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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-sent
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sent", "results": []}]} | mekarahul/distilbert-base-uncased-finetuned-sent | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T13:43:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sent
======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5482
* Accuracy: 0.48
* F1: 0.3658
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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: linear\n* num\\_epochs: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_norm300_aug5_10_8x_plus_8_10_4x
This model is a fine-tuned version of [distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm300_aug5_10_8x_plus_8_10_4x", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm300_aug5_10_8x_plus_8_10_4x | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T13:51:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm300\_aug5\_10\_8x\_plus\_8\_10\_4x
================================================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.07... | [
"### 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: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers | # Romanian paraphrase
Fine-tune t5-base-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own [dataset](https://huggingface.co/datasets/BlackKakapo/paraphrase-ro-v2). The dataset contains ~30k ex... | {"language": ["ro"], "license": ["apache-2.0"], "tags": [], "annotations_creators": [], "language_creators": ["machine-generated"], "multilinguality": ["monolingual"], "pretty_name": "BlackKakapo/t5-base-paraphrase-ro", "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text2text-g... | BlackKakapo/t5-base-paraphrase-ro-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ro",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-19T13:51:23+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Romanian paraphrase
!v2.0
Fine-tune t5-base-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own dataset. The dataset contains ~30k examples.
### How to use
### Or
### Generate
### Output
| [
"# Romanian paraphrase\n\n!v2.0\n\nFine-tune t5-base-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own dataset. The dataset contains ~30k examples.",
"### How to use",
"### Or",
"### Generate",
"### Output"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Romanian paraphrase\n\n!v2.0\n\nFine-tune t5-base-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had... |
fill-mask | transformers | # MWP-BERT
NAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
[](https://paperswithcode.com/sota/math-word-problem... | {"license": "afl-3.0"} | invokerliang/MWP-BERT-en | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-19T13:54:10+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # MWP-BERT
NAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
![PWC](URL
![PWC](URL
Github link: URL
| [
"# MWP-BERT\nNAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving\n\n![PWC](URL\n![PWC](URL\n\nGithub link: URL"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MWP-BERT\nNAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving\n\n![PWC](URL\n![PWC](URL\n\nGithub link: URL"
] |
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. -->
# ruRoberta-large-finetuned
This model is a fine-tuned version of [sberbank-ai/ruRoberta-large](https://huggingface.co/sberbank-ai... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ruRoberta-large-finetuned", "results": []}]} | rugo/ruRoberta-large-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T14:03:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| ruRoberta-large-finetuned
=========================
This model is a fine-tuned version of sberbank-ai/ruRoberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6045
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_... |
fill-mask | transformers | # MWP-BERT
NAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
[](https://paperswithcode.com/sota/math-word-problem... | {"license": "afl-3.0"} | invokerliang/MWP-BERT-zh | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T14:03:11+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| # MWP-BERT
NAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
![PWC](URL
![PWC](URL
Github link: URL
Please use the tokenizer of "hfl/chinese-bert-wwm-ext" for this model.
| [
"# MWP-BERT\nNAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving\n\n![PWC](URL\n![PWC](URL\n\nGithub link: URL\n\n\nPlease use the tokenizer of \"hfl/chinese-bert-wwm-ext\" for this model."
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# MWP-BERT\nNAACL 2022 Findings Paper: MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving\n\n
| {"license": "agpl-3.0"} | Thamognya/Bert-Base-Emotion-Sentiment-Analysis | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T14:12:26+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Bert-Base-Emotion-Sentiment-Analysis
Github Src: URL
| [
"# Bert-Base-Emotion-Sentiment-Analysis\n\nGithub Src: URL"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bert-Base-Emotion-Sentiment-Analysis\n\nGithub Src: URL"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | nicjac/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T15:10:00+00:00 | [] | [] | TAGS
#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0755
* Accuracy: 0.9752
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e... |
null | null | # RNA_Project
# Projeto Final - Modelos Preditivos Conexionistas
### Aluno - Caio Emanoel Serpa Lopes
### Tutor - Vitor Casadei
---
|**Tipo de Projeto**|**Modelo Selecionado**|**Linguagem**|
|--|--|--|
|Classificação de Imagens|MobileNetV2|Tensorflow|
[Clique aqui para rodar o modelo via browser (roboflow)](https:... | {} | caioeserpa/MobileNetV2_RNA_Class | null | [
"region:us"
] | null | 2022-08-19T15:10:04+00:00 | [] | [] | TAGS
#region-us
| RNA\_Project
============
Projeto Final - Modelos Preditivos Conexionistas
================================================
### Aluno - Caio Emanoel Serpa Lopes
### Tutor - Vitor Casadei
---
Tipo de Projeto: Classificação de Imagens, Modelo Selecionado: MobileNetV2, Linguagem: Tensorflow
Clique aqui para ... | [
"### Aluno - Caio Emanoel Serpa Lopes",
"### Tutor - Vitor Casadei\n\n\n\n\n---\n\n\nTipo de Projeto: Classificação de Imagens, Modelo Selecionado: MobileNetV2, Linguagem: Tensorflow\n\n\nClique aqui para rodar o modelo via browser (roboflow)\n\n\nPerformance\n===========\n\n\nO modelo treinado possui performance... | [
"TAGS\n#region-us \n",
"### Aluno - Caio Emanoel Serpa Lopes",
"### Tutor - Vitor Casadei\n\n\n\n\n---\n\n\nTipo de Projeto: Classificação de Imagens, Modelo Selecionado: MobileNetV2, Linguagem: Tensorflow\n\n\nClique aqui para rodar o modelo via browser (roboflow)\n\n\nPerformance\n===========\n\n\nO modelo tr... |
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. -->
# ruBert-base-finetuned
This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "ruBert-base-finetuned", "results": []}]} | rugo/ruBert-base-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T15:12:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ruBert-base-finetuned
=====================
This model is a fine-tuned version of sberbank-ai/ruBert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8911
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch\\_size: 16\... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | shabohin/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-19T15:32:35+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
text-classification | transformers |
This model was an experiment BUT NOT THE FINAL MODEL.
The final model was ***annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal*** (https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal)
Please consider using/trying that model instead.
This model was an experiment for the followin... | {} | annahaz/xlm-roberta-base-misogyny-sexism-tweets | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T16:14:54+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This model was an experiment BUT NOT THE FINAL MODEL.
The final model was *annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal* (URL
Please consider using/trying that model instead.
This model was an experiment for the following paper BUT THIS MODEL IS NOT THE FINAL MODEL:
---
license: mit
tags:
* g... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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 fram... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | jackoyoungblood/qrdqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T16:20:37+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN 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 ag... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN 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... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
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-en-in-lm
This model is a fine-tuned version of [crossdelenna/wav2vec2-large-en-in-lm](https://huggingface.co/cro... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-en-in-lm", "results": []}]} | crossdelenna/wav2vec2-large-en-in-lm | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T16:26:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wav2vec2-large-en-in-lm
=======================
This model is a fine-tuned version of crossdelenna/wav2vec2-large-en-in-lm
It achieves the following results on the evaluation set:
* Loss: 0.0478
* Wer: 0.0951
Model description
-----------------
Wav2vec2 Automatic speech recognition for Indian English accent u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_si... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole8", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"t... | Mahmoud7/Reinforce-CartPole8 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-19T16:46:48+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-a-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-a-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/rel... | research-backup/roberta-large-semeval2012-v2-average-no-mask-prompt-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T17:06:50+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-a-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Que... | [
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_sim... |
fill-mask | transformers |
## HindRoBERTa
HindRoBERTa is a Hindi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi monolingual datasets.
