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null | keras |
## Model description
This repo contains the model and the notebook for implementing MelGAN to inverse spectrogram using feature matching [MelGAN-based spectrogram inversion using feature matching](https://keras.io/examples/audio/melgan_spectrogram_inversion/).
Full credits go to [Darshan Deshpande](https://twitter.c... | {"library_name": "keras"} | keras-io/MelGAN-spectrogram-inversion | null | [
"keras",
"tensorboard",
"has_space",
"region:us"
] | null | 2022-06-14T11:48:12+00:00 | [] | [] | TAGS
#keras #tensorboard #has_space #region-us
|
## Model description
This repo contains the model and the notebook for implementing MelGAN to inverse spectrogram using feature matching MelGAN-based spectrogram inversion using feature matching.
Full credits go to Darshan Deshpande
Reproduced by Vu Minh Chien
Motivation: Autoregressive vocoders have been ubiquito... | [
"## Model description\n\nThis repo contains the model and the notebook for implementing MelGAN to inverse spectrogram using feature matching MelGAN-based spectrogram inversion using feature matching.\n\nFull credits go to Darshan Deshpande\n\nReproduced by Vu Minh Chien\n\nMotivation: Autoregressive vocoders have b... | [
"TAGS\n#keras #tensorboard #has_space #region-us \n",
"## Model description\n\nThis repo contains the model and the notebook for implementing MelGAN to inverse spectrogram using feature matching MelGAN-based spectrogram inversion using feature matching.\n\nFull credits go to Darshan Deshpande\n\nReproduced by Vu ... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="joitandr/q-FrozenLake-v1-4x4-nonslippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-nonslippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type":... | joitandr/q-FrozenLake-v1-4x4-nonslippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-14T11:54:37+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tamil-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-tamil-colab", "results": []}]} | Priya9/wav2vec2-large-xls-r-300m-tamil-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T12:01:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-tamil-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5869
* Wer: 0.7266
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
question-answering | transformers |
# MarkupLM, fine-tuned on WebSRC
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understandin... | {"language": ["en"], "datasets": ["websrc"], "inference": false} | microsoft/markuplm-base-finetuned-websrc | null | [
"transformers",
"pytorch",
"markuplm",
"question-answering",
"en",
"dataset:websrc",
"arxiv:2110.08518",
"has_space",
"region:us"
] | null | 2022-06-14T12:08:06+00:00 | [
"2110.08518"
] | [
"en"
] | TAGS
#transformers #pytorch #markuplm #question-answering #en #dataset-websrc #arxiv-2110.08518 #has_space #region-us
|
# MarkupLM, fine-tuned on WebSRC
Multimodal (text +markup language) pre-training for Document AI
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage in... | [
"# MarkupLM, fine-tuned on WebSRC\n\nMultimodal (text +markup language) pre-training for Document AI",
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"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training meth... |
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 [klue/roberta-small](https://huggingface.co/klue/r... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | janeel/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T12:14:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of klue/roberta-small on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1272
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### 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: 32\n* eval\\_batch... |
null | null | Peacock Fox | {} | Arnoldstein/PeacockFox | null | [
"region:us"
] | null | 2022-06-14T12:32:21+00:00 | [] | [] | TAGS
#region-us
| Peacock Fox | [] | [
"TAGS\n#region-us \n"
] |
text-classification | generic | ## Hugging Face Transformers with Scikit-learn Classifiers 🤩🌟
This repository contains a small proof-of-concept pipeline that leverages longformer embeddings with scikit-learn Logistic Regression that does sentiment analysis.
The training leverages the language module of [whatlies](https://github.com/koaning/whatli... | {"license": "apache-2.0", "library_name": "generic", "tags": ["text-classification", "generic", "notebook-favorites"]} | scikit-learn/sklearn-transformers | null | [
"generic",
"text-classification",
"notebook-favorites",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-14T12:33:48+00:00 | [] | [] | TAGS
#generic #text-classification #notebook-favorites #license-apache-2.0 #has_space #region-us
| ## Hugging Face Transformers with Scikit-learn Classifiers
This repository contains a small proof-of-concept pipeline that leverages longformer embeddings with scikit-learn Logistic Regression that does sentiment analysis.
The training leverages the language module of whatlies.
See the tutorial notebook here.
