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text-generation | transformers | skt에서 만든 gptdc를 ainize 서비스를 이용해서 훈련시키고 huggingface에서 시뮬레이션 합니다 | {} | juanna/gptdc | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T10:22:26+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| skt에서 만든 gptdc를 ainize 서비스를 이용해서 훈련시키고 huggingface에서 시뮬레이션 합니다 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# En-Nso
This model is a fine-tuned version of [kabelomalapane/en_nso_ukuxhumana_model](https://huggingface.co/kabelomalapane/en_n... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Nso", "results": []}]} | kabelomalapane/En-Nso | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T10:32:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| En-Nso
======
This model is a fine-tuned version of kabelomalapane/en\_nso\_ukuxhumana\_model on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9067
* Bleu: 23.5436
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: 4\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: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
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. -->
# TRY
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "TRY", "results": []}]} | Vikasbhandari/TRY | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T10:42:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# TRY
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4234
- eval_wer: 0.3884
- eval_runtime: 51.9275
- eval_samples_per_second: 32.353
- eval_steps_per_second: 4.044
- epoch: 7.03
- step: 3500
## Model descri... | [
"# TRY\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.4234\n- eval_wer: 0.3884\n- eval_runtime: 51.9275\n- eval_samples_per_second: 32.353\n- eval_steps_per_second: 4.044\n- epoch: 7.03\n- step: 3500",
... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TRY\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following results on the evaluation set:... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1539625904313360390/RV2f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dinidu/1657198765981/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dinidu | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T11:14:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Dinidu de Alwis
@dinidu
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft500_6class600
This model is a fine-tuned version of [distilbert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft500_6class600", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft500_6class600 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T11:40:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft500\_6class600
==================================================
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: 1.6317
* Accuracy: 0.35
* F1: 0.3327
Model description
-------... | [
"### 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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
null | keras |
## Model description
This repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. [Drug Molecule Generation with VAE](https://keras.io/examples/generative/molecule_generation/).
Full credits go to [Victor Basu](https://www.linkedin.com/in/victor-basu-520958147/)
R... | {"library_name": "keras"} | keras-io/drug-molecule-generation-with-VAE | null | [
"keras",
"tensorboard",
"has_space",
"region:us"
] | null | 2022-07-07T11:43:32+00:00 | [] | [] | TAGS
#keras #tensorboard #has_space #region-us
|
## Model description
This repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. Drug Molecule Generation with VAE.
Full credits go to Victor Basu
Reproduced by Vu Minh Chien
Motivation: Using a Variational Autoencoder to generate molecules for drug discovery. A... | [
"## Model description\n\nThis repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. Drug Molecule Generation with VAE.\n\nFull credits go to Victor Basu\n\nReproduced by Vu Minh Chien\n\nMotivation: Using a Variational Autoencoder to generate molecules for drug ... | [
"TAGS\n#keras #tensorboard #has_space #region-us \n",
"## Model description\n\nThis repo contains the model and the notebook for fine-tuning BERT model on SNLI Corpus for Semantic Similarity. Drug Molecule Generation with VAE.\n\nFull credits go to Victor Basu\n\nReproduced by Vu Minh Chien\n\nMotivation: Using a... |
image-classification | transformers |
# puppies_classify
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hug... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | cherrypaca/puppies_classify | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T12:25:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# puppies_classify
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### husky
!husky
#### pomeranian
!pomeranian | [
"# puppies_classify\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### husky\n\n!husky",
"#### pomeranian\n\n!pomeranian"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# puppies_classify\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any iss... |
video-classification | transformers |
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Kinetics-400 for 800 epochs in a self-supervised way. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and fi... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-base-short | null | [
"transformers",
"pytorch",
"videomae",
"pretraining",
"vision",
"video-classification",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-07T12:25:55+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Kinetics-400 for 800 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository.
D... | [
"# VideoMAE (base-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Kinetics-400 for 800 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repositor... | [
"TAGS\n#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (base-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Kinetics-400 for 800 epochs in a self-sup... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# turkishReviews-ds-mini
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves th... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "turkishReviews-ds-mini", "results": []}]} | kmkarakaya/turkishReviews-ds-mini | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T12:29:04+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| turkishReviews-ds-mini
======================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 9.1630
* Validation Loss: 9.2431
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay... |
text2text-generation | transformers | ## m2m100 fine-tuned on the ca_zh_wikipedia dataset for machine translation
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Training](#training)
- [Training data](#training-d... | {"language": ["ca", "zh"], "license": "cc-by-4.0", "datasets": ["projecte-aina/ca_zh_wikipedia"], "metrics": ["bleu"], "model-index": [{"name": "m2m100_418M_ft_zh_ca", "results": [{"task": {"type": "translation"}, "dataset": {"name": "Flores", "type": "flores"}, "metrics": [{"type": "bleu", "value": 18.0, "name": "BLEU... | projecte-aina/m2m100_418M_ft_zh_ca | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ca",
"zh",
"dataset:projecte-aina/ca_zh_wikipedia",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T12:39:44+00:00 | [] | [
"ca",
"zh"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #ca #zh #dataset-projecte-aina/ca_zh_wikipedia #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| m2m100 fine-tuned on the ca\_zh\_wikipedia dataset for machine translation
--------------------------------------------------------------------------
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Training
+ Training data
+ Training procedur... | [
"### Training data\n\n\nAs a data for fine-tuning we used the ca\\_zh\\_wikipedia dataset extracted from Wikipedia.",
"### Training procedure",
"#### Tokenization\n\n\nThe original m2m100\\_418M model's sentencepiece tokenizer was used. The fine-tuning dataset that contained both simplified and traditional Chin... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #ca #zh #dataset-projecte-aina/ca_zh_wikipedia #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nAs a data for fine-tuning we used the ca\\_zh\\_wikipedia dataset extracted from Wikipedia... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Nso-En
This model is a fine-tuned version of [kabelomalapane/nso_en_ukuxhumana_model](https://huggingface.co/kabelomalapane/nso_... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Nso-En", "results": []}]} | kabelomalapane/Nso-En | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T12:40:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Nso-En
======
This model is a fine-tuned version of kabelomalapane/nso\_en\_ukuxhumana\_model on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3144
* Bleu: 24.4184
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: 4\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: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
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. -->
# nl_electra
This model is a pretrained version of [ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra) on the Du... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "nl_electra", "results": []}]} | ajders/nl_electra | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T12:48:39+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| nl\_electra
===========
This model is a pretrained version of ELECTRA on the Dutch subset of the CC100 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4650
* Accuracy: 0.5392
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used ... | [
"### 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: 703\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #electra #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: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* ... |
text-classification | transformers |
#
[Debora Nozza](http://dnozza.github.io/) •
[Federico Bianchi](https://federicobianchi.io/) •
[Giuseppe Attanasio](https://gattanasio.cc/)
# HATE-ITA Base
HATE-ITA is a binary hate speech classification model for Italian social media text.
