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zero-shot-classification | null |
# Test | {"tags": ["zero-shot-classification"], "widget": [{"candidate_labels": ["test1", "test"]}]} | osanseviero/test_zero | null | [
"zero-shot-classification",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#zero-shot-classification #region-us
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null | transformers | example | {} | osanseviero/torch | null | [
"transformers",
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"region:us"
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#transformers #pytorch #endpoints_compatible #region-us
| example | [] | [
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feature-extraction | transformers | Example card
Second modification | {} | osanseviero/upload-to-hub | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"feature-extraction",
"endpoints_compatible",
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#transformers #pytorch #jax #roberta #feature-extraction #endpoints_compatible #region-us
| Example card
Second modification | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #feature-extraction #endpoints_compatible #region-us \n"
] |
tabular-classification | sklearn |
## Wine Quality classification
### A Simple Example of Scikit-learn Pipeline
> Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 by Saptashwa Bhattacharyya
### How to use
```python
from huggingface_hub import hf_hub_url, cached_download
impo... | {"tags": ["tabular-classification", "sklearn"], "dataset": ["wine-quality"], "widget": [{"structuredData": {"fixed_acidity": [7.3, 7.8, 10.3], "volatile_acidity": [0.7, 0.88, 0.32], "citric_acid": [0, 0, 0.45], "residual_sugar": [1.9, 2.6, 6.4], "chlorides": [0.076, 0.098, 0.073], "free_sulfur_dioxide": [11, 25, 5], "t... | osanseviero/wine-quality | null | [
"sklearn",
"joblib",
"tabular-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sklearn #joblib #tabular-classification #has_space #region-us
| Wine Quality classification
---------------------------
### A Simple Example of Scikit-learn Pipeline
>
> Inspired by URL by Saptashwa Bhattacharyya
>
>
>
### How to use
#### Get sample data from this repo
#### Get your prediction
#### Eval
### Disclaimer
No red wine was drunk (unfortunately) whil... | [
"### A Simple Example of Scikit-learn Pipeline\n\n\n\n> \n> Inspired by URL by Saptashwa Bhattacharyya\n> \n> \n>",
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | osanseviero/xlm-roberta-base-finetuned-panx-de | null | [
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"pytorch",
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"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1344
* F1: 0.8647
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | oseibrefo/distilbert-base-uncased-finetuned-cola | null | [
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| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7595
* Matthews Correlation: 0.5498
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text2text-generation | transformers | # mt5-simplification-spanish
## Model description
This is a fine-tuned mt5-small model for generating simple text from complex text.
This model was created with the IXA Group research group of the University of the Basque Country, the model has been evaluated with the Sari, Bleu and Fklg metrics; it was trained and ... | {"language": ["es"], "license": "cc-by-nc-sa-4.0", "tags": ["simplification", "mt5", "spanish"], "metrics": ["sari"], "widget": [{"text": "La Simplificaci\u00f3n Textual es el proceso de transformaci\u00f3n de un texto a otro texto equivalente m\u00e1s comprensible para un determinado tipo de grupo o poblaci\u00f3n."},... | oskrmiguel/mt5-simplification-spanish | null | [
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"pytorch",
"mt5",
"text2text-generation",
"simplification",
"spanish",
"es",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
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| # mt5-simplification-spanish
## Model description
This is a fine-tuned mt5-small model for generating simple text from complex text.