# Cla... | [
"## Hugging Face Transformers with Scikit-learn Classifiers \n\nThis repository contains a small proof-of-concept pipeline that leverages longformer embeddings with scikit-learn Logistic Regression that does sentiment analysis. \nThe training leverages the language module of whatlies.\nSee the tutorial notebook her... | [
"TAGS\n#generic #text-classification #notebook-favorites #license-apache-2.0 #has_space #region-us \n",
"## Hugging Face Transformers with Scikit-learn Classifiers \n\nThis repository contains a small proof-of-concept pipeline that leverages longformer embeddings with scikit-learn Logistic Regression that does se... |
question-answering | transformers |
# MarkupLM, fine-tuned on WebSRC
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understandin... | {"language": ["en"], "datasets": ["websrc"], "inference": false} | microsoft/markuplm-large-finetuned-websrc | null | [
"transformers",
"pytorch",
"markuplm",
"question-answering",
"en",
"dataset:websrc",
"arxiv:2110.08518",
"region:us"
] | null | 2022-06-14T12:38:07+00:00 | [
"2110.08518"
] | [
"en"
] | TAGS
#transformers #pytorch #markuplm #question-answering #en #dataset-websrc #arxiv-2110.08518 #region-us
|
# MarkupLM, fine-tuned on WebSRC
Multimodal (text +markup language) pre-training for Document AI
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage in... | [
"# MarkupLM, fine-tuned on WebSRC\n\nMultimodal (text +markup language) pre-training for Document AI",
"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA an... | [
"TAGS\n#transformers #pytorch #markuplm #question-answering #en #dataset-websrc #arxiv-2110.08518 #region-us \n",
"# MarkupLM, fine-tuned on WebSRC\n\nMultimodal (text +markup language) pre-training for Document AI",
"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text ... |
null | null | Giorno with huge pen | {} | Xjbvjjgv/333 | null | [
"region:us"
] | null | 2022-06-14T12:48:38+00:00 | [] | [] | TAGS
#region-us
| Giorno with huge pen | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | emergix/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T13:05:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4626
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
null | null | testing testing 123 | {"license": "other"} | Tess/tesseract | null | [
"license:other",
"region:us"
] | null | 2022-06-14T13:09:21+00:00 | [] | [] | TAGS
#license-other #region-us
| testing testing 123 | [] | [
"TAGS\n#license-other #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmv2-g-aadhaar-236doc-06-14
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/mic... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2-g-aadhaar-236doc-06-14", "results": []}]} | Sebabrata/lmv2-g-aadhaar-236doc-06-14 | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T13:24:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| lmv2-g-aadhaar-236doc-06-14
===========================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0427
* Aadhaar Precision: 0.9783
* Aadhaar Recall: 1.0
* Aadhaar F1: 0.9890
* Aadhaar Number: 45
* D... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* tra... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-clincal-scratch-wl-es
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.c... | {"tags": ["generated_from_trainer"], "base_model": "dccuchile/bert-base-spanish-wwm-uncased", "model-index": [{"name": "bert-clincal-scratch-wl-es", "results": []}]} | plncmm/bert-clinical-scratch-wl-es | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T13:45:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-dccuchile/bert-base-spanish-wwm-uncased #autotrain_compatible #endpoints_compatible #region-us
|
# bert-clincal-scratch-wl-es
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# bert-clincal-scratch-wl-es\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-dccuchile/bert-base-spanish-wwm-uncased #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-clincal-scratch-wl-es\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown datase... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad2-finetuned-squad2
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "roberta-base-squad2-finetuned-squad2", "results": []}]} | Marscen/roberta-base-squad2-finetuned-squad2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T13:50:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
| roberta-base-squad2-finetuned-squad2
====================================
This model is a fine-tuned version of deepset/roberta-base-squad2 on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8815
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_si... |
text-generation | transformers |
# Hermite DialoGPT Model | {"tags": ["conversational"]} | Hermite/DialoGPT-large-hermite | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T14:06:17+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Hermite DialoGPT Model | [
"# Hermite DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Hermite DialoGPT Model"
] |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-ceb
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["ceb"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-ceb | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"ceb",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T14:09:30+00:00 | [] | [
"ceb"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #ceb #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-ceb
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - CEB (Cebua... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #ceb #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="joitandr/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | joitandr/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-14T14:17:09+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/xlnet-base-cased-attribute-correction
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-bas... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ksabeh/xlnet-base-cased-attribute-correction", "results": []}]} | ksabeh/xlnet-base-cased-attribute-correction | null | [
"transformers",
"tf",
"xlnet",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T14:39:27+00:00 | [] | [] | TAGS
#transformers #tf #xlnet #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
| ksabeh/xlnet-base-cased-attribute-correction
============================================
This model is a fine-tuned version of xlnet-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0599
* Validation Loss: 0.0214
* Epoch: 0
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 36852, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #xlnet #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay'... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-jra
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["jra"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-jra | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"jra",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T14:40:33+00:00 | [] | [
"jra"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #jra #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-jra
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - JRA (Jarai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #jra #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\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. -->
# qnli
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QNLI", "type": "glue", "args": "qnli"}, "metric... | Alireza1044/mobilebert_QNLI | null | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T14:54:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# qnli
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3731
- Accuracy: 0.9068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluat... | [
"# qnli\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE QNLI dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3731\n- Accuracy: 0.9068",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# qnli\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE QNLI dataset.\nI... |
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/635525362471038977/hSfNB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lukaesch/1655224388749/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lukaesch | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T15:21:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Lukas (URL)
@lukaesch
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-segmentation | transformers |
# SegFormer (b1-sized) model fine-tuned on sidewalk-semantic dataset
SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and f... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation"], "datasets": ["segments/sidewalk-semantic"]} | chainyo/segformer-b1-sidewalk | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"dataset:segments/sidewalk-semantic",
"arxiv:2105.15203",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T15:31:12+00:00 | [
"2105.15203"
] | [] | TAGS
#transformers #pytorch #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #license-apache-2.0 #endpoints_compatible #region-us
|
# SegFormer (b1-sized) model fine-tuned on sidewalk-semantic dataset
SegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.