<img src="https://raw.githubusercontent.com/MilaNLProc/hate-ita/main/hat... | {"language": "it", "license": "gpl-3.0", "tags": ["text classification", "abusive language", "hate speech", "offensive language"], "widget": [{"text": "Ci sono dei bellissimi capibara!", "example_title": "Hate Speech Classification 1"}, {"text": "Sei una testa di cazzo!!", "example_title": "Hate Speech Classification 2... | MilaNLProc/hate-ita-xlm-r-base | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"text classification",
"abusive language",
"hate speech",
"offensive language",
"it",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T13:04:18+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #text classification #abusive language #hate speech #offensive language #it #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
Debora Nozza •
Federico Bianchi •
Giuseppe Attanasio
HATE-ITA Base
=============
HATE-ITA is a binary hate speech classification model for Italian social media text.
<img src="URL width="200">
Abstract
--------
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted proper... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #text classification #abusive language #hate speech #offensive language #it #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
#
[Debora Nozza](http://dnozza.github.io/) •
[Federico Bianchi](https://federicobianchi.io/) •
[Giuseppe Attanasio](https://gattanasio.cc/)
# HATE-ITA Large
HATE-ITA is a binary hate speech classification model for Italian social media text.
<img src="https://raw.githubusercontent.com/MilaNLProc/hate-ita/main/ha... | {"language": "it", "license": "gpl-3.0", "tags": ["text classification", "abusive language", "hate speech", "offensive language"], "widget": [{"text": "Ci sono dei bellissimi capibara!", "example_title": "Hate Speech Classification 1"}, {"text": "Sei una testa di cazzo!!", "example_title": "Hate Speech Classification 2... | MilaNLProc/hate-ita-xlm-r-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"text classification",
"abusive language",
"hate speech",
"offensive language",
"it",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T13:08:09+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #text classification #abusive language #hate speech #offensive language #it #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
Debora Nozza •
Federico Bianchi •
Giuseppe Attanasio
HATE-ITA Large
==============
HATE-ITA is a binary hate speech classification model for Italian social media text.
<img src="URL width="200">
Abstract
--------
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted prop... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #text classification #abusive language #hate speech #offensive language #it #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-redditComments
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-finetuned-redditComments", "results": []}]} | kuttersn/gpt2-finetuned-redditComments | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T13:15:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-finetuned-redditComments
=============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8418
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: 2\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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | mmazuecos/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-07T13:16:40+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... |
sentence-similarity | sentence-transformers |
# gemasphi/laprador_trained
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becom... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gemasphi/laprador_trained | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T13:25:03+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# gemasphi/laprador_trained
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
T... | [
"# gemasphi/laprador_trained\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers insta... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# gemasphi/laprador_trained\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks li... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_mod... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_modified_for_t5_qg"], "model-index": [{"name": "t5-end2end-questions-generation", "results": []}]} | Aayesha/t5-end2end-questions-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad_modified_for_t5_qg",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T13:32:07+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-end2end-questions-generation
===============================
This model is a fine-tuned version of t5-base on the squad\_modified\_for\_t5\_qg dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8015
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | mmazuecos/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-07T13:42:53+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 ... |
sentence-similarity | sentence-transformers |
# gemasphi/laprador_untrained
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model bec... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gemasphi/laprador_untrained | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T14:20:02+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# gemasphi/laprador_untrained
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
... | [
"# gemasphi/laprador_untrained\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers ins... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# gemasphi/laprador_untrained\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks ... |
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... | bothrajat/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T15:33:10+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 |
# **DQN** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BreakoutNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stab... | {"library_name": "stable-baselines3", "tags": ["BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BreakoutNoFrameskip-v4",... | bothrajat/dqn-BreakoutNoFrameskip-v4 | null | [
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T15:44:59+00:00 | [] | [] | TAGS
#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing BreakoutNoFrameskip-v4
This is a trained model of a DQN agent playing BreakoutNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.... | [
"# DQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a DQN agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agent... | [
"TAGS\n#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a DQN agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# europython-imdb