This model was created with the IXA Group research group of the University of the Basque Country, the model has been evaluated with the Sari, Bleu and Fklg metrics; it was trained and ... | [
"# mt5-simplification-spanish",
"## Model description\n\nThis is a fine-tuned mt5-small model for generating simple text from complex text.\n\nThis model was created with the IXA Group research group of the University of the Basque Country, the model has been evaluated with the Sari, Bleu and Fklg metrics; it was... | [
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"# mt5-simplification-spanish",
"## Model description\n\nThis is a fine-tuned mt5-small model for gener... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "ar", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Arabic by Othmane Rifki", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spe... | othrif/wav2vec2-large-xlsr-arabic | null | [
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"license:apache-2.0",
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"endpoints_compatible",
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|
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as f... | [
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the Common Voice. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can b... | [
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"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Egyptian-Arabic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Egyptian using the [arabicspeech.org MGB-3](https://arabicspeech.org/mgb3-asr/)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "arz", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["https://arabicspeech.org/"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Egyptian Arabic by Othmane Rifki", "results": [{"task": {"type": "automatic-speech-rec... | othrif/wav2vec2-large-xlsr-egyptian | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
# Wav2Vec2-Large-XLSR-53-Egyptian-Arabic
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Egyptian using the URL MGB-3
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluate... | [
"# Wav2Vec2-Large-XLSR-53-Egyptian-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Egyptian using the URL MGB-3\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model... | [
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"# Wav2Vec2-Large-XLSR-53-Egyptian-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Egyptian using the URL MGB-3\nWhen using this model,... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Moroccan
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [MGB5 Moroccan Arabic](http://www.islrn.org/resources/938-639-614-524-5/) kindly provided by [ELDA](http://www.elra.info/en/about/elda/) and [ArabicSpeech](https://arabicspeech.org... | {"language": "ary", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["mgb5"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Moroccan Arabic dialect by Othmane Rifki", "results": [{"task": {"type": "automatic-speech-recognition", "n... | othrif/wav2vec2-large-xlsr-moroccan | null | [
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|
# Wav2Vec2-Large-XLSR-53-Moroccan
Fine-tuned facebook/wav2vec2-large-xlsr-53 on MGB5 Moroccan Arabic kindly provided by ELDA and ArabicSpeech.
In order to have access to MGB5, please request it from ELDA.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used d... | [
"# Wav2Vec2-Large-XLSR-53-Moroccan\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on MGB5 Moroccan Arabic kindly provided by ELDA and ArabicSpeech.\n\nIn order to have access to MGB5, please request it from ELDA.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe mod... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ary #dataset-mgb5 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Moroccan\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on MGB5 Moroccan Arabic kindly provided by ELDA ... |
automatic-speech-recognition | transformers | # Test Wav2Vec2 with egyptian arabic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Egyptian using the [arabicspeech.org MGB-3](https://arabicspeech.org/mgb3-asr/)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can... | {"language": "ar", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["https://arabicspeech.org/"], "model-index": [{"name": "XLSR Wav2Vec2 Egyptian by Zaid Alyafeai and Othmane Rifki", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognit... | othrif/wav2vec_test | null | [
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"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"ar",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #ar #license-apache-2.0 #endpoints_compatible #region-us
| # Test Wav2Vec2 with egyptian arabic
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Egyptian using the URL MGB-3
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
| [
"# Test Wav2Vec2 with egyptian arabic\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Egyptian using the URL MGB-3\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:"
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text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | otto-camp/DialoGPT-small-RickBot | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
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"# Rick DialoGPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model_index": [{"name": "finetuned-bert-mrpc", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metric": {... | oumeima/finetuned-bert-mrpc | null | [
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"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned-bert-mrpc
===================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5280
* Accuracy: 0.8529
* F1: 0.9003
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* t... |
null | null | tacotron2-100k.h5 | {} | outman/TensorSpeech | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| tacotron2-100k.h5 | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers | ## ParsTwiNER: Transformer-based Model for Named Entity Recognition at Informal Persian
An open, broad-coverage corpus and model for informal Persian named entity recognition collected from Twitter.
Paper presenting ParsTwiNER: [2021.wnut-1.16](https://aclanthology.org/2021.wnut-1.16/)
---
## Results
The following tab... | {} | overfit/twiner-bert-base-mtl | null | [
"transformers",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| ParsTwiNER: Transformer-based Model for Named Entity Recognition at Informal Persian
------------------------------------------------------------------------------------
An open, broad-coverage corpus and model for informal Persian named entity recognition collected from Twitter.