[project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [<a href='ht... | {"language": "hi", "license": "cc-by-4.0"} | l3cube-pune/hindi-roberta | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"hi",
"arxiv:2211.11418",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T17:21:53+00:00 | [
"2211.11418"
] | [
"hi"
] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #hi #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## HindRoBERTa
HindRoBERTa is a Hindi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi monolingual datasets.
[project link] (URL
More details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] .
Citing:
Other Monoli... | [
"## HindRoBERTa\nHindRoBERTa is a Hindi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi monolingual datasets. \n[project link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] .\n\nCiting:\n\n\... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #hi #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## HindRoBERTa\nHindRoBERTa is a Hindi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi monolingual... |
token-classification | transformers | # tner/deberta-v3-large-ontonotes5
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-pa... | {"datasets": ["tner/ontonotes5"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-ontonotes5", "results": [{"task": {"type":... | tner/deberta-v3-large-ontonotes5 | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/ontonotes5",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T17:22:34+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/ontonotes5 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-ontonotes5
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/ontonotes5 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.9069623608411381
- P... | [
"# tner/deberta-v3-large-ontonotes5\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/ontonotes5 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.906962360... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/ontonotes5 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-v3-large-ontonotes5\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/ontonotes5 dataset.\nModel fine-tu... |
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... | rhiga/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T17:32:06+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
fill-mask | transformers |
## HindAlBERT
HindAlBERT is a Hindi AlBERT model model trained on publicly available Hindi monolingual datasets.
[project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] (<a href='... | {"language": "hi", "license": "cc-by-4.0"} | l3cube-pune/hindi-albert | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"hi",
"arxiv:2211.11418",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T17:36:25+00:00 | [
"2211.11418"
] | [
"hi"
] | TAGS
#transformers #pytorch #albert #fill-mask #hi #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## HindAlBERT
HindAlBERT is a Hindi AlBERT model model trained on publicly available Hindi monolingual datasets.
[project link] (URL
More details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] (<a href='URL pdf </a>)
Other Monolingual Indic BERT models are listed below:... | [
"## HindAlBERT\nHindAlBERT is a Hindi AlBERT model model trained on publicly available Hindi monolingual datasets. \n[project link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] (<a href='URL pdf </a>)\n\n\n\nOther Monolingual Indic BERT models are l... | [
"TAGS\n#transformers #pytorch #albert #fill-mask #hi #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## HindAlBERT\nHindAlBERT is a Hindi AlBERT model model trained on publicly available Hindi monolingual datasets. \n[project link] (URL\n\nMore details on the data... |
fill-mask | transformers |
## HindBERT
HindBERT is a Hindi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on publicly available Hindi monolingual datasets.
[project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [<a href='ht... | {"language": "hi", "license": "cc-by-4.0"} | l3cube-pune/hindi-bert-v1 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"hi",
"arxiv:2211.11418",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T17:40:30+00:00 | [
"2211.11418"
] | [
"hi"
] | TAGS
#transformers #pytorch #bert #fill-mask #hi #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## HindBERT
HindBERT is a Hindi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on publicly available Hindi monolingual datasets.
[project link] (URL
More details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] <br>
A new version of mod... | [
"## HindBERT\nHindBERT is a Hindi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on publicly available Hindi monolingual datasets. \n[project link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] <br>\nA new versi... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #hi #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## HindBERT\nHindBERT is a Hindi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on publicly available Hindi monolingual datase... |
fill-mask | transformers |
## HindBERT
HindBERT is a Hindi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi monolingual datasets.
[project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [<a href='https:/... | {"language": "hi", "license": "cc-by-4.0"} | l3cube-pune/hindi-bert-v2 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"hi",
"arxiv:2211.11418",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T17:42:53+00:00 | [
"2211.11418"
] | [
"hi"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #hi #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## HindBERT
HindBERT is a Hindi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi monolingual datasets.
[project link] (URL
More details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>]
Citing:
Other Monolingual ... | [
"## HindBERT\nHindBERT is a Hindi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi monolingual datasets. \n[project link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] \n\nCiting:\n\n\nOther... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #hi #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## HindBERT\nHindBERT is a Hindi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi monolingua... |
fill-mask | transformers |
## DevRoBERTa
DevRoBERTa is a Devanagari RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi and Marathi monolingual datasets.
[project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in o... | {"language": ["hi", "mr", "multilingual"], "license": "cc-by-4.0"} | l3cube-pune/hindi-marathi-dev-roberta | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"hi",
"mr",
"multilingual",
"arxiv:2211.11418",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T17:49:21+00:00 | [
"2211.11418"
] | [
"hi",
"mr",
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #hi #mr #multilingual #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## DevRoBERTa
DevRoBERTa is a Devanagari RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi and Marathi monolingual datasets.
[project link] (URL
More details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] .
Citing... | [
"## DevRoBERTa\nDevRoBERTa is a Devanagari RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi and Marathi monolingual datasets. \n[project link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] .\... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #hi #mr #multilingual #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## DevRoBERTa\nDevRoBERTa is a Devanagari RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly availa... |
fill-mask | transformers |
## DevAlBERT
DevAlBERT is a Devanagari AlBERT model model trained on publicly available Hindi and Marathi monolingual datasets.
[project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper <... | {"language": ["hi", "mr", "multilingual"], "license": "cc-by-4.0"} | l3cube-pune/hindi-marathi-dev-albert | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"hi",
"mr",
"multilingual",
"arxiv:2211.11418",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T18:19:20+00:00 | [
"2211.11418"
] | [
"hi",
"mr",
"multilingual"
] | TAGS
#transformers #pytorch #albert #fill-mask #hi #mr #multilingual #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## DevAlBERT
DevAlBERT is a Devanagari AlBERT model model trained on publicly available Hindi and Marathi monolingual datasets.
[project link] (URL
More details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] .
Citing:
Other Monolingual Indic BERT models are listed below... | [
"## DevAlBERT\nDevAlBERT is a Devanagari AlBERT model model trained on publicly available Hindi and Marathi monolingual datasets. \n[project link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [<a href='URL paper </a>] .\n\nCiting:\n\n\nOther Monolingual Indic BERT models are... | [
"TAGS\n#transformers #pytorch #albert #fill-mask #hi #mr #multilingual #arxiv-2211.11418 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## DevAlBERT\nDevAlBERT is a Devanagari AlBERT model model trained on publicly available Hindi and Marathi monolingual datasets. \n[project link]... |
null | pythae |
### Downloading this model from the Hub
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_aae")
```
## Reproducibility
This trained mod... | {"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]} | clementchadebec/reproduced_aae | null | [
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-08-19T18:24:05+00:00 | [] | [
"en"
] | TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us
| ### Downloading this model from the Hub
This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'
Reproducibility
---------------
This trained model reproduces the results of Table 1 in [1].