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "europython-imdb", "results": []}]} | Rocketknight1/europython-imdb | null | [
"transformers",
"tf",
"deberta-v2",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T15:56:10+00:00 | [] | [] | TAGS
#transformers #tf #deberta-v2 #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| europython-imdb
===============
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1279
* Train Accuracy: 0.9548
* Validation Loss: 0.1595
* Validation Accuracy: 0.9418
* Epoch: 1
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #deberta-v2 #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 2e-05,... |
null | transformers |
This is a test of my methodology. | {"license": "mit"} | ddegenaro/reu_midsummer_test | null | [
"transformers",
"pytorch",
"bert",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T16:06:58+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #license-mit #endpoints_compatible #region-us
|
This is a test of my methodology. | [] | [
"TAGS\n#transformers #pytorch #bert #license-mit #endpoints_compatible #region-us \n"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-oneindia
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) o... | {"language": ["ml"], "license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-oneindia", "results": []}]} | akhisreelibra/malayalam-summariser | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"ml",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T16:25:56+00:00 | [] | [
"ml"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #ml #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-oneindia
============================
This model is a fine-tuned version of google/mt5-small on URL dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0277
* Rouge1: 6.1146
* Rouge2: 0.9858
* Rougel: 6.085
* Rougelsum: 6.0965
Model description
-----------------
Model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #ml #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | Mascariddu8/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T16:34:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
image-segmentation | 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. -->
# segformer-b0-finetuned-segments-water-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-water-2", "results": []}]} | imadd/segformer-b0-finetuned-segments-water-2 | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T16:50:33+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-finetuned-segments-water-2
=======================================
This model is a fine-tuned version of nvidia/mit-b0 on the imadd/water\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5845
* Mean Iou: nan
* Mean Accuracy: nan
* Overall Accuracy: nan
* Per Category... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #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: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batc... |
token-classification | transformers | # tner/twitter-roberta-base-2019-90m-tweetner7-continuous
This model is a fine-tuned version of [tner/twitter-roberta-base-2019-90m-tweetner-2020](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` spli... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-2019-90m-tweetner7-continuous | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T17:02:47+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-2019-90m-tweetner7-continuous
This model is a fine-tuned version of tner/twitter-roberta-base-2019-90m-tweetner-2020 on the
tner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'.
Model fine-tuning is do... | [
"# tner/twitter-roberta-base-2019-90m-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/twitter-roberta-base-2019-90m-tweetner-2020 on the \ntner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'. \nModel fine-tun... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-2019-90m-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/twitter-roberta-base-2019-90m-tweetner-2020 on the ... |
token-classification | transformers | # tner/twitter-roberta-base-dec2020-tweetner7-continuous
This model is a fine-tuned version of [tner/twitter-roberta-base-dec2020-tweetner-2020](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner-2020) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split).... | {"datasets": ["tner/tweetner7"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}", "example_title": "NER Example 1"}], "model-index": [... | tner/twitter-roberta-base-dec2020-tweetner7-continuous | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T17:03:07+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/twitter-roberta-base-dec2020-tweetner7-continuous
This model is a fine-tuned version of tner/twitter-roberta-base-dec2020-tweetner-2020 on the
tner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'.
Model fine-tuning is done... | [
"# tner/twitter-roberta-base-dec2020-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/twitter-roberta-base-dec2020-tweetner-2020 on the \ntner/tweetner7 dataset ('train_2021' split). The model is first fine-tuned on 'train_2020', and then continuously fine-tuned on 'train_2021'. \nModel fine-tunin... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweetner7 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/twitter-roberta-base-dec2020-tweetner7-continuous\n\nThis model is a fine-tuned version of tner/twitter-roberta-base-dec2020-tweetner-2020 on the \n... |
text2text-generation | transformers |
# grammar-synthesis-large - beta
A fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset.
usage in Python (after `pip install transformers`):
```python
from transformer... | {"license": ["cc-by-nc-sa-4.0", "apache-2.0"], "tags": ["grammar", "spelling", "punctuation", "error-correction", "grammar synthesis"], "datasets": ["jfleg"], "widget": [{"text": "i can has cheezburger", "example_title": "cheezburger"}, {"text": "There car broke down so their hitching a ride to they're class.", "exampl... | pszemraj/grammar-synthesis-large | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"grammar",
"spelling",
"punctuation",
"error-correction",
"grammar synthesis",
"dataset:jfleg",
"arxiv:2107.06751",
"license:cc-by-nc-sa-4.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
... | null | 2022-07-07T17:05:46+00:00 | [
"2107.06751"
] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #grammar #spelling #punctuation #error-correction #grammar synthesis #dataset-jfleg #arxiv-2107.06751 #license-cc-by-nc-sa-4.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# grammar-synthesis-large - beta
A fine-tuned version of google/t5-v1_1-large for grammar correction on an expanded version of the JFLEG dataset.