Paper presenting ParsTwiNER: URL-1.16... | [
"### Named Entity Recognition on Our Corpus\n\n\n\nHow to use\n----------",
"### TensorFlow 2.0\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsTwiNER in your research:\n\n\nAcknowledgments\n---------------\n\n\nThe authors would like to thank Dr. Momtazi for her supp... | [
"TAGS\n#transformers #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Named Entity Recognition on Our Corpus\n\n\n\nHow to use\n----------",
"### TensorFlow 2.0\n\n\nCite\n----\n\n\nPlease cite the following paper in your publication if you are using ParsTwiNER in... |
text-generation | null |
# ya | {"tags": ["conversational"]} | overgrowth/jokeboy | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# ya | [
"# ya"
] | [
"TAGS\n#conversational #region-us \n",
"# ya"
] |
text-classification | transformers | {0: 'Anorexia',
1: 'Anxiety',
2: 'Bullying',
3: 'Care',
4: 'Creativity',
5: 'Culture',
6: 'Depression',
7: 'Friends',
8: 'Getting help',
9: 'Happiness',
10: 'Helping others',
11: 'Helping yourself',
12: 'Hope',
13: 'Learning',
14: 'Life Issues',
15: 'Mental Health',
16: 'Mental Health Matters',
17: 'Me... | {} | owen99630/catexp2 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| {0: 'Anorexia',
1: 'Anxiety',
2: 'Bullying',
3: 'Care',
4: 'Creativity',
5: 'Culture',
6: 'Depression',
7: 'Friends',
8: 'Getting help',
9: 'Happiness',
10: 'Helping others',
11: 'Helping yourself',
12: 'Hope',
13: 'Learning',
14: 'Life Issues',
15: 'Mental Health',
16: 'Mental Health Matters',
17: 'Me... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Test | {"tags": ["conversational"]} | owencubes/DialoGPT-small-Josuke | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test | [
"# Test"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test"
] |
question-answering | transformers |
# T5-base model fine-tuned on BioASQ for Biological Question Answering 👩⚕️👨⚕️
[Google's T5-base](https://huggingface.co/t5-base) fine-tuned on [BioASQ](https://github.com/dmis-lab/biobert) (secondary task) for **Q&A** downstream task.
## Details of T5
[Google's T5](https://ai.googleblog.com/2020/02/exploring-tran... | {"language": "english", "license": "mit", "datasets": ["bioASQ"], "pipeline_tag": "question-answering"} | ozcangundes/T5-base-for-BioQA | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"question-answering",
"dataset:bioASQ",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"english"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-answering #dataset-bioASQ #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-base model fine-tuned on BioASQ for Biological Question Answering ️️
Google's T5-base fine-tuned on BioASQ (secondary task) for Q&A downstream task.
## Details of T5
Google's T5
Pretraining Dataset: C4
Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Authors: *Colin ... | [
"# T5-base model fine-tuned on BioASQ for Biological Question Answering ️️\nGoogle's T5-base fine-tuned on BioASQ (secondary task) for Q&A downstream task.",
"## Details of T5\nGoogle's T5 \n\nPretraining Dataset: C4\n\nPaper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer\n\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #question-answering #dataset-bioASQ #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-base model fine-tuned on BioASQ for Biological Question Answering ️️\nGoogle's T5-base fine... |
question-answering | transformers |
# mT5-small based Turkish Multitask (Answer Extraction, Question Generation and Question Answering) System
[Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Turkish Question Answering dataset](https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answeri... | {"language": "tr", "license": "apache-2.0", "tags": ["question-answering", "question-generation", "multitask-model"], "datasets": ["TQUAD"]} | ozcangundes/mt5-multitask-qa-qg-turkish | null | [
"transformers",