[1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto... | [
"### Downloading this model from the Hub\n\n\nThis model was trained with pythae. It can be downloaded or reloaded using the method 'load\\_from\\_hf\\_hub'\n\n\nReproducibility\n---------------\n\n\nThis trained model reproduces the results of Table 1 in [1].\n\n\n\n[1] Tolstikhin, O Bousquet, S Gelly, and B Schöl... | [
"TAGS\n#pythae #reproducibility #en #license-apache-2.0 #region-us \n",
"### Downloading this model from the Hub\n\n\nThis model was trained with pythae. It can be downloaded or reloaded using the method 'load\\_from\\_hf\\_hub'\n\n\nReproducibility\n---------------\n\n\nThis trained model reproduces the results ... |
null | pythae |
### Downloading this model from the Hub
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_wae")
```
## Reproducibility
This trained mod... | {"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]} | clementchadebec/reproduced_wae | null | [
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-08-19T18:25:06+00:00 | [] | [
"en"
] | TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us
| ### Downloading this model from the Hub
This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'
Reproducibility
---------------
This trained model reproduces the results of Table 1 in [1].
[1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto... | [
"### Downloading this model from the Hub\n\n\nThis model was trained with pythae. It can be downloaded or reloaded using the method 'load\\_from\\_hf\\_hub'\n\n\nReproducibility\n---------------\n\n\nThis trained model reproduces the results of Table 1 in [1].\n\n\n\n[1] Tolstikhin, O Bousquet, S Gelly, and B Schöl... | [
"TAGS\n#pythae #reproducibility #en #license-apache-2.0 #region-us \n",
"### Downloading this model from the Hub\n\n\nThis model was trained with pythae. It can be downloaded or reloaded using the method 'load\\_from\\_hf\\_hub'\n\n\nReproducibility\n---------------\n\n\nThis trained model reproduces the results ... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-PixelCopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | Mahmoud7/Reinforce-PixelCopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-19T18:28:33+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
null | pythae |
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_rae_gp")
```
## Reproducibility
This trained model reproduces the results of the off... | {"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]} | clementchadebec/reproduced_rae_gp | null | [
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-08-19T18:32:27+00:00 | [] | [
"en"
] | TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us
| This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'
Reproducibility
---------------
This trained model reproduces the results of the official implementation of [1].
[1] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf. ... | [] | [
"TAGS\n#pythae #reproducibility #en #license-apache-2.0 #region-us \n"
] |
null | pythae |
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_rae_l2")
```
## Reproducibility
This trained model reproduces the results of the off... | {"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]} | clementchadebec/reproduced_rae_l2 | null | [
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-08-19T18:33:02+00:00 | [] | [
"en"
] | TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us
| This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'
Reproducibility
---------------
This trained model reproduces the results of the official implementation of [1].
[1] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf. ... | [] | [
"TAGS\n#pythae #reproducibility #en #license-apache-2.0 #region-us \n"
] |
null | pythae |
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_beta_tc_vae")
```
## Reproducibility
This trained model reproduces the results of th... | {"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]} | clementchadebec/reproduced_beta_tc_vae | null | [
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-08-19T18:41:14+00:00 | [] | [
"en"
] | TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us
| This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'
Reproducibility
---------------
This trained model reproduces the results of the official implementation of [1].
[1] Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud. Isolating sources of di... | [] | [
"TAGS\n#pythae #reproducibility #en #license-apache-2.0 #region-us \n"
] |
null | pythae |
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_svae")
```
## Reproducibility
This trained model reproduces the results of Table 1 i... | {"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]} | clementchadebec/reproduced_svae | null | [
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-08-19T18:51:40+00:00 | [] | [
"en"
] | TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us
| This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'
Reproducibility
---------------
This trained model reproduces the results of Table 1 in [1].
[1] Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. Hyperspherical variational au... | [] | [
"TAGS\n#pythae #reproducibility #en #license-apache-2.0 #region-us \n"
] |
fill-mask | transformers |
# Model Description
TinyBioBERT is a distilled version of the [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2?text=The+goal+of+life+is+%5BMASK%5D.) which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset.
# Distillation Procedure
This model uses a unique dist... | {"license": "mit", "title": "README", "emoji": "\ud83c\udfc3", "colorFrom": "gray", "colorTo": "purple", "sdk": "static", "pinned": false} | nlpie/tiny-biobert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:2209.03182",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T18:57:54+00:00 | [
"2209.03182"
] | [] | TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Model Description
TinyBioBERT is a distilled version of the BioBERT which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset.
# Distillation Procedure
This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the st... | [
"# Model Description\nTinyBioBERT is a distilled version of the BioBERT which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset.",
"# Distillation Procedure\nThis model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Description\nTinyBioBERT is a distilled version of the BioBERT which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset.",
"#... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **CartPole-v1**
This is a trained model of a **QRDQN** agent playing **CartPole-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
rein... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v... | jackoyoungblood/qrdqn-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T19:32:50+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing CartPole-v1
This is a trained model of a QRDQN agent playing CartPole-v1
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with S... | [
"# QRDQN Agent playing CartPole-v1\nThis is a trained model of a QRDQN agent playing CartPole-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"#... | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing CartPole-v1\nThis is a trained model of a QRDQN agent playing CartPole-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1283149075
- CO2 Emissions (in grams): 7.7092
## Validation Metrics
- Loss: 0.551
- Accuracy: 0.849
- Macro F1: 0.632
- Micro F1: 0.849
- Weighted F1: 0.844
- Macro Precision: 0.632
- Micro Precision: 0.849
- Weighted Precision: ... | {"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["aujer/autotrain-data-not_interested_8_19"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 7.7092029324718965}} | aujer/ni_model_8_19 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:aujer/autotrain-data-not_interested_8_19",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T19:39:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_8_19 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1283149075
- CO2 Emissions (in grams): 7.7092
## Validation Metrics
- Loss: 0.551
- Accuracy: 0.849
- Macro F1: 0.632
- Micro F1: 0.849
- Weighted F1: 0.844
- Macro Precision: 0.632
- Micro Precision: 0.849
- Weighted Precision: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1283149075\n- CO2 Emissions (in grams): 7.7092",
"## Validation Metrics\n\n- Loss: 0.551\n- Accuracy: 0.849\n- Macro F1: 0.632\n- Micro F1: 0.849\n- Weighted F1: 0.844\n- Macro Precision: 0.632\n- Micro Precision: 0.849\n-... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_8_19 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1283149075\n- CO2 Emis... |
reinforcement-learning | stable-baselines3 |
# **DDPG** Agent playing **BipedalWalkerHardcore-v3**
This is a trained model of a **DDPG** agent playing **BipedalWalkerHardcore-v3**
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 fo... | {"library_name": "stable-baselines3", "tags": ["BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DDPG", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalkerHardcore... | jackoyoungblood/qrdqn-BipedalWalkerHardcore-v3 | null | [
"stable-baselines3",
"BipedalWalkerHardcore-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T20:09:07+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalkerHardcore-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DDPG Agent playing BipedalWalkerHardcore-v3
This is a trained model of a DDPG agent playing BipedalWalkerHardcore-v3
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 inc... | [
"# DDPG Agent playing BipedalWalkerHardcore-v3\nThis is a trained model of a DDPG agent playing BipedalWalkerHardcore-v3\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... | [
"TAGS\n#stable-baselines3 #BipedalWalkerHardcore-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DDPG Agent playing BipedalWalkerHardcore-v3\nThis is a trained model of a DDPG agent playing BipedalWalkerHardcore-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nTh... |
reinforcement-learning | stable-baselines3 |
# **DDPG** Agent playing **BipedalWalkerHardcore-v3**
This is a trained model of a **DDPG** agent playing **BipedalWalkerHardcore-v3**
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 fo... | {"library_name": "stable-baselines3", "tags": ["BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DDPG", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalkerHardcore... | jackoyoungblood/ddpg-BipedalWalkerHardcore-v3 | null | [
"stable-baselines3",
"BipedalWalkerHardcore-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T20:11:32+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalkerHardcore-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DDPG Agent playing BipedalWalkerHardcore-v3
This is a trained model of a DDPG agent playing BipedalWalkerHardcore-v3
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 inc... | [
"# DDPG Agent playing BipedalWalkerHardcore-v3\nThis is a trained model of a DDPG agent playing BipedalWalkerHardcore-v3\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... | [
"TAGS\n#stable-baselines3 #BipedalWalkerHardcore-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DDPG Agent playing BipedalWalkerHardcore-v3\nThis is a trained model of a DDPG agent playing BipedalWalkerHardcore-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nTh... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | dvalbuena1/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-19T20:22:08+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **quadrotor_multi** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "quadrotor_multi", "type": "quadrotor_multi"}, "metrics"... | andrewzhang505/quad-swarm-rl-multi-drone-obstacles | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T20:33:48+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the quadrotor_multi environment.