usage in Python (after 'pip install transformers'):
give it a spin in Colab at this notebook
## Model description
The intent is to create a text2text language model ... | [
"# grammar-synthesis-large - beta\n\nA fine-tuned version of google/t5-v1_1-large for grammar correction on an expanded version of the JFLEG dataset.\n\nusage in Python (after 'pip install transformers'):\n\n\n\ngive it a spin in Colab at this notebook",
"## Model description\n\nThe intent is to create a text2tex... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #grammar #spelling #punctuation #error-correction #grammar synthesis #dataset-jfleg #arxiv-2107.06751 #license-cc-by-nc-sa-4.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# grammar-... |
automatic-speech-recognition | transformers | ## Wav2Vec2.0 XLSR-53 large model の日本語 Fine Tuning モデル
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)を日本語用にFine Tuningしたモデル
## 使用データセット
- [Common Voice](https://commonvoice.mozilla.org/ja)
## 使い方
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datas... | {} | kwmr/wav2vec2_japanese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T17:23:30+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Wav2Vec2.0 XLSR-53 large model の日本語 Fine Tuning モデル
facebook/wav2vec2-large-xlsr-53を日本語用にFine Tuningしたモデル
## 使用データセット
- Common Voice
## 使い方
| [
"## Wav2Vec2.0 XLSR-53 large model の日本語 Fine Tuning モデル\nfacebook/wav2vec2-large-xlsr-53を日本語用にFine Tuningしたモデル",
"## 使用データセット\n- Common Voice",
"## 使い方"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Wav2Vec2.0 XLSR-53 large model の日本語 Fine Tuning モデル\nfacebook/wav2vec2-large-xlsr-53を日本語用にFine Tuningしたモデル",
"## 使用データセット\n- Common Voice",
"## 使い方"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100
This model is a fine-tuned version of [bert-base-uncased](https://h... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100", "results": []}]} | hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T17:42:58+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/cchs-timebert-visit-uncased-wordlevel-block512-batch4-ep100
==================================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8009
* Epoch: 99
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-0... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100
This model is a fine-tuned version of [bert-base-uncased](https://h... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100", "results": []}]} | hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T17:58:20+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100
==================================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8536
* Epoch: 99
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-0... |
null | null |
<!-- 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. -->
# pr_dataset_metadata
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "pr_dataset_metadata", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config": "plain_text", "split": "... | loicmagne/pr_dataset_metadata | null | [
"pytorch",
"tensorboard",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-07-07T18:06:03+00:00 | [] | [] | TAGS
#pytorch #tensorboard #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #region-us
|
# pr_dataset_metadata
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6216
- eval_accuracy: 1.0
- eval_runtime: 0.4472
- eval_samples_per_second: 2.236
- eval_steps_per_second: 2.236
- step: 0
## Model descri... | [
"# pr_dataset_metadata\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6216\n- eval_accuracy: 1.0\n- eval_runtime: 0.4472\n- eval_samples_per_second: 2.236\n- eval_steps_per_second: 2.236\n- step: 0",
"... | [
"TAGS\n#pytorch #tensorboard #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #region-us \n",
"# pr_dataset_metadata\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.6216\n- eval_ac... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1191381171164237824/jdS9... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mcconaughey/1657221054082/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mcconaughey | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T18:10:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Matthew McConaughey
@mcconaughey
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | quanxi/TESTppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-07T18:11:34+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | transformers |
# Model description
An RoBERTa reading comprehension model for [SQuAD 1.1](https://aclanthology.org/D16-1264/).
The model is initialized with [roberta-large](https://huggingface.co/roberta-large/) and fine-tuned on the [SQuAD 1.1 train data](https://huggingface.co/datasets/squad).
## Intended uses & limitations
Y... | {"language": "en", "license": "apache-2.0", "tags": ["MRC", "SQuAD 1.1", "roberta-large"]} | PrimeQA/squad-v1-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"MRC",
"SQuAD 1.1",
"roberta-large",
"en",
"arxiv:1606.05250",
"arxiv:1907.11692",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T18:36:59+00:00 | [
"1606.05250",
"1907.11692"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #MRC #SQuAD 1.1 #roberta-large #en #arxiv-1606.05250 #arxiv-1907.11692 #license-apache-2.0 #endpoints_compatible #region-us
|
# Model description
An RoBERTa reading comprehension model for SQuAD 1.1.
The model is initialized with roberta-large and fine-tuned on the SQuAD 1.1 train data.
## Intended uses & limitations
You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, rob... | [
"# Model description\n\nAn RoBERTa reading comprehension model for SQuAD 1.1.\n\nThe model is initialized with roberta-large and fine-tuned on the SQuAD 1.1 train data.",
"## Intended uses & limitations\n\nYou can use the raw model for the reading comprehension task. Biases associated with the pre-existing langu... | [
"TAGS\n#transformers #pytorch #roberta #MRC #SQuAD 1.1 #roberta-large #en #arxiv-1606.05250 #arxiv-1907.11692 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model description\n\nAn RoBERTa reading comprehension model for SQuAD 1.1.\n\nThe model is initialized with roberta-large and fine-tuned on th... |
null | transformers |
# Model description
An XLM-RoBERTa reading comprehension model for [SQuAD 1.1](https://aclanthology.org/D16-1264/).
The model is initialized with [xlm-roberta-large](https://huggingface.co/xlm-roberta-large/) and fine-tuned on the [SQuAD 1.1 train data](https://huggingface.co/datasets/squad).
## Intended uses & li... | {"language": ["multilingual"], "license": "apache-2.0", "tags": ["MRC", "SQuAD 1.1", "xlm-roberta-large"]} | PrimeQA/squad-v1-xlm-roberta-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"MRC",
"SQuAD 1.1",
"xlm-roberta-large",
"multilingual",
"arxiv:1606.05250",
"arxiv:1910.11856",
"arxiv:1911.02116",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T18:46:24+00:00 | [
"1606.05250",
"1910.11856",
"1911.02116"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #MRC #SQuAD 1.1 #xlm-roberta-large #multilingual #arxiv-1606.05250 #arxiv-1910.11856 #arxiv-1911.02116 #license-apache-2.0 #endpoints_compatible #region-us
|
# Model description
An XLM-RoBERTa reading comprehension model for SQuAD 1.1.
The model is initialized with xlm-roberta-large and fine-tuned on the SQuAD 1.1 train data.
