us \n",
"# Graphcore/wav2vec2-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of perf... |
text-classification | transformers |
Objectivity sentence classification model based on **distilbert-base-uncased-finetuned-sst-2-english**. It was fine-tuned with Rotten-IMDB movie review [data](http://www.cs.cornell.edu/people/pabo/movie-review-data/) using extracted sentences from film plots as objective examples and review comments as subjective lang... | {"license": "gpl-3.0"} | marcosfp/distilbert-base-uncased-finetuned-objectivity-rotten | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T09:59:03+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
Objectivity sentence classification model based on distilbert-base-uncased-finetuned-sst-2-english. It was fine-tuned with Rotten-IMDB movie review data using extracted sentences from film plots as objective examples and review comments as subjective language examples.
With a test set of 5%, we obtained an accuracy o... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #license-gpl-3.0 #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/1317183233495388160/nLbB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/emarobot/1649416424059/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/emarobot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T10:12:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
3bkreno
@emarobot
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"
] |
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. -->
# ft-tatoeba-ar-en
This model was trained from scratch on the open_subtitles dataset.
## Model description
More information need... | {"tags": ["translation", "generated_from_trainer"], "datasets": ["open_subtitles"], "model-index": [{"name": "ft-tatoeba-ar-en", "results": []}]} | abdusah/ft-tatoeba-ar-en | null | [
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:open_subtitles",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T10:49:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-open_subtitles #autotrain_compatible #endpoints_compatible #region-us
|
# ft-tatoeba-ar-en
This model was trained from scratch on the open_subtitles dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following ... | [
"# ft-tatoeba-ar-en\n\nThis model was trained from scratch on the open_subtitles dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training h... | [
"TAGS\n#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-open_subtitles #autotrain_compatible #endpoints_compatible #region-us \n",
"# ft-tatoeba-ar-en\n\nThis model was trained from scratch on the open_subtitles dataset.",
"## Model description\n\n... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Dutch-Large-ft-CGN
A Dutch Wav2Vec2 model. This model is created by further pre-training the original English [`facebook/wav2vec2-large`](https://huggingface.co/facebook/wav2vec2-large) model on Dutch speech from [Het Corpus Gesproken Nederlands](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gespro... | {"language": "nl", "tags": ["speech"]} | GroNLP/wav2vec2-dutch-large-ft-cgn | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"nl",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T11:21:08+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #speech #nl #endpoints_compatible #region-us
|
# Wav2Vec2-Dutch-Large-ft-CGN
A Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-large' model on Dutch speech from Het Corpus Gesproken Nederlands. Subsequently, the model is fine-tuned on the same Dutch speech using CTC. | [
"# Wav2Vec2-Dutch-Large-ft-CGN\n\nA Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-large' model on Dutch speech from Het Corpus Gesproken Nederlands. Subsequently, the model is fine-tuned on the same Dutch speech using CTC."
] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #speech #nl #endpoints_compatible #region-us \n",
"# Wav2Vec2-Dutch-Large-ft-CGN\n\nA Dutch Wav2Vec2 model. This model is created by further pre-training the original English 'facebook/wav2vec2-large' model on Dutch speech from Het... |
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-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | kiana/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T11:36:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4088
Model description
-----------------
More information needed
Intended u... | [
"### 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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #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\... |
text-classification | transformers | Fine-tuned KB-BERT for Swedish Riksdag introductions | {} | jesperjmb/parlaBERT | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T11:40:08+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Fine-tuned KB-BERT for Swedish Riksdag introductions | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-ft-CGN
This model is created by fine-tuning the [`facebook/wav2vec2-large-xlsr-53`](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) model on Dutch speech from [Het Corpus Gesproken Nederlands](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/) using CTC. | {"language": "nl", "tags": ["speech"]} | GroNLP/wav2vec2-large-xlsr-53-ft-cgn | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"nl",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T11:40:18+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #nl #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-ft-CGN
This model is created by fine-tuning the 'facebook/wav2vec2-large-xlsr-53' model on Dutch speech from Het Corpus Gesproken Nederlands using CTC. | [
"# Wav2Vec2-Large-XLSR-53-ft-CGN\n\nThis model is created by fine-tuning the 'facebook/wav2vec2-large-xlsr-53' model on Dutch speech from Het Corpus Gesproken Nederlands using CTC."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #nl #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-ft-CGN\n\nThis model is created by fine-tuning the 'facebook/wav2vec2-large-xlsr-53' model on Dutch speech from Het Corpus Gesproken Nederlands using CTC."