... | [
"# SegFormer (b1-sized) model fine-tuned on sidewalk-semantic dataset\n\nSegFormer model fine-tuned on segments/sidewalk-semantic at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this reposito... | [
"TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #dataset-segments/sidewalk-semantic #arxiv-2105.15203 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SegFormer (b1-sized) model fine-tuned on sidewalk-semantic dataset\n\nSegFormer model fine-tuned on segments/sidewalk-semantic at ... |
text-classification | transformers |
# DistilBERT ArXiv Category Classification
DistilBERT model fine-tuned on a small subset of the [ArXiv dataset](https://www.kaggle.com/datasets/Cornell-University/arxiv) to predict the category of a given paper.
| {"language": ["en"], "license": "apache-2.0", "tags": ["distilbert"], "datasets": ["arxiv_dataset"]} | Wi/arxiv-distilbert-base-cased | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:arxiv_dataset",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T16:28:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #dataset-arxiv_dataset #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# DistilBERT ArXiv Category Classification
DistilBERT model fine-tuned on a small subset of the ArXiv dataset to predict the category of a given paper.
| [
"# DistilBERT ArXiv Category Classification\n\nDistilBERT model fine-tuned on a small subset of the ArXiv dataset to predict the category of a given paper."
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #en #dataset-arxiv_dataset #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT ArXiv Category Classification\n\nDistilBERT model fine-tuned on a small subset of the ArXiv dataset to predict the category of a g... |
automatic-speech-recognition | transformers |
# xls-r-300m-danish-nst-cv9
This is a version of [chcaa/xls-r-300m-danish](https://huggingface.co/chcaa/xls-r-300m-danish) finetuned for Danish ASR on the training set of the public NST dataset and the Danish part of Common Voice 9. The model is trained on 16kHz, so ensure that you use the same sample rate.
The mod... | {"language": "da", "license": "apache-2.0", "tags": ["speech-to-text"], "datasets": ["common-voice-9", "nst"]} | chcaa/xls-r-300m-danish-nst-cv9 | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"speech-to-text",
"da",
"dataset:common-voice-9",
"dataset:nst",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T16:36:52+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #speech-to-text #da #dataset-common-voice-9 #dataset-nst #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-danish-nst-cv9
=========================
This is a version of chcaa/xls-r-300m-danish finetuned for Danish ASR on the training set of the public NST dataset and the Danish part of Common Voice 9. The model is trained on 16kHz, so ensure that you use the same sample rate.
The model was trained using fairs... | [] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #speech-to-text #da #dataset-common-voice-9 #dataset-nst #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-Spanish
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on th... | {"license": "apache-2.0", "tags": ["summarization", "mt5", "es", "spanish", "abstractive summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "mt5-base-finetuned-Spanish", "results": []}]} | eslamxm/mt5-base-finetuned-Spanish | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"es",
"spanish",
"abstractive summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"r... | null | 2022-06-14T17:45:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #es #spanish #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mt5-base-finetuned-Spanish
This model is a fine-tuned version of google/mt5-base on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1727
- Rouge-1: 28.11
- Rouge-2: 12.09
- Rouge-l: 24.62
- Gen Len: 18.73
- Bertscore: 72.25
## Model description
More information needed... | [
"# mt5-base-finetuned-Spanish\n\nThis model is a fine-tuned version of google/mt5-base on the wiki_lingua dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.1727\n- Rouge-1: 28.11\n- Rouge-2: 12.09\n- Rouge-l: 24.62\n- Gen Len: 18.73\n- Bertscore: 72.25",
"## Model description\n\nMore i... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #es #spanish #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt5-base-finetuned-Spanish\n\nThis... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-kan
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["kan", "kn"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Int... | sil-ai/wav2vec2-bloom-speech-kan | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"kan",
"kn",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T17:50:07+00:00 | [] | [
"kan",
"kn"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #kan #kn #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-kan
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - KAN (Kanna... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #kan #kn #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | cindy203cc/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T17:55:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3187
- Accuracy: 0.8633
- F1: 0.8629
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3187\n- Accuracy: 0.8633\n- F1: 0.8629",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
text-generation | transformers |
# Ukrainian AI chatbot alpha release
This model was trained on dataset of movie dialogs (uncleaned) from opensubtitles.org.
Link to training scripts: [https://github.com/robinhad/ukrainian-ai](https://github.com/robinhad/ukrainian-ai).
Link to end-to-end open source AI demo (speech-to-text-to-AI-to-voice): [https:... | {"license": "mit", "tags": ["conversational"], "widget": [{"text": "\u043f\u0440\u0438\u0432\u0456\u0442, \u044f\u043a \u0442\u0435\u0431\u0435 \u0437\u0432\u0430\u0442\u0438?", "example_title": "\u041f\u0438\u0442\u0430\u0454\u043c\u043e \u0456\u043c'\u044f"}]} | robinhad/gpt2-uk-conversational | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T18:35:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Ukrainian AI chatbot alpha release
This model was trained on dataset of movie dialogs (uncleaned) from URL.
Link to training scripts: URL
Link to end-to-end open source AI demo (speech-to-text-to-AI-to-voice): URL
# Example usage
<img src="URL alt="visitors badge"/> | [
"# Ukrainian AI chatbot alpha release\n\nThis model was trained on dataset of movie dialogs (uncleaned) from URL.\n\nLink to training scripts: URL \nLink to end-to-end open source AI demo (speech-to-text-to-AI-to-voice): URL",
"# Example usage\n\n\n\n<img src=\"URL alt=\"visitors badge\"/>"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Ukrainian AI chatbot alpha release\n\nThis model was trained on dataset of movie dialogs (uncleaned) from URL.\n\nLink to training ... |
text-generation | transformers | # Controllable text generation for the marketing content of NFTs
This repository contains all the information, code and datasets of the "Controllable text generation for the marketing content of NFTs" transformers' model started as a group project at the Machine Learning degree of [opencampus.sh](https://opencampus.sh)... | {} | DemocracyStudio/generate_nft_content | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T18:39:11+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Controllable text generation for the marketing content of NFTs
This repository contains all the information, code and datasets of the "Controllable text generation for the marketing content of NFTs" transformers' model started as a group project at the Machine Learning degree of URL.