## Intended uses & limitations
You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language mo... | [
"# Model description\n\nAn XLM-RoBERTa reading comprehension model for SQuAD 1.1.\n\nThe model is initialized with xlm-roberta-large and fine-tuned on the SQuAD 1.1 train data.",
"## Intended uses & limitations\n\nYou can use the raw model for the reading comprehension task. Biases associated with the pre-existi... | [
"TAGS\n#transformers #pytorch #xlm-roberta #MRC #SQuAD 1.1 #xlm-roberta-large #multilingual #arxiv-1606.05250 #arxiv-1910.11856 #arxiv-1911.02116 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model description\n\nAn XLM-RoBERTa reading comprehension model for SQuAD 1.1.\n\nThe model is initialized ... |
text2text-generation | transformers | Moran and Aviv project for solving Summarization task.
We choose 2 architectures: TextRank and BART (facebook).
In Streamlit' application, you can enter your article as an input, and the output is a summary.
Inspired by HIT studies. | {} | Aviv/Moran_Aviv_Bart | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-07T18:46:46+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Moran and Aviv project for solving Summarization task.
We choose 2 architectures: TextRank and BART (facebook).
In Streamlit' application, you can enter your article as an input, and the output is a summary.
Inspired by HIT studies. | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1104340243
- CO2 Emissions (in grams): 27.982443349742287
## Validation Metrics
- Loss: 0.9584922790527344
- Accuracy: 0.5843
- Macro F1: 0.5801009597024507
- Micro F1: 0.5843
- Weighted F1: 0.5792137097243996
- Macro Precision: ... | {"language": "en", "tags": "autotrain", "datasets": ["mbyanfei/autotrain-data-amazon-shoe-reviews-classification"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 27.982443349742287} | mbyanfei/autotrain-amazon-shoe-reviews-classification-1104340243 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:mbyanfei/autotrain-data-amazon-shoe-reviews-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T18:48:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-mbyanfei/autotrain-data-amazon-shoe-reviews-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1104340243
- CO2 Emissions (in grams): 27.982443349742287
## Validation Metrics
- Loss: 0.9584922790527344
- Accuracy: 0.5843
- Macro F1: 0.5801009597024507
- Micro F1: 0.5843
- Weighted F1: 0.5792137097243996
- Macro Precision: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1104340243\n- CO2 Emissions (in grams): 27.982443349742287",
"## Validation Metrics\n\n- Loss: 0.9584922790527344\n- Accuracy: 0.5843\n- Macro F1: 0.5801009597024507\n- Micro F1: 0.5843\n- Weighted F1: 0.5792137097243996\n... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-mbyanfei/autotrain-data-amazon-shoe-reviews-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1... |
text2text-generation | transformers | Extractive Summarization | {} | Moran/Moran_Aviv_Bart | null | [
"transformers",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T19:17:10+00:00 | [] | [] | TAGS
#transformers #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Extractive Summarization | [] | [
"TAGS\n#transformers #bart #text2text-generation #autotrain_compatible #endpoints_compatible #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="phyous/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"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": ... | phyous/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-07T19:31:33+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1423289998544044032/vc29... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/gassy_dragon/1657227895422/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/gassy_dragon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T20:02:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Bau be tootin on ur butt.
@gassy\_dragon
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Traini... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# FelipeAD/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-s... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "FelipeAD/mt5-small-finetuned-amazon-en-es", "results": []}]} | FelipeAD/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T20:08:36+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| FelipeAD/mt5-small-finetuned-amazon-en-es
=========================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.0682
* Validation Loss: 3.3902
* Epoch: 7
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 9672, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
token-classification | spacy | ### Details: https://spacy.io/models/ca#ca_core_news_sm
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ca_core_news_sm` |
| **Version** | `3.3.0` |
| **spaCy** | `>=3.3.0.dev0,<3.4.0` |
| *... | {"language": ["ca"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | osanseviero/ca_core_news_sm | null | [
"spacy",
"token-classification",
"ca",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-07-07T20:22:35+00:00 | [] | [
"ca"
] | TAGS
#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (316 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nCatalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (316 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ca #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nCatalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (316 labels for 3 components)",
... |
token-classification | spacy | ### Details: https://spacy.io/models/en#en_core_web_sm
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `en_core_web_sm` |
| **Version** | `3.3.0` |
| **spaCy** | `>=3.3.0.dev0,<3.4.0` |
| **Default ... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | osanseviero/en_core_web_sm | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"region:us"
] | null | 2022-07-07T20:28:43+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #region-us
| ### Details: URL
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (112 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nEnglish pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (112 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nEnglish pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (112 labels for 3 components)",
"### Accura... |
object-detection | transformers | # YOLOS (small-sized) model
The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [th... | {"license": "apache-2.0", "tags": ["object-detection", "license-plate-detection", "vehicle-detection"], "datasets": ["coco", "license-plate-detection"], "metrics": ["average precision", "recall", "IOU"], "widget": [{"src": "https://drive.google.com/uc?id=1j9VZQ4NDS4gsubFf3m2qQoTMWLk552bQ", "example_title": "Skoda 1"}, ... | nickmuchi/yolos-small-rego-plates-detection | null | [
"transformers",
"pytorch",
"safetensors",
"yolos",
"object-detection",
"license-plate-detection",
"vehicle-detection",
"dataset:coco",
"dataset:license-plate-detection",
"arxiv:2106.00666",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-07T20:31:21+00:00 | [
"2106.00666"
] | [] | TAGS
#transformers #pytorch #safetensors #yolos #object-detection #license-plate-detection #vehicle-detection #dataset-coco #dataset-license-plate-detection #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| YOLOS (small-sized) model
=========================
The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repositor... | [
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe YOLOS model was pre-trained on ImageNet-1k and fine-tuned on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/... | [
"TAGS\n#transformers #pytorch #safetensors #yolos #object-detection #license-plate-detection #vehicle-detection #dataset-coco #dataset-license-plate-detection #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model:\n\n\nCurrently, ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | tfshaman/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T20:36:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7786
* Accuracy: 0.9158
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1486954631464771591/cwgD... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/fairytale_bot23/1657230245911/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/fairytale_bot23 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T20:43:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Fairytale Generator
@fairytale\_bot23
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# fancy-animales
Just for fun and to test the template!