] |
fill-mask | transformers |
# Biomedical language model for Spanish
## 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)
- [Training](#training)
- [Tokenization an... | {"language": ["es"], "license": "apache-2.0", "tags": ["biomedical", "clinical", "spanish"], "metrics": ["ppl"], "widget": [{"text": "El \u00fanico antecedente personal a rese\u00f1ar era la <mask> arterial."}, {"text": "Las radiolog\u00edas \u00f3seas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones v... | PlanTL-GOB-ES/bsc-bio-es | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"biomedical",
"clinical",
"spanish",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T12:15:24+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #fill-mask #biomedical #clinical #spanish #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Biomedical language model for Spanish
=====================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Tokenization and model pretraining
+ Training corpora and preprocessing
* Evaluation... | [
"### Tokenization and model pretraining\n\n\nThis model is a RoBERTa-based model trained on a\nbiomedical corpus in Spanish collected from several sources (see next section).\nThe training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE)\nused in the original RoBERTA model with a vocabular... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #biomedical #clinical #spanish #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Tokenization and model pretraining\n\n\nThis model is a RoBERTa-based model trained on a\nbiomedical corpus in Spanish collected from several so... |
fill-mask | transformers |
# Biomedical-clinical language model for Spanish
## 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)
- [Training](#training)
- [Evaluati... | {"language": ["es"], "license": "apache-2.0", "tags": ["biomedical", "clinical", "ehr", "spanish"], "metrics": ["ppl"], "widget": [{"text": "El \u00fanico antecedente personal a rese\u00f1ar era la <mask> arterial."}, {"text": "Las radiolog\u00edas \u00f3seas de cuerpo entero no detectan alteraciones <mask>, ni alterac... | PlanTL-GOB-ES/bsc-bio-ehr-es | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"biomedical",
"clinical",
"ehr",
"spanish",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-08T12:15:59+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #fill-mask #biomedical #clinical #ehr #spanish #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Biomedical-clinical language model for Spanish
==============================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
* Evaluation
* Additional information
+ Author
+ Contact information... | [
"### Tokenization and model pretraining\n\n\nThis model is a RoBERTa-based model trained on a\nbiomedical-clinical corpus in Spanish collected from several sources (see next section).\nThe training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE)\nused in the original RoBERTA model with a ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #biomedical #clinical #ehr #spanish #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Tokenization and model pretraining\n\n\nThis model is a RoBERTa-based model trained on a\nbiomedical-clinical corpus in Spanish ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-ner-v1
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-ch... | {"tags": ["generated_from_trainer"], "datasets": ["fdner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-chinese-finetuned-ner-v1", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "fdner", "type": "fdner", "args": "f... | leonadase/bert-base-chinese-finetuned-ner-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:fdner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T12:26:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-fdner #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-chinese-finetuned-ner-v1
==================================
This model is a fine-tuned version of bert-base-chinese on the fdner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0413
* Precision: 0.9812
* Recall: 0.9886
* F1: 0.9849
* Accuracy: 0.9910
Model description
-------... | [
"### 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: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-fdner #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\... |
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. -->
# MiniLMv2-L12-H384-sst2
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value... | philschmid/MiniLMv2-L12-H384-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T12:38:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L12-H384-sst2
======================
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2195
* Accuracy: 0.9209
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 256\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
text2text-generation | transformers |
# KoBART를 활용한 질문 생성 관련 Multitasking
Based on [kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2). You can see the notebook on [Kaggle](https://www.kaggle.com/rycont/koquestionbart)
한국어 문단에서 의미있는 질문을 생성하기 위해 다음과 같은 태스크를 멀티태스크로 학습한 모델입니다.
- 문단에서 답변이 될 수 있는 키워드 추출
- 키워드를 답변으로 할 수 있는 문장 생성
## 사용 방법
### 키워드 추... | {"language": ["ko"], "license": "gpl", "tags": ["KoBART", "BART", "Korean", "QG", "Question", "KorQuad"], "datasets": ["AIR/korquad"], "widget": [{"text": "\ud0a4\uc6cc\ub4dc \ucd94\ucd9c: 5<unused1>1943\ub144 10\uc6d4 \ub2f9\uc2dc, \ubc18\uc751\ub85c B\ub294 \ucd08\uae30 \uac00\ub3d9\uc5d0\uc11c 250 MW\uc758 \uc804\ub... | rycont/KoQuestionBART | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"KoBART",
"BART",
"Korean",
"QG",
"Question",
"KorQuad",
"ko",
"dataset:AIR/korquad",
"license:gpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T12:47:35+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #KoBART #BART #Korean #QG #Question #KorQuad #ko #dataset-AIR/korquad #license-gpl #autotrain_compatible #endpoints_compatible #region-us
|
# KoBART를 활용한 질문 생성 관련 Multitasking
Based on kobart-base-v2. You can see the notebook on Kaggle
한국어 문단에서 의미있는 질문을 생성하기 위해 다음과 같은 태스크를 멀티태스크로 학습한 모델입니다.
- 문단에서 답변이 될 수 있는 키워드 추출
- 키워드를 답변으로 할 수 있는 문장 생성
## 사용 방법
### 키워드 추출
입력
> [키워드 갯수]\<unused1>[문단]
출력
> [키워드1]\<unused2>[키워드1]\<unused2>[키워드n...
### 질문 생성
입력
> ... | [
"# KoBART를 활용한 질문 생성 관련 Multitasking\nBased on kobart-base-v2. You can see the notebook on Kaggle\n\n한국어 문단에서 의미있는 질문을 생성하기 위해 다음과 같은 태스크를 멀티태스크로 학습한 모델입니다.\n- 문단에서 답변이 될 수 있는 키워드 추출\n- 키워드를 답변으로 할 수 있는 문장 생성",
"## 사용 방법",
"### 키워드 추출\n입력\n> [키워드 갯수]\\<unused1>[문단]\n \n출력\n> [키워드1]\\<unused2>[키워드1]\\<unused2... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #KoBART #BART #Korean #QG #Question #KorQuad #ko #dataset-AIR/korquad #license-gpl #autotrain_compatible #endpoints_compatible #region-us \n",
"# KoBART를 활용한 질문 생성 관련 Multitasking\nBased on kobart-base-v2. You can see the notebook on Kaggle\n\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-recipe-ar
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-recipe-ar", "results": []}]} | edwardjross/xlm-roberta-base-finetuned-recipe-ar | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T12:53:55+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-recipe-ar
====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0529
* F1: 0.9856
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\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. -->
# MiniLMv2-L6-H384-sst2
This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://hug... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L6-H384-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value"... | philschmid/MiniLMv2-L6-H384-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T12:54:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L6-H384-sst2
=====================
This model is a fine-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2532
* Accuracy: 0.9197
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 256\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
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. -->
# MiniLMv2-L6-H768-sst2
This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large](https://hug... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L6-H768-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value"... | philschmid/MiniLMv2-L6-H768-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T12:54:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L6-H768-sst2
=====================
This model is a fine-tuned version of nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2013
* Accuracy: 0.9427
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 256\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
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-recipe-gk
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-recipe-gk", "results": []}]} | edwardjross/xlm-roberta-base-finetuned-recipe-gk | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T12:57:12+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-recipe-gk
====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1505
* F1: 0.9536
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-recipe-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "widget": [{"text": "1 sheet of frozen puff pastry (thawed)"}, {"text": "1/2 teaspoon fresh thyme, minced"}, {"text": "2-3 medium tomatoes"}, {"text": "1 petit oignon rouge"}], "model-index": [{"name": "xlm-roberta-base-finetuned-recipe-all", "re... | edwardjross/xlm-roberta-base-finetuned-recipe-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"arxiv:2004.12184",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-08T13:01:31+00:00 | [
"2004.12184"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #arxiv-2004.12184 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-recipe-all
=====================================
This model is a fine-tuned version of xlm-roberta-base on the recipe ingredient NER dataset from the paper A Named Entity Based Approach to Model Recipes (using both the 'gk' and 'ar' datasets).