You can either clone this reposi... | [
"# Controllable text generation for the marketing content of NFTs\nThis repository contains all the information, code and datasets of the \"Controllable text generation for the marketing content of NFTs\" transformers' model started as a group project at the Machine Learning degree of URL.\n\nYou can either clone t... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Controllable text generation for the marketing content of NFTs\nThis repository contains all the information, code and datasets of the \"Controllable text gener... |
text-generation | transformers |
# Luca Changretta GPT Model | {"tags": ["conversational"]} | Browbon/DialoGPT-small-LucaChangretta | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T19:11:27+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Luca Changretta GPT Model | [
"# Luca Changretta GPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Luca Changretta GPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-news
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ag_news"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-news", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ag_news", "type": "ag_news", ... | mosesju/distilbert-base-uncased-finetuned-news | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T19:16:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-ag_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-news
======================================
This model is a fine-tuned version of distilbert-base-uncased on the ag\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2117
* Accuracy: 0.9388
* F1: 0.9388
Model description
-----------------
This mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-ag_news #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... |
image-classification | transformers |
# koala-panda-wombat
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/h... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | nateraw/koala-panda-wombat | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T19:30:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# koala-panda-wombat
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### koala
!koala
#### panda
!panda
#### wombat
!wombat | [
"# koala-panda-wombat\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### koala\n\n!koala",
"#### panda\n\n!panda",
"#### wombat\n\n!wombat"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# koala-panda-wombat\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any i... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | tanbwilson/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-14T19:31:12+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
<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/1513529336107839491/OQup... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rangersfc/1655240322192/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rangersfc | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T19:58:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rangers Football Club
@rangersfc
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"
] |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-fra
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["fra", "fr"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Int... | sil-ai/wav2vec2-bloom-speech-fra | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"fra",
"fr",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T20:13:45+00:00 | [] | [
"fra",
"fr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #fra #fr #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-fra
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - FRA datase... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #fra #fr #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}... | tuni/distilbert-base-uncased-finetuned-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T20:50:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mnli
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6574
* Accuracy: 0.8205
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmroberta-finetuned-fa
This model is a fine-tuned version of [](https://huggingface.co/) on the pn_summary dataset.
It achieves... | {"tags": ["summarization", "fa", "xlmroberta", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["pn_summary"], "model-index": [{"name": "xlmroberta-finetuned-fa", "results": []}]} | eslamxm/xlmroberta-finetuned-fa | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"fa",
"xlmroberta",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:pn_summary",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T21:08:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #fa #xlmroberta #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #autotrain_compatible #endpoints_compatible #region-us
|
# xlmroberta-finetuned-fa
This model is a fine-tuned version of [](URL on the pn_summary dataset.
It achieves the following results on the evaluation set:
- Loss: 8.2286
- Rouge-1: 4.99
- Rouge-2: 0.0
- Rouge-l: 4.99
- Gen Len: 20.0
- Bertscore: 51.89
## Model description
More information needed
## Intended uses... | [
"# xlmroberta-finetuned-fa\n\nThis model is a fine-tuned version of [](URL on the pn_summary dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 8.2286\n- Rouge-1: 4.99\n- Rouge-2: 0.0\n- Rouge-l: 4.99\n- Gen Len: 20.0\n- Bertscore: 51.89",
"## Model description\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #fa #xlmroberta #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlmroberta-finetuned-fa\n\nThis model is a fine-tuned version of []... |
token-classification | transformers |
bertweet-base (https://huggingface.co/vinai/bertweet-base) finetuned on CoNLL (2003) English, following https://github.com/huggingface/transformers/tree/main/examples/legacy/token-classification | {"language": ["en"], "tags": ["NER"], "datasets": ["conll2003"]} | emilys/BERTweet-CoNLL | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"NER",
"en",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T21:41:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #NER #en #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us
|
bertweet-base (URL finetuned on CoNLL (2003) English, following URL | [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #NER #en #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | pooopp | {} | poopscoop/poopscoop | null | [
"region:us"
] | null | 2022-06-14T21:42:59+00:00 | [] | [] | TAGS
#region-us
| pooopp | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Ortho DialoGPT Model | {"tags": ["conversational"]} | gloomyworm/DialoGPT-medium-ortho | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T21:54:29+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Ortho DialoGPT Model | [
"# Ortho DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ortho DialoGPT Model"
] |
token-classification | transformers |
bertweet-base (https://huggingface.co/vinai/bertweet-base) finetuned on WNUT (2017), following https://github.com/huggingface/transformers/tree/main/examples/legacy/token-classification | {"language": ["en"], "tags": ["NER"], "datasets": ["wnut_17"]} | emilys/BERTweet-WNUT17 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"NER",
"en",
"dataset:wnut_17",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T21:59:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #NER #en #dataset-wnut_17 #autotrain_compatible #endpoints_compatible #region-us
|
bertweet-base (URL finetuned on WNUT (2017), following URL | [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #NER #en #dataset-wnut_17 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | tyler-richardett/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-14T22:17:36+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Tstarshak/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-14T22:30:41+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers | <img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
# bert2gpt2SUMM-finetuned-mlsum
---
## This model is used for french summarization
- Problem type: Summa... | {"language": "fr", "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new", "results": []}]} | Chemsseddine/bert2gpt2SUMM-finetuned-mlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-14T22:39:14+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| <img src="URL alt="Map of positive probabilities per country." width="200"/>
bert2gpt2SUMM-finetuned-mlsum
=============================
---
This model is used for french summarization
-------------------------------------------
* Problem type: Summarization
* Model ID: 980832493
* CO2 Emissions (in grams): 0... | [
"### 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 #encoder-decoder #text2text-generation #generated_from_trainer #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
image-classification | transformers |
# teeth_test
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | steven123/teeth_test | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T22:46:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# teeth_test
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Good Teeth
!Good Teeth
#### Missing Teeth
!Missing Teeth
#### Rotten Teeth
!Rotten Teeth | [
"# teeth_test\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Good Teeth\n\n!Good Teeth",
"#### Missing Teeth\n\n!Missing Teeth",
"#### Rotten Teeth\n\... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# teeth_test\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues wi... |
text-generation | transformers | ## How to use
```python
import transformers
model = transformers.GPT2LMHeadModel.from_pretrained("lbox/lcube-base")
tokenizer = transformers.AutoTokenizer.from_pretrained(
"lbox/lcube-base",
bos_token="[BOS]",
unk_token="[UNK]",
pad_token="[PAD]",
mask_token="[MASK]",
)
text = "피고인은 불상지에 있는 커피숍에서... | {} | lbox/lcube-base | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T22:50:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## How to use
For more information please visit <URL
## Licensing Information
Copyright 2022-present LBox Co. Ltd.