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/h... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | andy-0v0/fancy-animales | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T21:16:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# fancy-animales
Just for fun and to test the template!
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### chow chow
!chow chow
#### panda
!panda
#### penguin
!penguin
#### sloth
!sloth
#### wombat... | [
"# fancy-animales\n\nJust for fun and to test the template!\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### chow chow\n\n!chow chow",
"#### panda\n\n!panda",
"#### penguin\n\n!penguin",
... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# fancy-animales\n\nJust for fun and to test the template!\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport an... |
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-hinglish
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-hinglish", "results": []}]} | sam34738/bert-hinglish | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T22:37:37+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-hinglish
=============
This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5475
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-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 #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* e... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1
This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_w... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1", "results": []}]} | gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T23:35:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_singing3\_ft\_wav2vec2-large-xlsr-53-v1
=======================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing3\_ft\_wav2vec2-large-xlsr-53-v1 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evalu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* ... |
text-generation | transformers |
# PlordBot - medium | {"tags": ["conversational"]} | JamesStratford/PLord-bot-DialoGPT-medium | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-07T23:59:21+00:00 | [] | [] | TAGS
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|
# PlordBot - medium | [
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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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. -->
# Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model
This model is a fine-tuned version of [emilyalsentzer/Bio_Clinic... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model", "results": []}]} | okho0653/Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model | null | [
"transformers",
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"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
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"region:us"
] | null | 2022-07-08T00:09:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information need... | [
"# Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\... | [
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"# Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model\n\nThis model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It ac... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dummy-model", "results": []}]} | ankitsharma/dummy-model | null | [
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"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T00:49:43+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dummy-model
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
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"## Mod... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | eplatas/distilroberta-base-finetuned-wikitext2 | null | [
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"region:us"
] | null | 2022-07-08T00:52:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8359
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
null | null |
# D&D&VQGAN
## Intro
As I get a chance to play around with a lot more of these models. I find myself wanting to create D&D (or general fantasy and Sci-Fi themed images) generated from text prompt (think of what you see being implemented now in AI Dungeon). | {"license": "cc"} | katharsis/vqgan-imagenet-dnd | null | [
"license:cc",
"region:us"
] | null | 2022-07-08T00:57:03+00:00 | [] | [] | TAGS
#license-cc #region-us
|
# D&D&VQGAN
## Intro
As I get a chance to play around with a lot more of these models. I find myself wanting to create D&D (or general fantasy and Sci-Fi themed images) generated from text prompt (think of what you see being implemented now in AI Dungeon). | [
"# D&D&VQGAN",
"## Intro \n\nAs I get a chance to play around with a lot more of these models. I find myself wanting to create D&D (or general fantasy and Sci-Fi themed images) generated from text prompt (think of what you see being implemented now in AI Dungeon)."
] | [
"TAGS\n#license-cc #region-us \n",
"# D&D&VQGAN",
"## Intro \n\nAs I get a chance to play around with a lot more of these models. I find myself wanting to create D&D (or general fantasy and Sci-Fi themed images) generated from text prompt (think of what you see being implemented now in AI Dungeon)."
] |
text-generation | transformers |
#Cat DialoGPT Model | {"tags": ["conversational"]} | CaptPyrite/DialoGPT-small-cat | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-08T01:17:50+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Cat DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
# OFA-huge
## Introduction
This is the **huge** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple... | {"license": "apache-2.0"} | OFA-Sys/ofa-huge | null | [
"transformers",
"pytorch",
"ofa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T01:57:17+00:00 | [] | [] | TAGS
#transformers #pytorch #ofa #license-apache-2.0 #endpoints_compatible #region-us
|
# OFA-huge
## Introduction
This is the huge version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple seq... | [
"# OFA-huge",
"## Introduction\nThis is the huge version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a s... | [
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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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | Shenghao1993/distilbert-base-uncased-distilled-clinc | null | [
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"dataset:clinc_oos",
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"region:us"
] | null | 2022-07-08T02:23:11+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3120
* Accuracy: 0.9455
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 9",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_wav2vec2_s878
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your spee... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_wav2vec2_s878 | null | [
"transformers",
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"automatic-speech-recognition",
"en",
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_wav2vec2_s878
Fine-tuned facebook/wav2vec2-large-lv60 for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
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automatic-speech-recognition | transformers | # exp_w2v2t_en_wav2vec2_s924
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your spee... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_wav2vec2_s924 | null | [
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"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T02:56:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_wav2vec2_s924
Fine-tuned facebook/wav2vec2-large-lv60 for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
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object-detection | pytorch |
# Model Card for yolov6n
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#m... | {"language": "en", "license": "gpl-3.0", "library_name": "pytorch", "tags": ["object-detection", "yolo", "autogenerated-modelcard"], "model_name": "yolov6n"} | nateraw/yolov6n | null | [
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"object-detection",
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"1910.09700"
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"en"
] | TAGS
#pytorch #object-detection #yolo #autogenerated-modelcard #en #arxiv-1910.09700 #license-gpl-3.0 #region-us
|
# Model Card for yolov6n
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
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object-detection | pytorch |
# Model Card for yolov6s
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#m... | {"language": "en", "license": "gpl-3.0", "library_name": "pytorch", "tags": ["object-detection", "yolo", "autogenerated-modelcard"], "model_name": "yolov6s"} | nateraw/yolov6s | null | [
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"yolo",
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"1910.09700"
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#pytorch #object-detection #yolo #autogenerated-modelcard #en #arxiv-1910.09700 #license-gpl-3.0 #has_space #region-us
|
# Model Card for yolov6s
# Table of Contents
1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
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object-detection | pytorch |
# Model Card for yolov6t
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#m... | {"language": "en", "license": "gpl-3.0", "library_name": "pytorch", "tags": ["object-detection", "yolo", "autogenerated-modelcard"], "model_name": "yolov6t"} | nateraw/yolov6t | null | [
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"object-detection",
"yolo",
"autogenerated-modelcard",
"en",
"arxiv:1910.09700",
"license:gpl-3.0",
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] | null | 2022-07-08T03:19:38+00:00 | [
"1910.09700"
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"en"
] | TAGS
#pytorch #object-detection #yolo #autogenerated-modelcard #en #arxiv-1910.09700 #license-gpl-3.0 #region-us
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# Model Card for yolov6t
# Table of Contents
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4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