It achieves the following results on the eval... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #arxiv-2004.12184 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
null | null | ## Description
SqueezeNet from PyTorch-zoo, pretrained with ImageNet and fine-tuned with scenic dataset from kaggle https://www.kaggle.com/datasets/arnaud58/landscape-pictures
## Results
Trained with 8K samples, tested with 120++ non-overlapping samples.
Accuracy: 0.978261
f1-score: 0.978417
| {"license": "afl-3.0"} | iceboy95/SqueezeNet_VisionQ1_20220512 | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-04-08T13:09:42+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| ## Description
SqueezeNet from PyTorch-zoo, pretrained with ImageNet and fine-tuned with scenic dataset from kaggle URL
## Results
Trained with 8K samples, tested with 120++ non-overlapping samples.
Accuracy: 0.978261
f1-score: 0.978417
| [
"## Description\nSqueezeNet from PyTorch-zoo, pretrained with ImageNet and fine-tuned with scenic dataset from kaggle URL",
"## Results\nTrained with 8K samples, tested with 120++ non-overlapping samples. \n\nAccuracy: 0.978261\n\nf1-score: 0.978417"
] | [
"TAGS\n#license-afl-3.0 #region-us \n",
"## Description\nSqueezeNet from PyTorch-zoo, pretrained with ImageNet and fine-tuned with scenic dataset from kaggle URL",
"## Results\nTrained with 8K samples, tested with 120++ non-overlapping samples. \n\nAccuracy: 0.978261\n\nf1-score: 0.978417"
] |
question-answering | transformers | python run_squad.py \
--model_name_or_path google/canine-c \
--do_train \
--do_eval \
--per_gpu_train_batch_size 1 \
--per_gpu_eval_batch_size 1 \
--gradient_accumulation_steps 128 \
--learning_rate 3e-5 \
--num_train_epochs 3 \
--max_seq_length 1024 \
--doc_stride 128 \
--max_answer_length 240 \... | {} | Splend1dchan/canine-c-squad | null | [
"transformers",
"pytorch",
"safetensors",
"canine",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T13:16:41+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #canine #question-answering #endpoints_compatible #region-us
| python run_squad.py \
--model_name_or_path google/canine-c \
--do_train \
--do_eval \
--per_gpu_train_batch_size 1 \
--per_gpu_eval_batch_size 1 \
--gradient_accumulation_steps 128 \
--learning_rate 3e-5 \
--num_train_epochs 3 \
--max_seq_length 1024 \
--doc_stride 128 \
--max_answer_length 240 \... | [] | [
"TAGS\n#transformers #pytorch #safetensors #canine #question-answering #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. -->
# TSC_finetuning-sentiment-movie-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dist... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "TSC_finetuning-sentiment-movie-model", "results": []}]} | malcolm/TSC_finetuning-sentiment-movie-model | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T13:33:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TSC_finetuning-sentiment-movie-model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1480
- Accuracy: 0.9578
- F1: 0.9757
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# TSC_finetuning-sentiment-movie-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1480\n- Accuracy: 0.9578\n- F1: 0.9757",
"## Model description\n\nMore information needed",
"## Intended uses & limit... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TSC_finetuning-sentiment-movie-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achi... |
null | null | # 1. Deep Learning for Vision
</p>
Upside down detector: Train a model to detect if images are upside down
* Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub)
* Synthetically turn some of the images upside down. Create a training and test set.
* Build a neural network (using Ten... | {} | MeerAnwar/CodingChallengeFatimaFellowship | null | [
"region:us"
] | null | 2022-04-08T13:35:45+00:00 | [] | [] | TAGS
#region-us
| # 1. Deep Learning for Vision
</p>
Upside down detector: Train a model to detect if images are upside down
* Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub)
* Synthetically turn some of the images upside down. Create a training and test set.
* Build a neural network (using Ten... | [
"# 1. Deep Learning for Vision\n</p>\nUpside down detector: Train a model to detect if images are upside down\n\n* Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub)\n* Synthetically turn some of the images upside down. Create a training and test set.\n* Build a neural network... | [
"TAGS\n#region-us \n",
"# 1. Deep Learning for Vision\n</p>\nUpside down detector: Train a model to detect if images are upside down\n\n* Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub)\n* Synthetically turn some of the images upside down. Create a training and test set.\... |
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. -->
# FakevsRealNews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "FakevsRealNews", "results": []}]} | Shadman-Rohan/FakevsRealNews | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T13:37:19+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| FakevsRealNews
==============
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Accuracy: 1.0
* F1: 1.0
* Precision: 1.0
* Recall: 1.0
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #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: 5e-05\n* train\\_batch\\_size: ... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1445263525878902787/yW8p... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lilpeeplyric/1649430909105/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lilpeeplyric | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T14:14:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
lil peep lyrics bot
@lilpeeplyric
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# avialfont/dummy-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "avialfont/dummy-finetuned-amazon-en-es", "results": []}]} | avialfont/dummy-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T14:20:54+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| avialfont/dummy-finetuned-amazon-en-es
======================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.6755
* Validation Loss: 3.8033
* Epoch: 2
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 3627, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
text-generation | transformers |
# InCoder 1B
A 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.
The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or ... | {"license": "cc-by-nc-4.0", "tags": ["code", "python", "javascript"]} | facebook/incoder-1B | null | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"code",
"python",
"javascript",
"arxiv:2204.05999",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-08T14:40:08+00:00 | [
"2204.05999"
] | [] | TAGS
#transformers #pytorch #xglm #text-generation #code #python #javascript #arxiv-2204.05999 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# InCoder 1B
A 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.