This model was trained using Sample Factory 2.0: URL
| [] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n"
] |
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="dvalbuena1/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": ... | dvalbuena1/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-19T20:38:35+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="dvalbuena1/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 +/... | dvalbuena1/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-19T20:42:36+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | pythae |
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_vamp")
```
## Reproducibility
This trained model reproduces the results of Table 1 i... | {"language": "en", "license": "apache-2.0", "tags": ["pythae", "reproducibility"]} | clementchadebec/reproduced_vamp | null | [
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-08-19T20:52:47+00:00 | [] | [
"en"
] | TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us
| This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'
Reproducibility
---------------
This trained model reproduces the results of Table 1 in [1].
[1] Jakub Tomczak and Max Welling. Vae with a vampprior. In International Conference on Artificial Intelligence ... | [] | [
"TAGS\n#pythae #reproducibility #en #license-apache-2.0 #region-us \n"
] |
null | pythae |
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from pythae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_vae")
```
## Reproducibility
This trained model reproduces the results of the VAE us... | {"language": "en", "license": "apache-2.0", "library_name": "pythae", "tags": ["pythae", "reproducibility"]} | clementchadebec/reproduced_vae | null | [
"pythae",
"reproducibility",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-08-19T21:01:49+00:00 | [] | [
"en"
] | TAGS
#pythae #reproducibility #en #license-apache-2.0 #region-us
| This model was trained with pythae. It can be downloaded or reloaded using the method 'load\_from\_hf\_hub'
Reproducibility
---------------
This trained model reproduces the results of the VAE used in Table 1 in [1].
[1] Danilo Rezende and Shakir Mohamed. Variational inference with normalizing flows. In Internat... | [] | [
"TAGS\n#pythae #reproducibility #en #license-apache-2.0 #region-us \n"
] |
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... | marii/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T21:29:53+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... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-b-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-b-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/rel... | research-backup/roberta-large-semeval2012-v2-average-no-mask-prompt-b-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-19T22:10:39+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-b-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Que... | [
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_sim... |
text-generation | transformers |
<p align="center">
<img src="https://github.com/faGH/fa.creative/blob/master/Icons/FrostAura/FA%20Logo/FrostAura.Logo.Complex.png?raw=true" width="75" title="hover text">
</p>
# fa.intelligence.models.generative.novels.fiction
## Description
This FrostAura Intelligence model is a fine-tuned version of [EleutherAI/g... | {"language": ["en"], "license": "mit", "tags": ["text-generation", "novel-generation", "fiction", "gpt-neo-x", "pytorch"], "thumbnail": "https://github.com/faGH/fa.creative/blob/master/Icons/FrostAura/FA%20Logo/FrostAura.Logo.Complex.png?raw=true"} | FrostAura/gpt-neox-20b-fiction-novel-generation | null | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"novel-generation",
"fiction",
"gpt-neo-x",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-19T22:14:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neox #text-generation #novel-generation #fiction #gpt-neo-x #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
URL.fiction
===========
Description
-----------
This FrostAura Intelligence model is a fine-tuned version of EleutherAI/gpt-neox-20b for fictional text content generation.
Getting Started
---------------
### PIP Installation
### Usage
Further Fine-Tuning
-------------------
... | [
"### PIP Installation",
"### Usage\n\n\nFurther Fine-Tuning\n-------------------\n\n\n'in development'\n\n\nSupport\n-------\n\n\nIf you enjoy FrostAura open-source content and would like to support us in continuous delivery, please consider a donation via a platform of your choice.\n\n\n\nFor any queries, contac... | [
"TAGS\n#transformers #pytorch #gpt_neox #text-generation #novel-generation #fiction #gpt-neo-x #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### PIP Installation",
"### Usage\n\n\nFurther Fine-Tuning\n-------------------\n\n\n'in development'... |
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... | dvalbuena1/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T22:43:41+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... |
reinforcement-learning | stable-baselines3 |
# **DDPG** Agent playing **BipedalWalker-v3**
This is a trained model of a **DDPG** agent playing **BipedalWalker-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselin... | {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DDPG", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "B... | jackoyoungblood/ddpg-BipedalWalker-v3 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-19T23:02:40+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DDPG Agent playing BipedalWalker-v3
This is a trained model of a DDPG agent playing BipedalWalker-v3
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage... | [
"# DDPG Agent playing BipedalWalker-v3\nThis is a trained model of a DDPG agent playing BipedalWalker-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included... | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DDPG Agent playing BipedalWalker-v3\nThis is a trained model of a DDPG agent playing BipedalWalker-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training f... |
null | null |
## 라이브러리 버전
- transformers: 4.21.1
- datasets: 2.4.0
- tokenizers: 0.12.1
## 훈련 코드
```python
from datasets import load_dataset
from tokenizers import ByteLevelBPETokenizer
tokenizer = ByteLevelBPETokenizer(unicode_normalizer="nfkc", trim_offsets=True)
ds = load_dataset("Bingsu/my-korean-training-corpus", use_auth_... | {"language": ["ko"], "license": ["mit"], "tags": ["roberta", "tokenizer only"]} | Bingsu/ko_BBPE_tokenizer_roberta | null | [
"roberta",
"tokenizer only",
"ko",
"license:mit",
"region:us"
] | null | 2022-08-19T23:33:49+00:00 | [] | [
"ko"
] | TAGS
#roberta #tokenizer only #ko #license-mit #region-us
|
## 라이브러리 버전
- transformers: 4.21.1
- datasets: 2.4.0
- tokenizers: 0.12.1
## 훈련 코드
약 7시간 소모 (i5-12600 non-k)
!image
이후 토크나이저의 post-processor를 RobertaProcessing으로 교체합니다.
'add_prefix_space=False'옵션은 roberta-base를 그대로 따라하기 위한 것입니다.
그리고 'model_max_length' 설정을 해주었습니다.