"pytorch",
"jax",
"mt5",
"text2text-generation",
"question-answering",
"question-generation",
"multitask-model",
"tr",
"dataset:TQUAD",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #mt5 #text2text-generation #question-answering #question-generation #multitask-model #tr #dataset-TQUAD #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5-small based Turkish Multitask (Answer Extraction, Question Generation and Question Answering) System
Google's Multilingual T5-small is fine-tuned on Turkish Question Answering dataset for three downstream task Answer extraction, Question Generation and Question Answering served in this single model. mT5 model w... | [
"# mT5-small based Turkish Multitask (Answer Extraction, Question Generation and Question Answering) System\n\nGoogle's Multilingual T5-small is fine-tuned on Turkish Question Answering dataset for three downstream task Answer extraction, Question Generation and Question Answering served in this single model. mT5 m... | [
"TAGS\n#transformers #pytorch #jax #mt5 #text2text-generation #question-answering #question-generation #multitask-model #tr #dataset-TQUAD #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5-small based Turkish Multitask (Answer Extraction, Question Ge... |
question-answering | transformers |
# mT5-small based Turkish Question Answering System
[Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Turkish Question Answering dataset](https://github.com/TQuad/turkish-nlp-qa-dataset) for **Q&A** downstream task by using Pytorch Lightning.⚡
The notebook that ... | {"language": "tr", "license": "mit", "datasets": ["TQUAD"], "pipeline_tag": "question-answering"} | ozcangundes/mt5-small-turkish-squad | null | [
"transformers",
"pytorch",
"jax",
"mt5",
"text2text-generation",
"question-answering",
"tr",
"dataset:TQUAD",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #mt5 #text2text-generation #question-answering #tr #dataset-TQUAD #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5-small based Turkish Question Answering System
Google's Multilingual T5-small is fine-tuned on Turkish Question Answering dataset for Q&A downstream task by using Pytorch Lightning.
The notebook that includes all fine tuning process will be shared on my Github page later. mT5 small model has 300 million param... | [
"# mT5-small based Turkish Question Answering System\n\nGoogle's Multilingual T5-small is fine-tuned on Turkish Question Answering dataset for Q&A downstream task by using Pytorch Lightning. \n\nThe notebook that includes all fine tuning process will be shared on my Github page later. mT5 small model has 300 milli... | [
"TAGS\n#transformers #pytorch #jax #mt5 #text2text-generation #question-answering #tr #dataset-TQUAD #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5-small based Turkish Question Answering System\n\nGoogle's Multilingual T5-small is fine-tuned on Turkish Q... |
summarization | transformers |
# mT5-small based Turkish Summarization System
[Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [MLSUM Turkish news dataset](https://github.com/recitalAI/MLSUM) for **Summarization** downstream task by using Pytorch Lightning.⚡
mT5 small model has 300 million pa... | {"language": "tr", "license": "mit", "datasets": ["MLSUM"], "pipeline_tag": "summarization"} | ozcangundes/mt5-small-turkish-summarization | null | [
"transformers",
"pytorch",
"jax",
"mt5",
"text2text-generation",
"summarization",
"tr",
"dataset:MLSUM",
"arxiv:2004.14900",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.14900"
] | [
"tr"
] | TAGS
#transformers #pytorch #jax #mt5 #text2text-generation #summarization #tr #dataset-MLSUM #arxiv-2004.14900 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5-small based Turkish Summarization System
Google's Multilingual T5-small is fine-tuned on MLSUM Turkish news dataset for Summarization downstream task by using Pytorch Lightning.