The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or ... | [
"# InCoder 1B\n\nA 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.\n\nThe model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, B... | [
"TAGS\n#transformers #pytorch #xglm #text-generation #code #python #javascript #arxiv-2204.05999 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# InCoder 1B\n\nA 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows... |
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. -->
# augmented_Squad_Translated
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "augmented_Squad_Translated", "results": []}]} | krinal214/augmented_Squad_Translated | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T14:58:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| augmented\_Squad\_Translated
============================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5251
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: 1",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
text-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/1317183233495388160/nLbB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/notsorobot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T15:11:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
3bkreno
@notsorob
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | # BART-base fine-tuned on NaturalQuestions for **Question Generation**
[BART Model](https://arxiv.org/pdf/1910.13461.pdf) trained for Question Generation in an unsupervised manner using [Back-Training](https://arxiv.org/pdf/2104.08801.pdf) algorithm (Kulshreshtha et al, EMNLP 2021). The dataset used are unaligned ques... | {"license": "cc-by-4.0"} | McGill-NLP/bart-qg-mlquestions-backtraining | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1910.13461",
"arxiv:2104.08801",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T15:38:22+00:00 | [
"1910.13461",
"2104.08801"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-1910.13461 #arxiv-2104.08801 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # BART-base fine-tuned on NaturalQuestions for Question Generation
BART Model trained for Question Generation in an unsupervised manner using Back-Training algorithm (Kulshreshtha et al, EMNLP 2021). The dataset used are unaligned questions and passages from MLQuestions dataset.
## Details of Back-Training
The Back-... | [
"# BART-base fine-tuned on NaturalQuestions for Question Generation\n\nBART Model trained for Question Generation in an unsupervised manner using Back-Training algorithm (Kulshreshtha et al, EMNLP 2021). The dataset used are unaligned questions and passages from MLQuestions dataset.",
"## Details of Back-Training... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-1910.13461 #arxiv-2104.08801 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BART-base fine-tuned on NaturalQuestions for Question Generation\n\nBART Model trained for Question Generation in an unsupervised manner u... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-airplanes
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-p... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-airplanes", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "image_folder", "args": "defau... | johnnydevriese/vit-airplanes | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T15:45:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-airplanes
=============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0152
* Accuracy: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 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: 4\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
null | transformers |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | johnowhitaker/lwg_colorbs | null | [
"transformers",
"huggan",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T16:17:46+00:00 | [] | [] | TAGS
#transformers #huggan #gan #license-mit #endpoints_compatible #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#transformers #huggan #gan #license-mit #endpoints_compatible #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent is... |
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. -->
# TestMeanFraction2
This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcame... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model-index": [{"name": "TestMeanFraction2", "results": []}]} | caush/TestMeanFraction2 | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T16:26:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| TestMeanFraction2
=================
This model is a fine-tuned version of cmarkea/distilcamembert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3967
* Matthews Correlation: 0.2537
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### 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: 8",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_s... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds-sample-gpt-small-10epoch
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown d... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-sample-gpt-small-10epoch", "results": []}]} | Pavithra/codeparrot-ds-sample-gpt-small-10epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T16:43:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| codeparrot-ds-sample-gpt-small-10epoch
======================================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0943
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n... |
unconditional-image-generation | keras |
## Model description
Simple DCGAN implementation in TensorFlow to generate CryptoPunks.
## Generated samples
<img src="https://github.com/dimitreOliveira/cryptogans/raw/main/assets/gen_samples.png" width="350" height="350">
Project repository: [CryptoGANs](https://github.com/dimitreOliveira/cryptogans).
## Usage
... | {"library_name": "keras", "tags": ["gan", "dcgan", "huggan", "tensorflow", "unconditional-image-generation"]} | huggan/crypto-gan | null | [
"keras",
"gan",
"dcgan",
"huggan",
"tensorflow",
"unconditional-image-generation",
"has_space",
"region:us"
] | null | 2022-04-08T17:15:01+00:00 | [] | [] | TAGS
#keras #gan #dcgan #huggan #tensorflow #unconditional-image-generation #has_space #region-us
|
## Model description
Simple DCGAN implementation in TensorFlow to generate CryptoPunks.
## Generated samples
<img src="URL width="350" height="350">
Project repository: CryptoGANs.
## Usage
You can play with the HuggingFace space demo.
Or try it yourself
## Training data
For training, I used the 10000 Crypto... | [
"## Model description\n\nSimple DCGAN implementation in TensorFlow to generate CryptoPunks.",
"## Generated samples\n<img src=\"URL width=\"350\" height=\"350\">\n\nProject repository: CryptoGANs.",
"## Usage\n\nYou can play with the HuggingFace space demo.\n\nOr try it yourself",
"## Training data\n\nFor tra... | [
"TAGS\n#keras #gan #dcgan #huggan #tensorflow #unconditional-image-generation #has_space #region-us \n",
"## Model description\n\nSimple DCGAN implementation in TensorFlow to generate CryptoPunks.",
"## Generated samples\n<img src=\"URL width=\"350\" height=\"350\">\n\nProject repository: CryptoGANs.",
"## Us... |
fill-mask | transformers |
# GO-Language model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
-... | {"license": "mit", "datasets": ["damlab/uniprot"], "metrics": ["accuracy"], "widget": [{"text": "involved_in GO:0006468 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372", "example_title": "Function"}]} | damlab/GO-language | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"dataset:damlab/uniprot",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T17:26:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #dataset-damlab/uniprot #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# GO-Language model
## Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
This model was built as a way to encode the Gene Ontology definition ... | [
"# GO-Language model",
"## Table of Contents\n- Summary\n- Model Description\n- Intended Uses & Limitations\n- How to Use\n- Training Data\n- Training Procedure\n - Preprocessing\n - Training\n- Evaluation Results\n- BibTeX Entry and Citation Info",
"## Summary\n\nThis model was built as a way to encode the G... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #dataset-damlab/uniprot #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# GO-Language model",
"## Table of Contents\n- Summary\n- Model Description\n- Intended Uses & Limitations\n- How to Use\n- Training Data\n- Training Procedure\n - Pr... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | {"library_name": "keras"} | nateraw/autoencoder-keras-mnist-demo-new | null | [
"keras",
"region:us"
] | null | 2022-04-08T17:37:04+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': ... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"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",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
text2text-generation | transformers | # poetry-generation-nextline-mbart-gut-en-single
* `nextline`: generates a poem line from previous line(s)
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `gut`: trained on Project Gutenberg data
* `en`: English language
* `single`: uses only last poem line... | {} | bmichele/poetry-generation-nextline-mbart-gut-en-single | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T17:46:39+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # poetry-generation-nextline-mbart-gut-en-single