저장된 폴더의 'tokenizer_config.json' 파일에 '"model... | [
"## 라이브러리 버전\n\n- transformers: 4.21.1\n- datasets: 2.4.0\n- tokenizers: 0.12.1",
"## 훈련 코드\n\n\n\n약 7시간 소모 (i5-12600 non-k)\n!image\n\n\n이후 토크나이저의 post-processor를 RobertaProcessing으로 교체합니다.\n\n\n'add_prefix_space=False'옵션은 roberta-base를 그대로 따라하기 위한 것입니다.\n\n그리고 'model_max_length' 설정을 해주었습니다.\n\n\n저장된 폴더의 'tokeni... | [
"TAGS\n#roberta #tokenizer only #ko #license-mit #region-us \n",
"## 라이브러리 버전\n\n- transformers: 4.21.1\n- datasets: 2.4.0\n- tokenizers: 0.12.1",
"## 훈련 코드\n\n\n\n약 7시간 소모 (i5-12600 non-k)\n!image\n\n\n이후 토크나이저의 post-processor를 RobertaProcessing으로 교체합니다.\n\n\n'add_prefix_space=False'옵션은 roberta-base를 그대로 따라하기 ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-fine-tuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "cola", "sp... | VanHoan/bert-fine-tuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T01:35:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-fine-tuned-cola
====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8408
* Matthews Correlation: 0.5981
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | jackoyoungblood/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-20T01:49:24+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
null | null |
## Anime Segmentation Models
models of [https://github.com/SkyTNT/anime-segmentation](https://github.com/SkyTNT/anime-segmentation)
| {"license": "apache-2.0"} | skytnt/anime-seg | null | [
"onnx",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-08-20T02:56:08+00:00 | [] | [] | TAGS
#onnx #license-apache-2.0 #has_space #region-us
|
## Anime Segmentation Models
models of URL
| [
"## Anime Segmentation Models\n\nmodels of URL"
] | [
"TAGS\n#onnx #license-apache-2.0 #has_space #region-us \n",
"## Anime Segmentation Models\n\nmodels of URL"
] |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-c-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-c-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/rel... | research-backup/roberta-large-semeval2012-v2-average-no-mask-prompt-c-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T03:15:11+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-c-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Que... | [
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_sim... |
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. -->
# results
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the N... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "results", "results": []}]} | zuu/youtube-content-summarization | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-20T03:50:45+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# results
This model is a fine-tuned version of facebook/bart-large-cnn on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fol... | [
"# results\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tra... | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# results\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on the None dataset.",
"## Mod... |
text-generation | transformers |
#Harry Potter Diablo GPT Model | {"tags": ["conversational"]} | shungyan/Diablo-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-20T05:27:43+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter Diablo GPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | ny7777/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-20T05:30:34+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
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. -->
# hubert-large-xlsr-common1000asli-demo-colab-dd
This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "hubert-large-xlsr-common1000asli-demo-colab-dd", "results": []}]} | saeedmaroof/hubert-large-xlsr-common1000asli-demo-colab-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T05:52:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| hubert-large-xlsr-common1000asli-demo-colab-dd
==============================================
This model is a fine-tuned version of facebook/hubert-large-ll60k on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0754
* Wer: 0.5189
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #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* tra... |
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. -->
# translation
This model is a fine-tuned version of [facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) on the... | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "translation", "results": []}]} | maximedb/massive_en_translation | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T06:25:06+00:00 | [] | [] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #generated_from_trainer #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# translation
This model is a fine-tuned version of facebook/nllb-200-3.3B on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The ... | [
"# translation\n\nThis model is a fine-tuned version of facebook/nllb-200-3.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### ... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #generated_from_trainer #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# translation\n\nThis model is a fine-tuned version of facebook/nllb-200-3.3B on the None dataset.",
"## Model description\n\nMore information n... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-pokemon-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggin... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/pokemon", "metrics": []} | ny7777/ddpm-pokemon-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/pokemon",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-20T06:52:37+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/pokemon #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-pokemon-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/pokemon' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describ... | [
"# ddpm-pokemon-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Trai... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/pokemon #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-pokemon-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/pokemon' dataset.",
"## Intended uses & limitations",
"#### How t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-53-torgo-8batch-30epochs-500steps
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xlsr-53-torgo-8batch-30epochs-500steps", "results": []}]} | ying-tina/wav2vec2-xlsr-53-torgo-8batch-30epochs-500steps | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T06:57:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-53-torgo-8batch-30epochs-500steps
===============================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0747
* Cer: 0.3015
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["custom_squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | msms/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:custom_squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T07:39:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-custom_squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the custom\_squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2055
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-custom_squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_ba... |
summarization | transformers | # long-t5-tglobal-small-dutch-cnn-bf16-test
See logs at https://wandb.ai/yepster/long-t5-tglobal-small-dutch-cnn/runs/1qmed8ll?workspace=user-yepster
| {"language": ["nl"], "license": "apache-2.0", "tags": ["summarization", "longt5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned", "yhavinga/cnn_dailymail_dutch"], "pipeline_tag": "summarization", "widget": [{"text": "Het Van Goghmuseum in Amsterdam heeft vier kostbare prenten verworven van Mary Cassatt, de Amerikaa... | yhavinga/long-t5-tglobal-small-dutch-cnn-bf16-test | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"longt5",
"text2text-generation",
"summarization",
"seq2seq",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"dataset:yhavinga/cnn_dailymail_dutch",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compati... | null | 2022-08-20T07:41:01+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #longt5 #text2text-generation #summarization #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #dataset-yhavinga/cnn_dailymail_dutch #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # long-t5-tglobal-small-dutch-cnn-bf16-test
See logs at URL
| [
"# long-t5-tglobal-small-dutch-cnn-bf16-test\n\n\nSee logs at URL"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #longt5 #text2text-generation #summarization #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #dataset-yhavinga/cnn_dailymail_dutch #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# long-t5-tglobal-small-dutch-... |
text-to-image | stable-diffusion |
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The **Stable-Diffusion-v-1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original)
checkpoint and... | {"license": "creativeml-openrail-m", "library_name": "stable-diffusion", "tags": ["stable-diffusion", "text-to-image"], "inference": false, "extra_gated_prompt": "One more step before getting this model.\nThis model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and ... | CompVis/stable-diffusion-v-1-4-original | null | [
"stable-diffusion",
"text-to-image",
"arxiv:2207.12598",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"has_space",
"region:us"
] | null | 2022-08-20T07:42:51+00:00 | [