mT5 small model has 300 million parameters and model size is about 1.2GB. Therefore, it takes significant amount of time to fine tu... | [
"# mT5-small based Turkish Summarization System\n\nGoogle's Multilingual T5-small is fine-tuned on MLSUM Turkish news dataset for Summarization downstream task by using Pytorch Lightning. \n\nmT5 small model has 300 million parameters and model size is about 1.2GB. Therefore, it takes significant amount of time to... | [
"TAGS\n#transformers #pytorch #jax #mt5 #text2text-generation #summarization #tr #dataset-MLSUM #arxiv-2004.14900 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5-small based Turkish Summarization System\n\nGoogle's Multilingual T5-small is fine-tuned on M... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": ["tr"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Ozcan Gundes XLSR Wav2Vec2 Large Turkish", "results": [{"task": {"type": "automatic-speech-recognition", "name":... | ozcangundes/wav2vec2-large-xlsr-53-turkish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as f... | [
"# Wav2Vec2-Large-XLSR-53-Turkish\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Turkish\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voi... |
text2text-generation | generic |
random test repo | {"library_name": "generic", "tags": ["text2text-generation"]} | p-christ/12412fsasf | null | [
"generic",
"pytorch",
"t5",
"text2text-generation",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #pytorch #t5 #text2text-generation #region-us
|
random test repo | [] | [
"TAGS\n#generic #pytorch #t5 #text2text-generation #region-us \n"
] |
text2text-generation | transformers | # Transformer QG on DRCD
請參閱 https://github.com/p208p2002/Transformer-QG-on-DRCD 獲得更多細節
The inputs of the model refers to
```
we integrate C and A into a new C' in the following form.
C' = [c1, c2, ..., [HL], a1, ..., a|A|, [HL], ..., c|C|]
```
> Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-... | {"tags": ["question-generation"], "datasets": ["drcd"], "widget": [{"text": "[HL]\u4f0a\u9686\u00b7\u91cc\u592b\u00b7\u99ac\u65af\u514b[HL]\u662f\u4e00\u540d\u4f01\u696d\u5bb6\u548c\u5546\u696d\u5927\u4ea8"}]} | p208p2002/bart-drcd-qg-hl | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"question-generation",
"dataset:drcd",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #question-generation #dataset-drcd #autotrain_compatible #endpoints_compatible #region-us
| Transformer QG on DRCD
======================
請參閱 URL 獲得更多細節
The inputs of the model refers to
>
> Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.
>
>
>
Features
--------
* Fully pipline from fine-tune to evaluation
* Support most of state of the... | [
"### Seq2Seq LM\n\n\nDeploy\n------",
"### Start up",
"### Request example"
] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #question-generation #dataset-drcd #autotrain_compatible #endpoints_compatible #region-us \n",
"### Seq2Seq LM\n\n\nDeploy\n------",
"### Start up",
"### Request example"
] |
text2text-generation | transformers |
# Transformer QG on SQuAD
HLQG is Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.](https://www.aclweb.org/anthology/D19-5821/)
**This is a Reproduce Version**
More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQ... | {"tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "Harry Potter is a series of seven fantasy novels written by British author, [HL]J. K. Rowling[HL]."}]} | p208p2002/bart-squad-nqg-hl | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1606.05250",
"arxiv:1705.00106",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1606.05250",
"1705.00106"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #region-us
| Transformer QG on SQuAD
=======================
HLQG is Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.
This is a Reproduce Version
More detail: p208p2002/Transformer-QG-on-SQuAD
Usage
-----
### Input Format
### Input Example
>
> Who wrote Harry... | [
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =======================\n> \n> \n> \n\n\nData setting\n------------\n\n\nWe report two dataset setting as Follow",
"### SQuAD\n\n\n* train: 87599\\\\t\n* validation: 10570\n\n\n\n> \n> SQuAD: 100,000+ Questions for Machine Comprehens... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =======================\n> \n> \n> \n\n\nDa... |
text2text-generation | transformers |
# Transformer QG on SQuAD
HLQG is Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.](https://www.aclweb.org/anthology/D19-5821/)
**This is a Reproduce Version**
More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQ... | {"tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "Harry Potter is a series of seven fantasy novels written by British author, [HL]J. K. Rowling[HL]."}]} | p208p2002/bart-squad-qg-hl | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1606.05250",
"arxiv:1705.00106",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1606.05250",
"1705.00106"
] | [] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #region-us
| Transformer QG on SQuAD
=======================
HLQG is Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.