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automatic-speech-recognition | transformers | # exp_w2v2t_en_wav2vec2_s203
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your spee... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_wav2vec2_s203 | null | [
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"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T03:23:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_wav2vec2_s203
Fine-tuned facebook/wav2vec2-large-lv60 for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_wav2vec2_s203\n\nFine-tuned facebook/wav2vec2-large-lv60 for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_wav2vec2_s203\n\nFine-tuned facebook/wav2vec2-large-lv60 for speech recognition on English using the train split of Commo... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-100k_s807
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make ... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-100k_s807 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T03:32:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-100k_s807
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-100k_s807\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-100k_s807\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-100k_s421
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make ... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-100k_s421 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T03:43:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-100k_s421
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-100k_s421\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-100k_s421\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split... |
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. -->
# phobert-base-finetuned-imdb
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base... | {"tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "phobert-base-finetuned-imdb", "results": []}]} | ChauNguyen23/phobert-base-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T03:47:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-imdb #autotrain_compatible #endpoints_compatible #region-us
| phobert-base-finetuned-imdb
===========================
This model is a fine-tuned version of vinai/phobert-base on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6149
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-imdb #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: 64\n* ... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-100k_s364
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make ... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-100k_s364 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T03:56:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-100k_s364
Fine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-100k_s364\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-100k_s364\n\nFine-tuned facebook/wav2vec2-large-100k-voxpopuli for speech recognition on English using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_xlsr-53_s870
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_xlsr-53_s870 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:06:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_xlsr-53_s870
Fine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_xlsr-53_s870\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_xlsr-53_s870\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Com... |
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="phyous/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"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": "7.44 +/... | phyous/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-08T04:12:07+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"
] |
automatic-speech-recognition | transformers | # exp_w2v2t_en_xlsr-53_s769
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_xlsr-53_s769 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:18:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_xlsr-53_s769
Fine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_xlsr-53_s769\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_xlsr-53_s769\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Com... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_xlsr-53_s279
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_xlsr-53_s279 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:26:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_xlsr-53_s279
Fine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_xlsr-53_s279\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_xlsr-53_s279\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 for speech recognition on English using the train split of Com... |
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="phyous/q-Taxi-v3-2", 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-v3-2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 ... | phyous/q-Taxi-v3-2 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-08T04:27:28+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"
] |
automatic-speech-recognition | transformers | # exp_w2v2t_en_unispeech_s870
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_unispeech_s870 | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:30:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_unispeech_s870
Fine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_unispeech_s870\n\nFine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_unispeech_s870\n\nFine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split ... |
image-classification | timm | # Model card for resnet50d | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | nateraw/resnet50d | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-07-08T04:34:34+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for resnet50d | [
"# Model card for resnet50d"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for resnet50d"
] |
automatic-speech-recognition | transformers | # exp_w2v2t_en_unispeech_s227
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_unispeech_s227 | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:35:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_unispeech_s227
Fine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_unispeech_s227\n\nFine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_unispeech_s227\n\nFine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split ... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_unispeech_s809
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_unispeech_s809 | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:41:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_unispeech_s809
Fine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_unispeech_s809\n\nFine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_unispeech_s809\n\nFine-tuned microsoft/unispeech-large-1500h-cv for speech recognition on English using the train split ... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_hubert_s875
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech i... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_hubert_s875 | null | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:45:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #hubert #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_hubert_s875
Fine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_hubert_s875\n\nFine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #hubert #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_hubert_s875\n\nFine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voi... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_hubert_s596
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech i... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_hubert_s596 | null | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:49:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #hubert #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_hubert_s596
Fine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_hubert_s596\n\nFine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #hubert #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_hubert_s596\n\nFine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voi... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Cartpole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | igpaub/Reinforce-Cartpole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-08T04:53:59+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... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_hubert_s877
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech i... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_hubert_s877 | null | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T04:54:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #hubert #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_hubert_s877
Fine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_hubert_s877\n\nFine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #hubert #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_hubert_s877\n\nFine-tuned facebook/hubert-large-ll60k for speech recognition on English using the train split of Common Voi... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-sv_s179
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-sv_s179 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:01:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-sv_s179
Fine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-sv_s179\n\nFine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-sv_s179\n\nFine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of ... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-sv_s320
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-sv_s320 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:06:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-sv_s320
Fine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-sv_s320\n\nFine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-sv_s320\n\nFine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of ... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-sv_s438