"2207.12598",
"2112.10752",
"2103.00020",
"2205.11487",
"1910.09700"
] | [] | TAGS
#stable-diffusion #text-to-image #arxiv-2207.12598 #arxiv-2112.10752 #arxiv-2103.00020 #arxiv-2205.11487 #arxiv-1910.09700 #license-creativeml-openrail-m #has_space #region-us
|
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The Stable-Diffusion-v-1-4 checkpoint was initialized with the weights of the Stable-Diffusion-v-1-2
checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesth... | [
"#### Download the weights\n- URL\n- URL\n\nThese weights are intended to be used with the original CompVis Stable Diffusion codebase. If you are looking for the model to use with the Diffusers library, come here.",
"## Model Details\n- Developed by: Robin Rombach, Patrick Esser\n- Model type: Diffusion-based tex... | [
"TAGS\n#stable-diffusion #text-to-image #arxiv-2207.12598 #arxiv-2112.10752 #arxiv-2103.00020 #arxiv-2205.11487 #arxiv-1910.09700 #license-creativeml-openrail-m #has_space #region-us \n",
"#### Download the weights\n- URL\n- URL\n\nThese weights are intended to be used with the original CompVis Stable Diffusion c... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | HBtemari/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T07:57:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2125
* Accuracy: 0.927
* F1: 0.9272
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | rebolforces/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-20T08:10:58+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-d-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-d-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/rel... | research-backup/roberta-large-semeval2012-v2-average-no-mask-prompt-d-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T08:19:41+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-d-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Que... | [
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_sim... |
null | transformers |
# Erlangshen-DeBERTa-v2-97M-CWS-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLU任务，采用中文分词的，中文版的0.97亿参数DeBERTa-v2-Base。
Good at solving NLU tasks, adopting Chinese Word Segmentation (CWS), Chin... | {"language": ["zh"], "license": "apache-2.0", "tags": ["DeBERTa", "CWS", "Chinese Word Segmentation", "Chinese"], "inference": false} | IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-CWS-Chinese | null | [
"transformers",
"pytorch",
"deberta-v2",
"DeBERTa",
"CWS",
"Chinese Word Segmentation",
"Chinese",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"region:us"
] | null | 2022-08-20T09:30:29+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #deberta-v2 #DeBERTa #CWS #Chinese Word Segmentation #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #region-us
| Erlangshen-DeBERTa-v2-97M-CWS-Chinese
=====================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理NLU任务，采用中文分词的，中文版的0.97亿参数DeBERTa-v2-Base。
Good at solving NLU tasks, adopting Chinese Word Segmentation (CWS), Chinese DeBERTa-v2-Base wi... | [] | [
"TAGS\n#transformers #pytorch #deberta-v2 #DeBERTa #CWS #Chinese Word Segmentation #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #region-us \n"
] |
translation | transformers |
Logs at https://wandb.ai/yepster/byt5-small-ccmatrix-en-nl/runs/1wm9igj9?workspace=user-yepster
| {"language": ["nl", "en", "multilingual"], "license": "apache-2.0", "tags": ["byt5", "translation", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned", "yhavinga/ccmatrix"], "pipeline_tag": "translation", "widget": [{"text": "It is a painful and tragic spectacle that rises before me: I have drawn back the curtain from ... | yhavinga/byt5-small-ccmatrix-en-nl | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"byt5",
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"seq2seq",
"nl",
"en",
"multilingual",
"dataset:yhavinga/mc4_nl_cleaned",
"dataset:yhavinga/ccmatrix",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compa... | null | 2022-08-20T10:12:08+00:00 | [] | [
"nl",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #t5 #text2text-generation #byt5 #translation #seq2seq #nl #en #multilingual #dataset-yhavinga/mc4_nl_cleaned #dataset-yhavinga/ccmatrix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Logs at URL
| [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #t5 #text2text-generation #byt5 #translation #seq2seq #nl #en #multilingual #dataset-yhavinga/mc4_nl_cleaned #dataset-yhavinga/ccmatrix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
zero-shot-image-classification | null |
# Tiny CLIP
## Introduction
This is a smaller version of CLIP trained for EN only. The training script can be found [here](https://www.kaggle.com/code/sachin/tiny-en-clip/). This model is roughly 8 times smaller than CLIP. This was achieved by using a small text model (`microsoft/xtremedistil-l6-h256-uncased`) and a s... | {"language": ["en"], "license": "mit", "tags": ["zero-shot-image-classification"], "datasets": ["coco2017"]} | sachin/tiny_clip | null | [
"zero-shot-image-classification",
"en",
"dataset:coco2017",
"license:mit",
"region:us"
] | null | 2022-08-20T10:44:12+00:00 | [] | [
"en"
] | TAGS
#zero-shot-image-classification #en #dataset-coco2017 #license-mit #region-us
|
# Tiny CLIP
## Introduction
This is a smaller version of CLIP trained for EN only. The training script can be found here. This model is roughly 8 times smaller than CLIP. This was achieved by using a small text model ('microsoft/xtremedistil-l6-h256-uncased') and a small vision model ('edgenext_small'). For a in-depth... | [
"# Tiny CLIP",
"## Introduction\nThis is a smaller version of CLIP trained for EN only. The training script can be found here. This model is roughly 8 times smaller than CLIP. This was achieved by using a small text model ('microsoft/xtremedistil-l6-h256-uncased') and a small vision model ('edgenext_small'). For ... | [
"TAGS\n#zero-shot-image-classification #en #dataset-coco2017 #license-mit #region-us \n",
"# Tiny CLIP",
"## Introduction\nThis is a smaller version of CLIP trained for EN only. The training script can be found here. This model is roughly 8 times smaller than CLIP. This was achieved by using a small text model ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | HBtemari/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T11:17:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1365
* F1: 0.8649
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | bhavyasharma/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-20T11:30:01+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-to-image | diffusers |
# Stable Diffusion v1-4 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/st... | {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image"], "widget": [{"text": "A high tech solarpunk utopia in the Amazon rainforest", "example_title": "Amazon rainforest"}, {"text": "A pikachu fine dining with a view to the Eiffel Tower", "example_title": "Pikach... | CompVis/stable-diffusion-v1-4 | null | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"arxiv:2207.12598",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusio... | null | 2022-08-20T12:26:13+00:00 | [
"2207.12598",
"2112.10752",
"2103.00020",
"2205.11487",
"1910.09700"
] | [] | TAGS
#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #arxiv-2207.12598 #arxiv-2112.10752 #arxiv-2103.00020 #arxiv-2205.11487 #arxiv-1910.09700 #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
|
# Stable Diffusion v1-4 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion with Diffusers blog.
The Stable-Diffusion-v1-4 checkpoi... | [
"# Stable Diffusion v1-4 Model Card\n\nStable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.\nFor more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion with Diffusers blog.\n\nThe Stable-Diffusion-v1-4... | [
"TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #arxiv-2207.12598 #arxiv-2112.10752 #arxiv-2103.00020 #arxiv-2205.11487 #arxiv-1910.09700 #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n",
"# Stable Diffusi... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | HBtemari/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T12:28:20+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1608
* F1: 0.8593
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-generation | transformers |
<p align="center">
<img src="https://github.com/faGH/fa.creative/blob/master/Icons/FrostAura/FA%20Logo/FrostAura.Logo.Complex.png?raw=true" width="75" title="hover text">
</p>
# fa.intelligence.models.generative.novels.fiction
## Description
This FrostAura Intelligence model is a fine-tuned version of [EleutherAI/g... | {"language": ["en"], "license": "mit", "tags": ["text-generation", "novel-generation", "fiction", "gpt-neo", "pytorch"], "thumbnail": "https://github.com/faGH/fa.creative/blob/master/Icons/FrostAura/FA%20Logo/FrostAura.Logo.Complex.png?raw=true"} | FrostAura/gpt-neo-1.3B-fiction-novel-generation | null | [
"transformers",
"pytorch",
"jax",
"rust",
"gpt_neo",
"text-generation",
"novel-generation",
"fiction",
"gpt-neo",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T12:31:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #rust #gpt_neo #text-generation #novel-generation #fiction #gpt-neo #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
URL.fiction
===========
Description
-----------
This FrostAura Intelligence model is a fine-tuned version of EleutherAI/gpt-neo-1.3B for fictional text content generation.