Licensed under the CC BY-NC-ND 4.0 | [
"## How to use \n\n\nFor more information please visit <URL",
"## Licensing Information\n\nCopyright 2022-present LBox Co. Ltd.\n\nLicensed under the CC BY-NC-ND 4.0"
] | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use \n\n\nFor more information please visit <URL",
"## Licensing Information\n\nCopyright 2022-present LBox Co. Ltd.\n\nLicensed under the CC BY-NC-ND 4.0"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | tanbwilson/test2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-14T22:53:24+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-squadshifts-new_wiki-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://github.... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-base-squadshifts-new_wiki-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T22:59:37+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-squadshifts-new\_wiki-qg'
=====================================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: new\_wiki) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Lan... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (new\\_wiki)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric fil... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-squadshifts-nyt-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/asahi4... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-base-squadshifts-nyt-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T23:01:50+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-squadshifts-nyt-qg'
===============================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: nyt) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en
* Traini... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (nyt)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-squadshifts-reddit-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://github.com/... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-base-squadshifts-reddit-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T23:04:55+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-squadshifts-reddit-qg'
==================================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: reddit) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (reddit)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-base-squadshifts-amazon-qg`
This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.com/... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-base-squadshifts-amazon-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-14T23:07:03+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'lmqg/t5-base-squadshifts-amazon-qg'
==================================================
This model is fine-tuned version of lmqg/t5-base-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: amazon) via 'lmqg'.
### Overview
* Language model: lmqg/t5-base-squad
* Language: en... | [
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (amazon)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: lmqg/t5-base-squad\n* Language... |
image-classification | transformers |
# Teeth_B
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics)... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | steven123/Teeth_B | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-14T23:31:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Teeth_B
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Good Teeth
!Good Teeth
#### Missing Teeth
!Missing Teeth
#### Rotten Teeth
!Rotten Teeth | [
"# Teeth_B\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Good Teeth\n\n!Good Teeth",
"#### Missing Teeth\n\n!Missing Teeth",
"#### Rotten Teeth\n\n!R... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Teeth_B\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with ... |
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. -->
# 22s-dl-sentiment-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["yelp_review_full"], "metrics": ["accuracy"], "model-index": [{"name": "22s-dl-sentiment-1", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "yelp_review_full", "type": "yelp_review_full",... | DLochmelis33/22s-dl-sentiment-1 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T00:01:07+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-yelp_review_full #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# 22s-dl-sentiment-1
This model is a fine-tuned version of distilbert-base-uncased on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2574
- Accuracy: 0.9542
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# 22s-dl-sentiment-1\n\nThis model is a fine-tuned version of distilbert-base-uncased on the yelp_review_full dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2574\n- Accuracy: 0.9542",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-yelp_review_full #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# 22s-dl-sentiment-1\n\nThis model is a fine-tuned version of distilbert-base-uncased on the yelp_review_fu... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="tanbwilson/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | tanbwilson/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-15T00:02:49+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tanbwilson/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | tanbwilson/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-15T00:04:42+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | PyTorch Lightning |
## Model Details
This model is from [FSPBT-Image-Translation](https://github.com/rnwzd/FSPBT-Image-Translation)
## Citation Information
```bibtex
@Article{Texler20-SIG,
author = "Ond\v{r}ej Texler and David Futschik and Michal Ku\v{c}era and Ond\v{r}ej Jamri\v{s}ka and \v{S}\'{a}rka Sochorov\'{a} and Mengl... | {"license": "mit", "library_name": "PyTorch Lightning", "tags": ["Image Translation"]} | CVPR/FSPBT | null | [
"PyTorch Lightning",
"Image Translation",
"license:mit",
"has_space",
"region:us"
] | null | 2022-06-15T00:14:32+00:00 | [] | [] | TAGS
#PyTorch Lightning #Image Translation #license-mit #has_space #region-us
|
## Model Details
This model is from FSPBT-Image-Translation
| [
"## Model Details\nThis model is from FSPBT-Image-Translation"
] | [
"TAGS\n#PyTorch Lightning #Image Translation #license-mit #has_space #region-us \n",
"## Model Details\nThis model is from FSPBT-Image-Translation"
] |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-kek
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["kek"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-kek | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"kek",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-15T00:16:47+00:00 | [] | [
"kek"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #kek #dataset-bloom_speech #license-other #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-bloom-speech-kek
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - KEK (Chine... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #kek #dataset-bloom_speech #license-other #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | HrayrM/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T00:38:36+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2432
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 16",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
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-base-wnli
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE WNLI dat... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE WNLI", "type": "glue", "args": "wnli"}, "... | JeremiahZ/roberta-base-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T01:40:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-wnli
=================
This model is a fine-tuned version of roberta-base on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6849
* Accuracy: 0.5634
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: 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\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #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\\_rat... |
image-classification | transformers |
# Teeth_A
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics)... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | steven123/Teeth_A | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T01:42:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Teeth_A
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Good Teeth
!Good Teeth
#### Missing Teeth
!Missing Teeth
#### Rotten Teeth
!Rotten Teeth | [
"# Teeth_A\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Good Teeth\n\n!Good Teeth",
"#### Missing Teeth\n\n!Missing Teeth",
"#### Rotten Teeth\n\n!R... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Teeth_A\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with ... |
image-classification | transformers |
# Teeth_C
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics)... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | steven123/Teeth_C | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T01:53:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Teeth_C
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Good Teeth
!Good Teeth
#### Missing Teeth
!Missing Teeth
#### Rotten Teeth
!Rotten Teeth | [
"# Teeth_C\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Good Teeth\n\n!Good Teeth",