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-sv_s438 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:11:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-sv_s438
Fine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-sv_s438\n\nFine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-sv_s438\n\nFine-tuned facebook/wav2vec2-large-sv-voxpopuli for speech recognition on English using the train split of ... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_no-pretraining_s883
Fine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model ... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_no-pretraining_s883 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:16:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_no-pretraining_s883
Fine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_no-pretraining_s883\n\nFine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_no-pretraining_s883\n\nFine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train s... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_no-pretraining_s289
Fine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model ... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_no-pretraining_s289 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:21:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_no-pretraining_s289
Fine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_no-pretraining_s289\n\nFine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_no-pretraining_s289\n\nFine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train s... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_no-pretraining_s852
Fine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model ... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_no-pretraining_s852 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:26:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_no-pretraining_s852
Fine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_no-pretraining_s852\n\nFine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_no-pretraining_s852\n\nFine-tuned randomly initialized wav2vec2 model for speech recognition on English using the train s... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_wavlm_s767
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampl... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_wavlm_s767 | null | [
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:32:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_wavlm_s767
Fine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_wavlm_s767\n\nFine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wavlm #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_wavlm_s767\n\nFine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.\... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_wavlm_s461
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampl... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_wavlm_s461 | null | [
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:39:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_wavlm_s461
Fine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_wavlm_s461\n\nFine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wavlm #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_wavlm_s461\n\nFine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.\... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_wavlm_s990
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampl... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_wavlm_s990 | null | [
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:47:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_wavlm_s990
Fine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_wavlm_s990\n\nFine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wavlm #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_wavlm_s990\n\nFine-tuned microsoft/wavlm-large for speech recognition on English using the train split of Common Voice 7.0.\... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_unispeech-ml_s377
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
Whe... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_unispeech-ml_s377 | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:52:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_unispeech-ml_s377
Fine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_unispeech-ml_s377\n\nFine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_unispeech-ml_s377\n\nFine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_unispeech-ml_s103
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
Whe... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_unispeech-ml_s103 | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T05:57:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_unispeech-ml_s103
Fine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_unispeech-ml_s103\n\nFine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_unispeech-ml_s103\n\nFine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using... |
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. -->
# wiki2json
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achiev... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus_books"], "metrics": ["bleu"], "model-index": [{"name": "wiki2json", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "opus_books", "type": "opus_books", "args": "e... | jourlin/wiki2json | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-08T05:58:40+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-opus_books #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| wiki2json
=========
This model is a fine-tuned version of t5-small on the opus\_books dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6848
* Bleu: 4.8968
* Gen Len: 17.6362
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: 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: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-opus_books #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_unispeech-ml_s756
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
Whe... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_unispeech-ml_s756 | null | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T06:04:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_unispeech-ml_s756
Fine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_unispeech-ml_s756\n\nFine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #unispeech #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_unispeech-ml_s756\n\nFine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition on English using... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-fr_s118
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-fr_s118 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T06:12:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-fr_s118
Fine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-fr_s118\n\nFine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-fr_s118\n\nFine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of ... |
question-answering | transformers |
# bert-ancient-chinese-base-ud-head
## Model Description
This is a BERT model pre-trained on Classical Chinese texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from [bert-ancient-chinese](https://huggingface.co/Jihuai/bert-ancient-chinese) and [UD_Classical_Chine... | {"language": ["lzh"], "license": "apache-2.0", "tags": ["classical chinese", "literary chinese", "ancient chinese", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u7a... | KoichiYasuoka/bert-ancient-chinese-base-ud-head | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"classical chinese",
"literary chinese",
"ancient chinese",
"dependency-parsing",
"lzh",
"dataset:universal_dependencies",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T06:18:22+00:00 | [] | [
"lzh"
] | TAGS
#transformers #pytorch #bert #question-answering #classical chinese #literary chinese #ancient chinese #dependency-parsing #lzh #dataset-universal_dependencies #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-ancient-chinese-base-ud-head
## Model Description
This is a BERT model pre-trained on Classical Chinese texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from bert-ancient-chinese and UD_Classical_Chinese-Kyoto. Use [MASK] inside 'context' to avoid ambiguit... | [
"# bert-ancient-chinese-base-ud-head",
"## Model Description\n\nThis is a BERT model pre-trained on Classical Chinese texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from bert-ancient-chinese and UD_Classical_Chinese-Kyoto. Use [MASK] inside 'context' to avoi... | [
"TAGS\n#transformers #pytorch #bert #question-answering #classical chinese #literary chinese #ancient chinese #dependency-parsing #lzh #dataset-universal_dependencies #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-ancient-chinese-base-ud-head",
"## Model Description\n\nThis is a BERT model pr... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-fr_s691
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-fr_s691 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T06:20:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-fr_s691
Fine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-fr_s691\n\nFine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-fr_s691\n\nFine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of ... |
automatic-speech-recognition | transformers | # exp_w2v2t_en_vp-fr_s51
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure th... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2t_en_vp-fr_s51 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-08T06:28:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2t_en_vp-fr_s51
Fine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2t_en_vp-fr_s51\n\nFine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of Common Voice 7.0.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2t_en_vp-fr_s51\n\nFine-tuned facebook/wav2vec2-large-fr-voxpopuli for speech recognition on English using the train split of C... |
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