This is a Reproduce Version
More detail: p208p2002/Transformer-QG-on-SQuAD
Usage
-----
### Input Format
### Input Example
>
> Who wrote Harry... | [
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =======================\n> \n> \n> \n\n\nData setting\n------------\n\n\nWe report two dataset setting as Follow",
"### SQuAD\n\n\n* train: 87599\\\\t\n* validation: 10570\n\n\n\n> \n> SQuAD: 100,000+ Questions for Machine Comprehens... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =======================\n> \n>... |
text-generation | transformers | ## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModelForCausalLM,
)
tokenizer = BertTokenizerFast.from_pretrained('p208p2002/gpt2-drcd-qg-hl')
model = AutoModelForCausalLM.from_pretrained('p2... | {} | p208p2002/gpt2-drcd-qg-hl | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
### Input Format
### Input Example
> 誰撰寫哈利·波特? | [
"## Usage\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。",
"### Input Format",
"### Input Example\n\n> 誰撰寫哈利·波特?"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Usage\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。",
"### Input Format",
"### Inp... |
text-generation | transformers |
# Transformer QG on SQuAD
HLQG is Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.](https://www.aclweb.org/anthology/D19-5821/)
**This is a Reproduce Version**
More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQ... | {"tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "Harry Potter is a series of seven fantasy novels written by British author, [HL]J. K. Rowling[HL]."}]} | p208p2002/gpt2-squad-nqg-hl | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"question-generation",
"dataset:squad",
"arxiv:1606.05250",
"arxiv:1705.00106",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1606.05250",
"1705.00106"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Transformer QG on SQuAD
=======================
HLQG is Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.
This is a Reproduce Version
More detail: p208p2002/Transformer-QG-on-SQuAD
Usage
-----
### Input Format
### Input Example
>
> Who wrote Harry... | [
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =======================\n> \n> \n> \n\n\nData setting\n------------\n\n\nWe report two dataset setting as Follow",
"### SQuAD\n\n\n* train: 87599\\\\t\n* validation: 10570\n\n\n\n> \n> SQuAD: 100,000+ Questions for Machine Comprehens... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> ===... |
text-generation | transformers |
# Transformer QG on SQuAD
HLQG is Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.](https://www.aclweb.org/anthology/D19-5821/)
**This is a Reproduce Version**
More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQ... | {"tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "Harry Potter is a series of seven fantasy novels written by British author, [HL]J. K. Rowling[HL]."}]} | p208p2002/gpt2-squad-qg-hl | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"question-generation",
"dataset:squad",
"arxiv:1606.05250",
"arxiv:1705.00106",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1606.05250",
"1705.00106"
] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Transformer QG on SQuAD
=======================
HLQG is Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.
This is a Reproduce Version
More detail: p208p2002/Transformer-QG-on-SQuAD
Usage
-----
### Input Format
### Input Example
>
> Who wrote Harry... | [
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =======================\n> \n> \n> \n\n\nData setting\n------------\n\n\nWe report two dataset setting as Follow",
"### SQuAD\n\n\n* train: 87599\\\\t\n* validation: 10570\n\n\n\n> \n> SQuAD: 100,000+ Questions for Machine Comprehens... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =====... |
text2text-generation | transformers | # EQGG: Educational Question Group Generation
<span>
<a target="_blank" href="https://github.com/p208p2002/Neural-Question-Group-Generation">
<img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white">
</a>
<a target="_blank" href="https://huggingface.co/p208p2002/qmst-qgg">
... | {} | p208p2002/qmst-qgg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| # EQGG: Educational Question Group Generation
<span>
<a target="_blank" href="URL
<img src="URL
</a>
<a target="_blank" href="URL
<img src="URL HF Model Hub-ffea00?style=for-the-badge&logoColor=white">
</a>
<a target="_blank" href="URL
<img src="URL Live Demo-78ab78?style=for-the-badge&logoColor=white">
</a>
</span>
| [
"# EQGG: Educational Question Group Generation\n<span>\n<a target=\"_blank\" href=\"URL\n<img src=\"URL\n</a>\n\n<a target=\"_blank\" href=\"URL\n<img src=\"URL HF Model Hub-ffea00?style=for-the-badge&logoColor=white\">\n</a>\n\n<a target=\"_blank\" href=\"URL\n<img src=\"URL Live Demo-78ab78?style=for-the-badge&lo... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# EQGG: Educational Question Group Generation\n<span>\n<a target=\"_blank\" href=\"URL\n<img src=\"URL\n</a>\n\n<a target=\"_blank\" href=\"URL\n<img src=\"URL HF Model Hub-ffea00?styl... |
text2text-generation | transformers |
# Transformer QG on SQuAD
HLQG is Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.](https://www.aclweb.org/anthology/D19-5821/)
**This is a Reproduce Version**
More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQ... | {"tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "Harry Potter is a series of seven fantasy novels written by British author, [HL]J. K. Rowling[HL]."}]} | p208p2002/t5-squad-nqg-hl | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1606.05250",
"arxiv:1705.00106",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1606.05250",
"1705.00106"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Transformer QG on SQuAD
=======================
HLQG is Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.