"#### Missing Teeth\n\n!Missing Teeth",
"#### Rotten Teeth\n\n!R... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Teeth_C\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with ... |
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": []}]} | olpa/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T02:21:16+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: 1.4863
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 is a fine-tuned checkpoint of [T5-base](https://huggingface.co/t5-base). Fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://github.com/rpryzant/neutralizing-bias), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral po... | {"language": ["en"], "license": "apache-2.0", "datasets": ["WNC"], "metrics": ["accuracy"]} | erickfm/neutrally | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:WNC",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T02:49:30+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-WNC #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model is a fine-tuned checkpoint of T5-base. Fine-tuned on the Wiki Neutrality Corpus (WNC), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral point of view”. This model achieves state of the art (SOTA) performance with a BLEU s... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-WNC #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
# deberta-base-japanese-aozora-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-base-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-aozora) and [UD_Japanese-... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b66\u5e74\u306b... | KoichiYasuoka/deberta-base-japanese-aozora-ud-head | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"japanese",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T03:02:27+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# deberta-base-japanese-aozora-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-base-japanese-aozora and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity when specifying... | [
"# deberta-base-japanese-aozora-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-base-japanese-aozora and UD_Japanese-GSDLUW. Use [MASK] inside 'context' to avoid ambiguity when ... | [
"TAGS\n#transformers #pytorch #deberta-v2 #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# deberta-base-japanese-aozora-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on 青空文庫 for depend... |
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. -->
# sarcasm-detection-Bert-base-uncased-POS
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-Bert-base-uncased-POS", "results": []}]} | jkhan447/sarcasm-detection-Bert-base-uncased-POS | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T03:05:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-Bert-base-uncased-POS
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1904
- Accuracy: 0.591
## Model description
More information needed
## Intended uses & limitations
More information needed
##... | [
"# sarcasm-detection-Bert-base-uncased-POS\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.1904\n- Accuracy: 0.591",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-Bert-base-uncased-POS\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the ... |
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. -->
# sarcasm-detection-Bert-base-uncased-CR-POS
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-Bert-base-uncased-CR-POS", "results": []}]} | jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T03:05:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-Bert-base-uncased-CR-POS
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1816
- Accuracy: 0.5783
## Model description
More information needed
## Intended uses & limitations
More information needed... | [
"# sarcasm-detection-Bert-base-uncased-CR-POS\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 4.1816\n- Accuracy: 0.5783",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-Bert-base-uncased-CR-POS\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves t... |
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/1429866660299689984/CGXA... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/mysteriousgam54 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T03:05:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
themysteriousgamer
@mysteriousgam54
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 da... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-kjb
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["kjb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Inte... | sil-ai/wav2vec2-bloom-speech-kjb | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"kjb",
"dataset:bloom_speech",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T03:18:34+00:00 | [] | [
"kjb"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #kjb #dataset-bloom_speech #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-kjb
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - KJB (Q’anj... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #kjb #dataset-bloom_speech #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during traini... |
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. -->
# Malaya-speech_fine-tune_MrBrown_15_Jun
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https... | {"tags": ["generated_from_trainer"], "datasets": ["uob_singlish"], "model-index": [{"name": "Malaya-speech_fine-tune_MrBrown_15_Jun", "results": []}]} | RuiqianLi/Malaya-speech_fine-tune_MrBrown_15_Jun | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T03:20:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us
| Malaya-speech\_fine-tune\_MrBrown\_15\_Jun
==========================================
This model is a fine-tuned version of malay-huggingface/wav2vec2-xls-r-300m-mixed on the uob\_singlish dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4822
* Wer: 0.2449
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #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:... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="danielcfho/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/... | danielcfho/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-15T03:32:10+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-generation | transformers |
<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/770573812991754240/gyUr2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/danny_macaskill-martynashton/1655269165002/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/danny_macaskill-martynashton | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T03:58:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Danny MacAskill & Martyn Ashton
@danny\_macaskill-martynashton
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 ... | [] | [
"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. -->
# sst2
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args": "sst2"}, "metric... | Alireza1044/mobilebert_sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T04:16:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# sst2
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1730
- Accuracy: 0.9037
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluat... | [
"# sst2\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE SST2 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1730\n- Accuracy: 0.9037",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# sst2\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE SST2 dataset.\nI... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmroberta-finetune-en-cnn
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## M... | {"tags": ["summarization", "en", "ecnoder-decoder", "xlmroberta", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "xlmroberta-finetune-en-cnn", "results": []}]} | ahmeddbahaa/xlmroberta-finetune-en-cnn | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"ecnoder-decoder",
"xlmroberta",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T04:22:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #en #ecnoder-decoder #xlmroberta #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
|
# xlmroberta-finetune-en-cnn
This model is a fine-tuned version of [](URL on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamete... | [
"# xlmroberta-finetune-en-cnn\n\nThis model is a fine-tuned version of [](URL on the cnn_dailymail dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #en #ecnoder-decoder #xlmroberta #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlmroberta-finetune-en-cnn\n\nThis model is a f... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-mam
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["mam"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-mam | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"mam",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T04:23:30+00:00 | [] | [
"mam"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #mam #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-mam
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - MAM (Mam) ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #mam #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text2text-generation | transformers |
# SlovakT5-small
This model was trained on slightly adapted code from [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling).