Getting Started
---------------
### PIP Installation
### Usage
Further Fine-Tuning
-------------------
... | [
"### PIP Installation",
"### Usage\n\n\nFurther Fine-Tuning\n-------------------\n\n\nin development\n\n\nSupport\n-------\n\n\nIf you enjoy FrostAura open-source content and would like to support us in continuous delivery, please consider a donation via a platform of your choice.\n\n\n\nFor any queries, contact ... | [
"TAGS\n#transformers #pytorch #jax #rust #gpt_neo #text-generation #novel-generation #fiction #gpt-neo #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### PIP Installation",
"### Usage\n\n\nFurther Fine-Tuning\n-------------------\n\n\nin development\n\n\nSupport\n-------\n\n\nIf y... |
text2text-generation | transformers | important_labels = {
"no_relation":"관계 없음",
"per:employee_of":"고용",
"org:member_of":"소속",
"org:place_of_headquarters":"장소",
"org:top_members/employees":"대표",
"per:origin":"출신",
"per:title":"직업",
"per:colleagues":"동료",
"org:members":"소속",
"org:alternate_names":"본명",
"per:place... | {} | MrBananaHuman/re_generator | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T12:43:52+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| important_labels = {
"no_relation":"관계 없음",
"per:employee_of":"고용",
"org:member_of":"소속",
"org:place_of_headquarters":"장소",
"org:top_members/employees":"대표",
"per:origin":"출신",
"per:title":"직업",
"per:colleagues":"동료",
"org:members":"소속",
"org:alternate_names":"본명",
"per:place... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | ganger/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T13:20:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2152
* Accuracy: 0.927
* F1: 0.9270
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
feature-extraction | transformers | # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity_v2](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v2).
Fine-tuning is done via [RelBERT](https://... | {"datasets": ["relbert/semeval2012_relational_similarity_v2"], "model-index": [{"name": "relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/rel... | research-backup/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity_v2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T13:24:21+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce
RelBERT fine-tuned from roberta-large on
relbert/semeval2012_relational_similarity_v2.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Que... | [
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_similarity_v2.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity_v2 #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/semeval2012_relational_sim... |
sentence-similarity | sentence-transformers | # rufimelo/Legal-BERTimbau-sts-base-ma
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/rufimelo/Legal-BERTimbau-sts-base-ma is based on Legal-BERTimbau-base whic... | {"language": ["pt"], "tags": ["sentence-transformers", "sentence-similarity", "transformers"], "datasets": ["assin", "assin2", "stsb_multi_mt", "rufimelo/PortugueseLegalSentences-v0"], "thumbnail": "Portugues BERT for the Legal Domain", "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "O advogado a... | rufimelo/Legal-BERTimbau-sts-base-ma | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"pt",
"dataset:assin",
"dataset:assin2",
"dataset:stsb_multi_mt",
"dataset:rufimelo/PortugueseLegalSentences-v0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T13:24:34+00:00 | [] | [
"pt"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #pt #dataset-assin #dataset-assin2 #dataset-stsb_multi_mt #dataset-rufimelo/PortugueseLegalSentences-v0 #model-index #endpoints_compatible #region-us
| rufimelo/Legal-BERTimbau-sts-base-ma
====================================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/rufimelo/Legal-BERTimbau-sts-base-ma is based on Legal-BERTimba... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #pt #dataset-assin #dataset-assin2 #dataset-stsb_multi_mt #dataset-rufimelo/PortugueseLegalSentences-v0 #model-index #endpoints_compatible #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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | HBtemari/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T13:33:27+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2763
* F1: 0.8346
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | HBtemari/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T13:54:19+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2630
* F1: 0.8124
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-generation | null |
# RWKV-4 1.5B
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
## Model Description
RWKV-4 1.5B is a L24-D2048 causa... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["the_pile"]} | BlinkDL/rwkv-4-pile-1b5 | null | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"dataset:the_pile",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-08-20T13:56:55+00:00 | [] | [
"en"
] | TAGS
#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us
|
# RWKV-4 1.5B
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
## Model Description
RWKV-4 1.5B is a L24-D2048 causa... | [
"# RWKV-4 1.5B",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"## Model Description\n\nRWKV-4 1... | [
"TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us \n",
"# RWKV-4 1.5B",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
"# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.",
... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | HBtemari/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T14:13:09+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4043
* F1: 0.6886
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
translation | transformers |
# banglat5_nmt_bn_en
This repository contains the **BanglaT5** checkpoint finetuned on the [BanglaNMT]() Bengali-English dataset.
**Note**: The pretrained model uses a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). For best results, make sure the text units are normalize... | {"language": ["bn", "en", "multilingual"], "tags": ["translation"], "licenses": ["cc-by-nc-sa-4.0"]} | csebuetnlp/banglat5_nmt_bn_en | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"bn",
"en",
"multilingual",
"arxiv:2205.11081",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null | 2022-08-20T14:30:12+00:00 | [
"2205.11081"
] | [
"bn",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #bn #en #multilingual #arxiv-2205.11081 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space
| banglat5\_nmt\_bn\_en
=====================
This repository contains the BanglaT5 checkpoint finetuned on the BanglaNMT Bengali-English dataset.
Note: The pretrained model uses a specific normalization pipeline available here. For best results, make sure the text units are normalized using this library before token... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #translation #bn #en #multilingual #arxiv-2205.11081 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-en-demo
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-en-demo", "results": []}]} | NX2411/wav2vec2-large-xlsr-en-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T14:57:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-en-demo
===========================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1356
* Wer: 0.2015
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1... |
text2text-generation | transformers | # Commit Hash
1. BART 5 epoch training: 5e2267251ec1555e81f9ed6f090e1f70355ff1c8
2. BART 10 epoch training: 2e347c5f162fe18bb8d874d2bd0b46ae3d9ff175
3. BART 13 epoch training: 58b307615eb37f44a9233318427420b330fb6cea
# Dataset
[link](https://huggingface.co/datasets/Adapting/Knowledge-Driven-Dialogues)
# Training Resu... | {} | Adapting/Knowledge-Driven-Dialogue | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T14:57:54+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Commit Hash
===========
1. BART 5 epoch training: 5e2267251ec1555e81f9ed6f090e1f70355ff1c8
2. BART 10 epoch training: 2e347c5f162fe18bb8d874d2bd0b46ae3d9ff175
3. BART 13 epoch training: 58b307615eb37f44a9233318427420b330fb6cea
Dataset
=======
link
Training Results
================
Usage
=====
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-korean-speech-emotion-recognition3
This model is a fine-tuned version of [jungjongho/wav2vec2-large-xlsr-korean-de... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-xlsr-korean-speech-emotion-recognition3", "results": []}]} | jungjongho/wav2vec2-xlsr-korean-speech-emotion-recognition3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T15:13:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-korean-speech-emotion-recognition3
================================================
This model is a fine-tuned version of jungjongho/wav2vec2-large-xlsr-korean-demo-colab\_epoch15 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0600
* Accuracy: 0.9876
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* s... |
translation | transformers |
# banglat5_nmt_en_bn
This repository contains the **BanglaT5** checkpoint finetuned on the [BanglaNMT]() English-Bengali dataset.
**Note**: The pretrained model uses a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). For best results, make sure the text units are normalize... | {"language": ["en", "bn"], "tags": ["translation"], "licenses": ["cc-by-nc-sa-4.0"]} | csebuetnlp/banglat5_nmt_en_bn | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"en",
"bn",
"arxiv:2205.11081",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null | 2022-08-20T15:32:17+00:00 | [
"2205.11081"
] | [
"en",
"bn"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #en #bn #arxiv-2205.11081 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space
| banglat5\_nmt\_en\_bn
=====================
This repository contains the BanglaT5 checkpoint finetuned on the BanglaNMT English-Bengali dataset.