This is a Reproduce Version
More detail: p208p2002/Transformer-QG-on-SQuAD
Usage
-----
### Input Format
### Input Example
>
> Who wrote Harry... | [
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =======================\n> \n> \n> \n\n\nData setting\n------------\n\n\nWe report two dataset setting as Follow",
"### SQuAD\n\n\n* train: 87599\\t\n* validation: 10570\n\n\n\n> \n> SQuAD: 100,000+ Questions for Machine Comprehensio... | [
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> ... |
text2text-generation | transformers |
# Transformer QG on SQuAD
HLQG is Proposed by [Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.](https://www.aclweb.org/anthology/D19-5821/)
**This is a Reproduce Version**
More detail: [p208p2002/Transformer-QG-on-SQuAD](https://github.com/p208p2002/Transformer-QG-on-SQ... | {"tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "Harry Potter is a series of seven fantasy novels written by British author, [HL]J. K. Rowling[HL]."}]} | p208p2002/t5-squad-qg-hl | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1606.05250",
"arxiv:1705.00106",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1606.05250",
"1705.00106"
] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Transformer QG on SQuAD
=======================
HLQG is Proposed by Ying-Hong Chan & Yao-Chung Fan. (2019). A Re-current BERT-based Model for Question Generation.
This is a Reproduce Version
More detail: p208p2002/Transformer-QG-on-SQuAD
Usage
-----
### Input Format
### Input Example
>
> Who wrote Harry... | [
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =======================\n> \n> \n> \n\n\nData setting\n------------\n\n\nWe report two dataset setting as Follow",
"### SQuAD\n\n\n* train: 87599\\\\t\n* validation: 10570\n\n\n\n> \n> SQuAD: 100,000+ Questions for Machine Comprehens... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1606.05250 #arxiv-1705.00106 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Input Format",
"### Input Example\n\n\n\n> \n> Who wrote Harry Potter?\n> =====... |
null | null | Starter to detect landfill. | {} | pablito-wabi/landfill-v1 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Starter to detect landfill. | [] | [
"TAGS\n#region-us \n"
] |
null | keras |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, pu... | {"license": "apache-2.0", "library_name": "keras", "title": "Video Vision Transformer on medmnist", "emoji": "\ud83e\uddd1\u200d\u2695\ufe0f", "colorFrom": "red", "colorTo": "green", "sdk": "gradio", "app_file": "app.py", "pinned": false} | pablorodriper/vivit | null | [
"keras",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #license-apache-2.0 #region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, pu... | [
"# Configuration\n\n'title': _string_\nDisplay title for the Space\n\n'emoji': _string_\nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_\nColor for Thumbnail gradient (red, yellow, green, ... | [
"TAGS\n#keras #license-apache-2.0 #region-us \n",
"# Configuration\n\n'title': _string_\nDisplay title for the Space\n\n'emoji': _string_\nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_... |
null | transformers | # BERT-STEM
BERT model fine-tuned on Science Technology Engineering and Mathematics (STEM) lessons.