If you want to know about training details or evaluation results, see [SlovakT5_report.pdf](https://huggingface.co/ApoTro/slovak-t5-small/r... | {"language": "sk", "license": "mit", "datasets": ["oscar"]} | ApoTro/slovak-t5-small | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"sk",
"dataset:oscar",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T04:40:06+00:00 | [] | [
"sk"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #sk #dataset-oscar #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SlovakT5-small
This model was trained on slightly adapted code from run_t5_mlm_flax.py.
If you want to know about training details or evaluation results, see SlovakT5_report.pdf. For evaluation, you can also run SlovakT5_eval.ipynb.
### How to use
SlovakT5-small can be fine-tuned for a lot of different downstream ... | [
"# SlovakT5-small\nThis model was trained on slightly adapted code from run_t5_mlm_flax.py. \nIf you want to know about training details or evaluation results, see SlovakT5_report.pdf. For evaluation, you can also run SlovakT5_eval.ipynb.",
"### How to use\nSlovakT5-small can be fine-tuned for a lot of different ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #sk #dataset-oscar #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# SlovakT5-small\nThis model was trained on slightly adapted code from run_t5_mlm_flax.py. \nIf you want to know about training detai... |
null | null | ---cum
license: afl-3.0
---
| {} | antifa/cum | null | [
"region:us"
] | null | 2022-06-15T05:02:49+00:00 | [] | [] | TAGS
#region-us
| ---cum
license: afl-3.0
---
| [] | [
"TAGS\n#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/795028567398576128/GG1GU... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/wikisignpost/1655274233816/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/wikisignpost | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T05:07:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
The Signpost
@wikisignpost
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"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 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"], "pipeline_tag": "sentence-similarity"} | sdugar/cross-en-de-fr-xlmr-200d-sentence-transformer | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T06:00:45+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 200 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 200 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 #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 200 dimensional dense vector space and can be used for tasks like clustering or semantic ... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Corianas/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-15T06:00:47+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
image-classification | transformers |
# dog-vs-chicken
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | lewtun/dog-vs-chicken | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T06:08:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# dog-vs-chicken
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### crispy fried chicken
!crispy fried chicken
#### poodle
!poodle | [
"# dog-vs-chicken\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### crispy fried chicken\n\n!crispy fried chicken",
"#### poodle\n\n!poodle"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# dog-vs-chicken\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issue... |
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. -->
# biobert-base-cased-v1.2-finetuned-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingfa... | {"tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "ncbi_disease", "type": "n... | hossay/biobert-base-cased-v1.2-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T06:19:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #model-index #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.2-finetuned-ner
=====================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0706
* Precision: 0.8396
* Recall: 0.8731
* F1: 0.8561
* Accuracy: 0.982... | [
"### 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 #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #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*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | mmeet611/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T06:33:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3052
- Accuracy: 0.8633
- F1: 0.8629
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3052\n- Accuracy: 0.8633\n- F1: 0.8629",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
text-generation | transformers |
# LucaChangretta DialoGPT Model | {"tags": ["conversational"]} | Browbon/DialoGPT-medium-LucaChangretta | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T06:41:33+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# LucaChangretta DialoGPT Model | [
"# LucaChangretta DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# LucaChangretta DialoGPT Model"
] |
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/1440653599420268547/-h0y... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/ravenel_jeremy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T06:42:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jeremy Ravenel
@ravenel\_jeremy
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/albert-base-v2-mlm-electronics-attribute-correction
This model is a fine-tuned version of [ksabeh/albert-base-v2-mlm-electronic... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ksabeh/albert-base-v2-mlm-electronics-attribute-correction", "results": []}]} | ksabeh/albert-base-v2-attribute-correction-mlm | null | [
"transformers",
"tf",
"albert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T06:46:56+00:00 | [] | [] | TAGS
#transformers #tf #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| ksabeh/albert-base-v2-mlm-electronics-attribute-correction
==========================================================
This model is a fine-tuned version of ksabeh/albert-base-v2-mlm-electronics on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0541
* Validation Loss: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 36852, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'Polynomi... |
fill-mask | 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. -->
# TEdetection_distiBERT_mLM_final_8e
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_mLM_final_8e", "results": []}]} | FritzOS/TEdetection_distiBERT_mLM_final_8e | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T06:55:17+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TEdetection_distiBERT_mLM_final_8e
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data... | [
"# TEdetection_distiBERT_mLM_final_8e\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TEdetection_distiBERT_mLM_final_8e\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following re... |
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-base-qqp
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE QQP datas... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roberta-base-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "args": "qqp"}... | JeremiahZ/roberta-base-qqp | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T07:17:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-qqp
================
This model is a fine-tuned version of roberta-base on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4435
* Accuracy: 0.9153
* F1: 0.8867
* Combined Score: 0.9010
Model description
-----------------
More information needed
Intended us... | [
"### 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: 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\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #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\\_rat... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Klinsc/firstTake | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-15T07:20:32+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
<!-- 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. -->
# opus-mt-ar-en-finetuned-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["news_commentary"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ar-en-finetuned-ar-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "news_commentary",... | shurafa16/opus-mt-ar-en-finetuned-ar-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:news_commentary",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T07:20:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-news_commentary #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ar-en-finetuned-ar-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on the news\_commentary dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6933
* Bleu: 32.8872
* Gen Len: 56.084
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-news_commentary #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* ... |
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | amartyobanerjee/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T07:33:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8560
- Bleu: 52.8311
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8560\n- Bleu: 52.8311",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
null | fastai |
#Project Description
The project aims to classify a given text input of symptoms into a medical specialty that might be relevant to the symptoms. The model classifies the input into one of the 28 specialties, such as gastroenterologist, pediatrics, etc., that it was trained in.