* 'nextline': generates a poem line from previous line(s)
* 'mbart': base model is facebook/mbart-large-cc25
* 'gut': trained on Project Gutenberg data
* 'en': English language
* 'single': uses only last poem line as input for generation | [
"# poetry-generation-nextline-mbart-gut-en-single\n\n * 'nextline': generates a poem line from previous line(s)\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'gut': trained on Project Gutenberg data\n * 'en': English language\n * 'single': uses only last poem line as input for generation"
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# poetry-generation-nextline-mbart-gut-en-single\n\n * 'nextline': generates a poem line from previous line(s)\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'gut': trained on Project ... |
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. -->
# parsbert-finetuned-pos
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/Ho... | {"tags": ["generated_from_trainer"], "datasets": ["udpos28"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "parsbert-finetuned-pos", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "udpos28", "type": "udpos28", "args": "fa"}, "... | sepidmnorozy/parsbert-finetuned-pos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:udpos28",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T17:52:17+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-udpos28 #model-index #autotrain_compatible #endpoints_compatible #region-us
| parsbert-finetuned-pos
======================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the udpos28 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1385
* Precision: 0.9448
* Recall: 0.9486
* F1: 0.9467
* Accuracy: 0.9599
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-udpos28 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | nateraw/test-save-keras-sequential | null | [
"keras",
"region:us"
] | null | 2022-04-08T18:07:35+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history 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 ## Training Metrics\nModel history needed\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 ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed | {"library_name": "keras"} | lysandre/test-save-keras-sequential | null | [
"keras",
"region:us"
] | null | 2022-04-08T18:32:35+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed"
] |
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-model2-torgo
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluati... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-model2-torgo", "results": []}]} | modhp/wav2vec2-model2-torgo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T18:47:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wav2vec2-model2-torgo
=====================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.9975
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.1\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0... | [
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null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed | {"library_name": "keras"} | lysandre/test-save-keras-sequential-seconsd-try | null | [
"keras",
"region:us"
] | null | 2022-04-08T19:01:59+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | nateraw/test-save-keras-sequential-2 | null | [
"keras",
"region:us"
] | null | 2022-04-08T19:16:58+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history 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",
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"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 ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
image-classification | timm | # Model card for some-timm-model | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/some-timm-model | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-04-08T19:41:30+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for some-timm-model | [
"# Model card for some-timm-model"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for some-timm-model"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | nateraw/test-save-keras-sequential-3 | null | [
"keras",
"region:us"
] | null | 2022-04-08T20:00:20+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history 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 ## Training Metrics\nModel history needed\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 ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
question-answering | transformers | TEST | {} | alinemati/BERT | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-04-08T21:45:22+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #question-answering #endpoints_compatible #region-us
| TEST | [] | [
"TAGS\n#transformers #tf #distilbert #question-answering #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b4_lr3e-3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-wikihow_3epoch_b4_lr3e-3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "ty... | Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-3 | null | [
"transformers",
"pytorch",
"tensorboard",
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"dataset:wikihow",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-08T22:02:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikihow\_3epoch\_b4\_lr3e-3
==============================================
This model is a fine-tuned version of t5-small on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3400
* Rouge1: 26.7383
* Rouge2: 10.1981
* Rougel: 22.8642
* Rougelsum: 26.0922
* Ge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
null | transformers | ## Model Card: FLAVA
## Model Details
FLAVA model was developed by the researchers at FAIR to understand if a single model can work across different modalities with a unified architecture. The model was pretrained solely using publicly available multimodal datasets containing 70M image-text pairs in total and thus fu... | {"license": "bsd-3-clause"} | facebook/flava-full | null | [
"transformers",
"pytorch",
"flava",
"pretraining",
"arxiv:2112.04482",
"arxiv:2108.10904",
"license:bsd-3-clause",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-08T23:40:12+00:00 | [
"2112.04482",
"2108.10904"
] | [] | TAGS
#transformers #pytorch #flava #pretraining #arxiv-2112.04482 #arxiv-2108.10904 #license-bsd-3-clause #endpoints_compatible #has_space #region-us
| ## Model Card: FLAVA
## Model Details
FLAVA model was developed by the researchers at FAIR to understand if a single model can work across different modalities with a unified architecture. The model was pretrained solely using publicly available multimodal datasets containing 70M image-text pairs in total and thus fu... | [
"## Model Card: FLAVA",
"## Model Details\n\nFLAVA model was developed by the researchers at FAIR to understand if a single model can work across different modalities with a unified architecture. The model was pretrained solely using publicly available multimodal datasets containing 70M image-text pairs in total ... | [
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"## Model Card: FLAVA",
"## Model Details\n\nFLAVA model was developed by the researchers at FAIR to understand if a single model can work across different... |
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. -->
# TSC_finetuning-sentiment-movie-model2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "TSC_finetuning-sentiment-movie-model2", "results": []}]} | malcolm/TSC_finetuning-sentiment-movie-model2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T00:14:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TSC_finetuning-sentiment-movie-model2
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1479
- Accuracy: 0.957
- F1: 0.9752
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# TSC_finetuning-sentiment-movie-model2\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1479\n- Accuracy: 0.957\n- F1: 0.9752",
"## Model description\n\nMore information needed",
"## Intended uses & limit... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TSC_finetuning-sentiment-movie-model2\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt ach... |
text-generation | transformers |
#DEATH
#https://discord.gg/kNxBCv7DtK | {"tags": ["conversational"]} | AmbricJohnson5888/death | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T01:12:51+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#DEATH
#URL | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-multi-news1
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/face... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-multi-news1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "mu... | nikhedward/bart-large-cnn-finetuned-multi-news1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:multi_news",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T01:56:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-multi_news #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bart-large-cnn-finetuned-multi-news1
====================================
This model is a fine-tuned version of facebook/bart-large-cnn on the multi\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0858
* Rouge1: 42.1215
* Rouge2: 14.9986
* Rougel: 23.4737
* Rougelsum: 36.4212
* Gen... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# t5smallmodel
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset.