Note: The pretrained model uses a specific normalization pipeline available here. For best results, make sure the text units are normalized using this library before token... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #translation #en #bn #arxiv-2205.11081 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n"
] |
text-classification | transformers | --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 0.0002 --mlm \
4 layers | {} | alishudi/distil_mse_4 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T16:04:56+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 0.0002 --mlm \
4 layers | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# outputs
This model is a fine-tuned version of [mrm8488/t5-base-finetuned-common_gen](https://huggingface.co/mrm8488/t5-base-fine... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "outputs", "results": []}]} | nishita/outputs | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-20T17:09:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| outputs
=======
This model is a fine-tuned version of mrm8488/t5-base-finetuned-common\_gen on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2833
* Rouge1: 79.0721
* Rouge2: 59.355
* Rougel: 70.9787
* Rougelsum: 70.9177
* Gen Len: 16.3819
Model description
----------------... | [
"### 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: 100",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# Ahmed007/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Ahmed007/bert-finetuned-squad", "results": []}]} | Ahmed007/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T17:33:01+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Ahmed007/bert-finetuned-squad
=============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7829
* Epoch: 1
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 16635, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | rishistyping/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-20T18:16:21+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
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. -->
# tiny-bert-sst2-mobilebert-distillation
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-sst2-mobilebert-distillation", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "sst2... | gokuls/tiny-bert-sst2-mobilebert-distillation | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T18:52:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-bert-sst2-mobilebert-distillation
======================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2829
* Accuracy: 0.8394
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\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 #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
token-classification | transformers |
# DATASET
MilliyetNER dataset was collected from the Turkish Milliyet newspaper articles between 1997-1998. This dataset is presented by [Tür et al. (2003)](https://www.cambridge.org/core/journals/natural-language-engineering/article/abs/statistical-information-extraction-system-for-turkish/7C288FAFC71D5F0763C1F8CE664... | {"language": "tr", "tags": ["ner", "token-classification", "berturk", "turkish"], "datasets": ["MilliyetNER"], "widget": [{"text": "T\u00fcrkiye'nin ba\u015fkenti Ankara'd\u0131r ve ilk cumhurba\u015fkan\u0131 Mustafa Kemal Atat\u00fcrk't\u00fcr."}]} | alierenak/berturk_cased_ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"ner",
"berturk",
"turkish",
"tr",
"dataset:MilliyetNER",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T19:27:25+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #token-classification #ner #berturk #turkish #tr #dataset-MilliyetNER #autotrain_compatible #endpoints_compatible #region-us
|
# DATASET
MilliyetNER dataset was collected from the Turkish Milliyet newspaper articles between 1997-1998. This dataset is presented by Tür et al. (2003). It was collected from news articles and manually annotated with three different entity types: Person, Location, Organization. The authors did not provide training/... | [
"# USAGE",
"# BENCHMARKING"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #ner #berturk #turkish #tr #dataset-MilliyetNER #autotrain_compatible #endpoints_compatible #region-us \n",
"# USAGE",
"# BENCHMARKING"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **PushBlock**
This is a trained model of a **ppo** agent playing **PushBlock** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comp... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-PushBlock"]} | rebolforces/testpushblock | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-PushBlock",
"region:us"
] | null | 2022-08-20T20:23:39+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #region-us
|
# ppo Agent playing PushBlock
This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the train... | [
"# ppo Agent playing PushBlock\n This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #region-us \n",
"# ppo Agent playing PushBlock\n This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Docu... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1287149278
- CO2 Emissions (in grams): 0.0396
## Validation Metrics
- Loss: 0.264
- Accuracy: 0.907
- Precision: 0.681
- Recall: 0.539
- AUC: 0.843
- F1: 0.602
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["jawadhussein462/autotrain-data-neurips_chanllenge"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.039558027906151955}} | jawadhussein462/autotrain-neurips_chanllenge-1287149278 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:jawadhussein462/autotrain-data-neurips_chanllenge",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T21:28:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-jawadhussein462/autotrain-data-neurips_chanllenge #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1287149278
- CO2 Emissions (in grams): 0.0396
## Validation Metrics
- Loss: 0.264
- Accuracy: 0.907
- Precision: 0.681
- Recall: 0.539
- AUC: 0.843
- F1: 0.602
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1287149278\n- CO2 Emissions (in grams): 0.0396",
"## Validation Metrics\n\n- Loss: 0.264\n- Accuracy: 0.907\n- Precision: 0.681\n- Recall: 0.539\n- AUC: 0.843\n- F1: 0.602",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-jawadhussein462/autotrain-data-neurips_chanllenge #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1287149278\n- CO2 ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1287149282
- CO2 Emissions (in grams): 25.1387
## Validation Metrics
- Loss: 0.272
- Accuracy: 0.911
- Precision: 0.733
- Recall: 0.494
- AUC: 0.823
- F1: 0.591
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H ... | {"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["jawadhussein462/autotrain-data-neurips_chanllenge"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 25.138742530638098}} | jawadhussein462/autotrain-neurips_chanllenge-1287149282 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:jawadhussein462/autotrain-data-neurips_chanllenge",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T21:29:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-jawadhussein462/autotrain-data-neurips_chanllenge #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1287149282
- CO2 Emissions (in grams): 25.1387
## Validation Metrics
- Loss: 0.272
- Accuracy: 0.911
- Precision: 0.733
- Recall: 0.494
- AUC: 0.823
- F1: 0.591
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1287149282\n- CO2 Emissions (in grams): 25.1387",
"## Validation Metrics\n\n- Loss: 0.272\n- Accuracy: 0.911\n- Precision: 0.733\n- Recall: 0.494\n- AUC: 0.823\n- F1: 0.591",
"## Usage\n\nYou can use cURL to access this model... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-jawadhussein462/autotrain-data-neurips_chanllenge #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1287149282\n- C... |
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. -->
# 4-way-detection-prop-16-bert
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": "4-way-detection-prop-16-bert", "results": []}]} | ultra-coder54732/4-way-detection-prop-16-bert | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T23:08:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# 4-way-detection-prop-16-bert
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpa... | [
"# 4-way-detection-prop-16-bert\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proce... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# 4-way-detection-prop-16-bert\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutor... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | rebolforces/testworm | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-08-20T23:12:49+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us
|
# ppo Agent playing Worm
This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
#... | [
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the training\... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us \n",
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\... |
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. -->
# banglabert-finetuned-squad
This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/bangl... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "banglabert-finetuned-squad", "results": []}]} | shams/banglabert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-08-20T23:19:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
| banglabert-finetuned-squad
==========================
This model is a fine-tuned version of csebuetnlp/banglabert on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9322
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* ... |
null | null |
Info here: https://github.com/josephrocca/rwkv-v4-web | {"license": "mit"} | rocca/rwkv-4-pile-web | null | [
"onnx",
"license:mit",
"region:us"
] | null | 2022-08-21T00:04:16+00:00 | [] | [] | TAGS
#onnx #license-mit #region-us
|
Info here: URL | [] | [
"TAGS\n#onnx #license-mit #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. -->
# Cbert_base_ws-finetuned-ner
This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.co/ckiplab/... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Cbert_base_ws-finetuned-ner", "results": []}]} | HYM/Cbert_base_ws-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-21T00:12:06+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Cbert\_base\_ws-finetuned-ner
=============================
This model is a fine-tuned version of ckiplab/bert-base-chinese-ws on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0582
* Precision: 0.9602
* Recall: 0.9633
* F1: 0.9617
* Accuracy: 0.9827
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 18\n* eval\\_batch\\_size: 18\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 18\n* ev... |
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