#Model Description
This model is a fi... | {"tags": ["fastai"]} | arshy/medicalspecialty-deberta | null | [
"fastai",
"region:us"
] | null | 2022-06-15T07:44:38+00:00 | [] | [] | TAGS
#fastai #region-us
|
#Project Description
The project aims to classify a given text input of symptoms into a medical specialty that might be relevant to the symptoms. The model classifies the input into one of the 28 specialties, such as gastroenterologist, pediatrics, etc., that it was trained in.
#Model Description
This model is a fi... | [] | [
"TAGS\n#fastai #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **BeamRiderNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BeamRiderNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for St... | {"library_name": "stable-baselines3", "tags": ["BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BeamRiderNoFrameskip-v4... | Corianas/dqn-BeamRiderNoFrameskip-v4 | null | [
"stable-baselines3",
"BeamRiderNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-15T07:55:40+00:00 | [] | [] | TAGS
#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing BeamRiderNoFrameskip-v4
This is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents include... | [
"# DQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained age... | [
"TAGS\n#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL ... |
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. -->
# gpt2-covid
This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/PlanTL-GOB-ES/gpt2-base-bn... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-covid", "results": []}]} | roscazo/gpt2-covid | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T07:55:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-covid
This model is a fine-tuned version of PlanTL-GOB-ES/gpt2-base-bne on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# gpt2-covid\n\nThis model is a fine-tuned version of PlanTL-GOB-ES/gpt2-base-bne on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-covid\n\nThis model is a fine-tuned version of PlanTL-GOB-ES/gpt2-base-bne on an unknown dataset.",
"## Mod... |
null | null | hello
| {"license": "afl-3.0"} | walston/magichub-accented-mandarin-chinese-asr-challenge | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-06-15T07:56:15+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| hello
| [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing_ft_wav2vec2-large-xlsr-53
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https:/... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing_ft_wav2vec2-large-xlsr-53", "results": []}]} | gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T07:57:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_singing\_ft\_wav2vec2-large-xlsr-53
===================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4327
* Wer: 0.2043... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sarcasm-detection-Bert-base-uncased-newdata
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/ber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sarcasm-detection-Bert-base-uncased-newdata", "results": []}]} | jkhan447/sarcasm-detection-Bert-base-uncased-newdata | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T08:29:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sarcasm-detection-Bert-base-uncased-newdata
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5383
- Accuracy: 0.7766
## Model description
More information needed
## Intended uses & limitations
More information neede... | [
"# sarcasm-detection-Bert-base-uncased-newdata\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5383\n- Accuracy: 0.7766",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMor... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sarcasm-detection-Bert-base-uncased-newdata\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TEdetection_distiBERT_NER_final_8e
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_final_8e](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_NER_final_8e", "results": []}]} | FritzOS/TEdetection_distiBERT_NER_final_8e | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T08:36:53+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TEdetection\_distiBERT\_NER\_final\_8e
======================================
This model is a fine-tuned version of FritzOS/TEdetection\_distiBERT\_mLM\_final\_8e on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0032
* Validation Loss: 0.0037
* Epoch: 0
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learn... |
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/1425907860773515264/a30I... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/contrapoints-iamcardib/1655286883789/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/contrapoints-iamcardib | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T08:53:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Natalie Wynn & Cardi B
@contrapoints-iamcardib
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. -->
# xlm-roberta-large-xnli-finetuned-mnli
This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-large-xnli-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}, "metri... | tuni/xlm-roberta-large-xnli-finetuned-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T08:57:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-large-xnli-finetuned-mnli
=====================================
This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2542
* Accuracy: 0.8549
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-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\\_rat... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | winson/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T09:06:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.1139
- eval_runtime: 1.8873
- eval_samples_per_second: 529.866
- eval_steps_per_second: 8.478
- step: 0
## Model descri... | [
"# distilbert-base-uncased-finetuned-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 3.1139\n- eval_runtime: 1.8873\n- eval_samples_per_second: 529.866\n- eval_steps_per_second: 8.478\n- step: 0",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nI... |
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