It achieve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "t5smallmodel", "results": []}]} | anegi/t5smallmodel | null | [
"transformers",
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"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T01:57:59+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| t5smallmodel
============
This model is a fine-tuned version of t5-small on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8672
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* l... |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-CTC Large (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Lang... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva"], "datasets": ["librispeech_asr", "fisher_corpus", "Switchboard-1", "WSJ-0", "WSJ-1", "National-Singapore-Co... | nvidia/stt_en_conformer_ctc_large | null | [
"nemo",
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"speech",
"audio",
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"pytorch",
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"Riva",
"en",
"arxiv:2005.08100",
"license:cc-by-4.0",
"model-index",
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"region:us"
] | null | 2022-04-09T02:43:21+00:00 | [
"2005.08100"
] | [
"en"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #en #arxiv-2005.08100 #license-cc-by-4.0 #model-index #has_space #region-us
| NVIDIA Conformer-CTC Large (en-US)
==================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lowercase English alphabe... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 kHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
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"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sampl... |
text-generation | transformers | #claura #https://discord.gg/kNxBCv7DtK | {"tags": ["conversational"]} | AmbricJohnson5888/claura | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T03:15:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #claura #URL | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 722121991
- CO2 Emissions (in grams): 8.052949236815056
## Validation Metrics
- Loss: 1.123626708984375
- Rouge1: 56.1275
- Rouge2: 33.5648
- RougeL: 51.986
- RougeLsum: 51.9943
- Gen Len: 13.2823
## Usage
You can use cURL to access this mo... | {"language": "unk", "tags": "autotrain", "datasets": ["Hodiden/autotrain-data-TestProj"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 8.052949236815056} | Hodiden/autotrain-TestProj-722121991 | null | [
"transformers",
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"t5",
"text2text-generation",
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"dataset:Hodiden/autotrain-data-TestProj",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T03:53:23+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-Hodiden/autotrain-data-TestProj #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 722121991
- CO2 Emissions (in grams): 8.052949236815056
## Validation Metrics
- Loss: 1.123626708984375
- Rouge1: 56.1275
- Rouge2: 33.5648
- RougeL: 51.986
- RougeLsum: 51.9943
- Gen Len: 13.2823
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 722121991\n- CO2 Emissions (in grams): 8.052949236815056",
"## Validation Metrics\n\n- Loss: 1.123626708984375\n- Rouge1: 56.1275\n- Rouge2: 33.5648\n- RougeL: 51.986\n- RougeLsum: 51.9943\n- Gen Len: 13.2823",
"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-Hodiden/autotrain-data-TestProj #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: 722121991\n- CO2 ... |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | gary109/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T04:25:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0981
* Accuracy: 0.9801
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_... |
text-classification | transformers |
Model that automatically classifies text messages as Racist or not Racist.
* `LABEL_0` output indicates non-racist text
* `LABEL_1` output indicates racist text
# Data
Tweets from Benítez-Andrades et al. (2022) dataset and the Datathon Against Racism tweets dataset. | {"language": "es", "license": "mit", "widget": [{"text": "Los mejores libros de Abdulrazak Gurnah, el ganador del Nobel de Literatura.", "example_title": "Non-racist example"}, {"text": "Ya est\u00e1n detenidos dos rumanos se\u00f1alados de cometer fraudes bancarios.", "example_title": "Racist example"}]} | jaumefib/datathon-against-racism | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T06:07:14+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Model that automatically classifies text messages as Racist or not Racist.
* 'LABEL_0' output indicates non-racist text
* 'LABEL_1' output indicates racist text
# Data
Tweets from Benítez-Andrades et al. (2022) dataset and the Datathon Against Racism tweets dataset. | [
"# Data\n\nTweets from Benítez-Andrades et al. (2022) dataset and the Datathon Against Racism tweets dataset."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Data\n\nTweets from Benítez-Andrades et al. (2022) dataset and the Datathon Against Racism tweets dataset."
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-scitldr-only-abstract
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-finetuned-scitldr-only-abstract", "results": []}]} | HenryHXR/t5-base-finetuned-scitldr-only-abstract | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T06:15:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-finetuned-scitldr-only-abstract
=======================================
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3365
* Rouge1: 34.3531
* Rouge2: 15.7554
* Rougel: 29.8918
* Rougelsum: 29.9514
* Gen Len: 18.7658
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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* num\\_epochs: 2\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Cantonese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Cantonese using the [Common Voice Corpus 8.0](https://commonvoice.mozilla.org/en/datasets).
When using this model, make sure that your speech input is sampled at 16kHz.
The Commo... | {"language": ["yue"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["cer"], "model-index": [{"name": "Wav2Vec2-Large-XLSR-53-Cantonese", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speec... | CAiRE/wav2vec2-large-xlsr-53-cantonese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"yue",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T06:23:48+00:00 | [] | [
"yue"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #yue #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Cantonese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice Corpus 8.0.
When using this model, make sure that your speech input is sampled at 16kHz.
The Common Voice's validated 'train' and 'dev' were used for training.
The script used for training can be found ... | [
"# Wav2Vec2-Large-XLSR-53-Cantonese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice Corpus 8.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThe Common Voice's validated 'train' and 'dev' were used for training.\n\nThe script used for training ca... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #yue #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Cantonese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Com... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | Wizounovziki/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T08:19:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b4_lr3e-4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-wikihow_3epoch_b4_lr3e-4", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "ty... | Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
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"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T08:45:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikihow\_3epoch\_b4\_lr3e-4
==============================================
This model is a fine-tuned version of t5-small on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2757
* Rouge1: 27.4024
* Rouge2: 10.7065
* Rougel: 23.3153
* Rougelsum: 26.7336
* Ge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# eliwill/gpt2-finetuned-krishna
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on a collection of books by J... | {"model-index": [{"name": "eliwill/gpt2-finetuned-krishna", "results": []}]} | eliwill/gpt2-finetuned-krishna | null | [
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T09:04:33+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| eliwill/gpt2-finetuned-krishna
==============================
This model is a fine-tuned version of gpt2 on a collection of books by Jiddu Krishnamurti.
It achieves the following results on the evaluation set:
* Train Loss: 3.4997
* Validation Loss: 3.6853
* Epoch: 0
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'd... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 722922024
- CO2 Emissions (in grams): 0.019299491458156143
## Validation Metrics
- Loss: 0.19609540700912476
- Accuracy: 0.9457627118644067
- Macro F1: 0.9404319054946133
- Micro F1: 0.9457627118644067
- Weighted F1: 0.9456037443... | {"language": "ja", "tags": "autotrain", "datasets": ["jicoc22578/autotrain-data-livedoor_news"], "widget": [{"text": "Windows 11\u642d\u8f09PC\u3092\u8cb7\u3063\u305f\u3089\u6700\u4f4e\u9650\u3084\u3063\u3066\u304a\u304d\u305f\u3044\u3053\u3068"}, {"text": "3\u6708\u30c7\u30b9\u30af\u30c8\u30c3\u30d7OS\u30b7\u30a7\u30a... | jicoc22578/autotrain-livedoor_news-722922024 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"ja",
"dataset:jicoc22578/autotrain-data-livedoor_news",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T09:33:57+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-jicoc22578/autotrain-data-livedoor_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 722922024
- CO2 Emissions (in grams): 0.019299491458156143
## Validation Metrics
- Loss: 0.19609540700912476
- Accuracy: 0.9457627118644067
- Macro F1: 0.9404319054946133
- Micro F1: 0.9457627118644067
- Weighted F1: 0.9456037443... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 722922024\n- CO2 Emissions (in grams): 0.019299491458156143",
"## Validation Metrics\n\n- Loss: 0.19609540700912476\n- Accuracy: 0.9457627118644067\n- Macro F1: 0.9404319054946133\n- Micro F1: 0.9457627118644067\n- Weighte... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-jicoc22578/autotrain-data-livedoor_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 722922024\n- CO2 Emissi... |
null | null | # Fatima's Fellowship Challenge
This card contains the model checkpoint, and training metrics of the computer vision coding challenge of the fellowship program.
- Epochs : 30
- Batch size : 32
- Learing rate : 0.0005
- Model : ResNet-50
- Optimizer : Adam
- Dataset : CIFAR10
| {} | Saitomar/Fellowship-Challenge-CV | null | [
"tensorboard",
"region:us"
] | null | 2022-04-09T09:36:13+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Fatima's Fellowship Challenge
This card contains the model checkpoint, and training metrics of the computer vision coding challenge of the fellowship program.
- Epochs : 30
- Batch size : 32
- Learing rate : 0.0005
- Model : ResNet-50
- Optimizer : Adam
- Dataset : CIFAR10
| [
"# Fatima's Fellowship Challenge\nThis card contains the model checkpoint, and training metrics of the computer vision coding challenge of the fellowship program.\n\n- Epochs : 30\n- Batch size : 32\n- Learing rate : 0.0005\n- Model : ResNet-50\n- Optimizer : Adam\n- Dataset : CIFAR10"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Fatima's Fellowship Challenge\nThis card contains the model checkpoint, and training metrics of the computer vision coding challenge of the fellowship program.\n\n- Epochs : 30\n- Batch size : 32\n- Learing rate : 0.0005\n- Model : ResNet-50\n- Optimizer : Adam\n- Dataset : ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-ipad-sum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It ac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-ipad-sum", "results": []}]} | Wizounovziki/t5-small-ipad-sum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T09:40:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-ipad-sum
=================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3632
* Rouge1: 90.6
* Rouge2: 29.6667
* Rougel: 90.8667
* Rougelsum: 90.6667
* Gen Len: 4.79
Model description
-----------------
More infor... | [
"### 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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
null | null | Jittor 版本的 | {} | student/Jittor_LSGAN | null | [
"region:us"
] | null | 2022-04-09T09:43:36+00:00 | [] | [] | TAGS
#region-us
| Jittor 版本的 | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# twitter_sexismo-finetuned-exist2021
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on the EXIST dataset
It achieves the following results on the evaluation set:
- Loss: 0.47
- Accuracy: 0.80
- F1: 0.83
- F2: 0.89
## Model... | {"license": "apache-2.0", "tags": ["sexism detector"], "datasets": ["EXIST_Dataset"], "metrics": ["accuracy"], "widget": [{"text": "manejas muy bien para ser mujer"}, {"text": "En temas pol\u00edticos hombres y mujeres son iguales"}, {"text": "Los ipad son unos equipos electr\u00f3nicos"}], "model-index": [{"name": "tw... | hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021 | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"sexism detector",
"dataset:EXIST_Dataset",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-09T10:07:22+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #sexism detector #dataset-EXIST_Dataset #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# twitter_sexismo-finetuned-exist2021
This model is a fine-tuned version of pysentimiento/robertuito-base-uncased on the EXIST dataset
It achieves the following results on the evaluation set:
- Loss: 0.47
- Accuracy: 0.80
- F1: 0.83
- F2: 0.89
## Model description
Model for the 'Somos NLP' Hackathon for detecting s... | [
"# twitter_sexismo-finetuned-exist2021\n\nThis model is a fine-tuned version of pysentimiento/robertuito-base-uncased on the EXIST dataset\n\nIt achieves the following results on the evaluation set:\n- Loss: 0.47\n- Accuracy: 0.80\n- F1: 0.83\n- F2: 0.89",
"## Model description\nModel for the 'Somos NLP' Hackatho... | [
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"# twitter_sexismo-finetuned-exist2021\n\nThis model is a fine-tuned version of pysentimiento/robertuito-base-... |
reinforcement-learning | stable-baselines3 | # TODO: Fill this model card
This is a pre-trained model of agent playing Asteroids-v0 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
```
pip install sta... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | TrabajoAprendizajeProfundo/Trabajo | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-04-09T10:48:09+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
| # TODO: Fill this model card
This is a pre-trained model of agent playing Asteroids-v0 using the stable-baselines3 library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
Then, you can use the model like this:
### Evaluation Results... | [
"# TODO: Fill this model card\nThis is a pre-trained model of agent playing Asteroids-v0 using the stable-baselines3 library.",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\n\n\nThen, you can use the model like this:",
"### E... | [
"TAGS\n#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us \n",
"# TODO: Fill this model card\nThis is a pre-trained model of agent playing Asteroids-v0 using the stable-baselines3 library.",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-b... |
token-classification | transformers | This is based on [Oliver Guhr's work](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large). The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polis... | {} | kredor/punctuate-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-09T11:05:11+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| This is based on Oliver Guhr's work. The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian.
----- report -----
... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null | Jittor 版本的 | {} | student/Jittor_GAN | null | [
"region:us"
] | null | 2022-04-09T11:06:07+00:00 | [] | [] | TAGS
#region-us
| Jittor 版本的 | [] | [
"TAGS\n#region-us \n"
] |
null | null | Jittor 版本的 | {} | student/Jittor_MNIST_Image_Recognition | null | [
"region:us"
] | null | 2022-04-09T11:10:06+00:00 | [] | [] | TAGS
#region-us
| Jittor 版本的 | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | DarrellTimothy/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T11:39:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
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"# Harry Potter DialoGPT Model"
] |
fill-mask | transformers | # TavBERT base model
A Turkish BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
### How to use
```python
import numpy as np
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForM... | {"language": "tr", "tags": ["roberta", "language model"], "datasets": ["oscar"]} | tau/tavbert-tr | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"language model",
"tr",
"dataset:oscar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T11:52:34+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #language model #tr #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us
| # TavBERT base model
A Turkish BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
### How to use
## Training data
OSCAR (Ortiz, 2019) Turkish section (27 GB text, 77 million sentences).
| [
"# TavBERT base model\nA Turkish BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).",
"### How to use",
"## Training data\nOSCAR (Ortiz, 2019) Turkish section (27 GB text, 77 million sentences)."
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"# TavBERT base model\nA Turkish BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et ... |
fill-mask | transformers | # TavBERT base model
An Arabic BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
### How to use
```python
import numpy as np
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForM... | {"language": "ar", "tags": ["roberta", "language model"], "datasets": ["oscar"]} | tau/tavbert-ar | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"language model",
"ar",
"dataset:oscar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T12:02:15+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #roberta #fill-mask #language model #ar #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us
| # TavBERT base model
An Arabic BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).
### How to use
## Training data
OSCAR (Ortiz, 2019) Arabic section (32 GB text, 67 million sentences).
| [
"# TavBERT base model\nAn Arabic BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020).",
"### How to use",
"## Training data\nOSCAR (Ortiz, 2019) Arabic section (32 GB text, 67 million sentences)."
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"# TavBERT base model\nAn Arabic BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et ... |
text-generation | transformers |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | AlekseyKorshuk/test | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggan",
"gan",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T12:15:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggan #gan #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
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"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
... |
null | null |
This was run from this implementation: https://github.com/NielsRogge/community-events-1/blob/improve_pix2pix/huggan/pytorch/pix2pix/train.py
The command to run was:
```bash
accelerate launch train.py --checkpoint_interval 1 --push_to_hub --output_dir pix2pix-facades --hub_model_id huggan/pix2pix-facades-demo --wand... | {"license": "mit", "tags": ["huggan", "gan"]} | huggan/pix2pix-facades-demo | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"region:us"
] | null | 2022-04-09T12:16:10+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #region-us
|
This was run from this implementation: URL
The command to run was:
| [] | [
"TAGS\n#pytorch #huggan #gan #license-mit #region-us \n"
] |
text-classification | transformers | Prot_bert finetuned on GPCR_train dataset of Drug Target prediction
Trainig paramenters:
overwrite_output_dir=True,
evaluation_strategy="epoch",
learning_rate=1e-3,
weight_decay=0.001,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
push_to_hub=True,
fp16=True,
logging... | {} | nepp1d0/SingleBertSmilesTargetInteraction | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T13:05:51+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Prot_bert finetuned on GPCR_train dataset of Drug Target prediction
Trainig paramenters:
overwrite_output_dir=True,
evaluation_strategy="epoch",
learning_rate=1e-3,
weight_decay=0.001,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
push_to_hub=True,
fp16=True,
logging... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | davidcheungo123/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T13:27:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4844
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-devices-sum-ver1
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-devices-sum-ver1", "results": []}]} | Wizounovziki/t5-base-devices-sum-ver1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T14:05:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-devices-sum-ver1
========================
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0935
* Rouge1: 97.2294
* Rouge2: 80.1323
* Rougel: 97.245
* Rougelsum: 97.2763
* Gen Len: 4.9507
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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
null | null |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | nielsr/pix2pix-cityscapes | null | [
"pytorch",
"huggan",
"gan",
"license:mit",
"region:us"
] | null | 2022-04-09T15:14:55+00:00 | [] | [] | TAGS
#pytorch #huggan #gan #license-mit #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#pytorch #huggan #gan #license-mit #region-us \n",
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"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediat... |
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-all-translated
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-all-translated", "results": []}]} | krinal214/bert-all-translated | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-09T16:19:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| bert-all-translated
===================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5775
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: 1",
"### Traini... | [
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"### 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 |
<!-- 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-devices-sum-ver1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-devices-sum-ver1", "results": []}]} | Wizounovziki/t5-small-devices-sum-ver1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T16:25:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-devices-sum-ver1
=========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2335
* Rouge1: 93.7171
* Rouge2: 73.3058
* Rougel: 93.7211
* Rougelsum: 93.689
* Gen Len: 4.7246
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: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gemasphi/laprador-query-encoder | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T16:40:15+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gemasphi/laprador-document-encoder | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T17:31:16+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
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