## Install:
To install from pip:
```
pip install bertstem
```
## Quickstart
To encode sentences and get embedding matrix for embedding layers:
```python
from BERT_STEM.BertSTEM import *
bert = BertSTEM()
# Exampl... | {} | pablouribe/bertstem | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #pretraining #endpoints_compatible #region-us
| # BERT-STEM
BERT model fine-tuned on Science Technology Engineering and Mathematics (STEM) lessons.
## Install:
To install from pip:
## Quickstart
To encode sentences and get embedding matrix for embedding layers:
To use it from HuggingFace:
| [
"# BERT-STEM\n\nBERT model fine-tuned on Science Technology Engineering and Mathematics (STEM) lessons.",
"## Install:\n\nTo install from pip:",
"## Quickstart\n\nTo encode sentences and get embedding matrix for embedding layers:\n\n\n\nTo use it from HuggingFace:"
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #endpoints_compatible #region-us \n",
"# BERT-STEM\n\nBERT model fine-tuned on Science Technology Engineering and Mathematics (STEM) lessons.",
"## Install:\n\nTo install from pip:",
"## Quickstart\n\nTo encode sentences and get embedding matrix for embedding l... |
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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - A... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | pablouribe/xls-r-ab-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 133.2596
- Wer: 19.1571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation ... | [
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 133.2596\n- Wer: 19.1571",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)... | {"language": ["es"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "xls-r-spanish-test", "results": [{"task": {... | pablouribe/xls-r-spanish-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #es #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - ES dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1461
* Wer: 1.0063
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #es #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
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",... | paintingpeter/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+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: 0.2795
* Accuracy: 0.9468
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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #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* learning\\_rate:... |
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",... | paintingpeter/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 | 2022-03-02T23:29:05+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: 0.7713
* Accuracy: 0.9174
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 |
# GPT | {"tags": ["conversational"]} | paladinx00/rh-bender | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT | [
"# GPT"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 590516680
- CO2 Emissions (in grams): 2.3709499644854883
## Validation Metrics
- Loss: 0.6466107964515686
- Accuracy: 0.6608695652173913
- Precision: 0.6515151515151515
- Recall: 0.7288135593220338
- AUC: 0.6334745762711864
- F1: 0.688
... | {"language": "en", "tags": "autonlp", "datasets": ["panashe/autonlp-data-eo"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.3709499644854883} | panashe/autonlp-eo-590516680 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:panashe/autonlp-data-eo",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-panashe/autonlp-data-eo #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 590516680
- CO2 Emissions (in grams): 2.3709499644854883
## Validation Metrics
- Loss: 0.6466107964515686
- Accuracy: 0.6608695652173913
- Precision: 0.6515151515151515
- Recall: 0.7288135593220338
- AUC: 0.6334745762711864
- F1: 0.688
... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 590516680\n- CO2 Emissions (in grams): 2.3709499644854883",
"## Validation Metrics\n\n- Loss: 0.6466107964515686\n- Accuracy: 0.6608695652173913\n- Precision: 0.6515151515151515\n- Recall: 0.7288135593220338\n- AUC: 0.63347457627... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-panashe/autonlp-data-eo #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 590516680\n- CO2 Emissions (in grams): 2.3709499... |
summarization | transformers |
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
`t5-base-indonesian-summarization-cased` model is based on `t5-base-bahasa-summarization-cased` by [huseinzol05](https://huggingface.co/huseinzol05), finetuned using [indosum](https://github.com/kata... | {"language": "id", "tags": ["pipeline:summarization", "summarization", "t5"], "datasets": ["indosum"]} | panggi/t5-base-indonesian-summarization-cased | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"pipeline:summarization",
"summarization",
"id",
"dataset:indosum",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-indosum #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
't5-base-indonesian-summarization-cased' model is based on 't5-base-bahasa-summarization-cased' by huseinzol05, finetuned using indosum dataset.
## Load Finetuned Model
## Code Sample
Output:
... | [
"# Indonesian T5 Summarization Base Model\n\nFinetuned T5 base summarization model for Indonesian.",
"## Finetuning Corpus\n\n't5-base-indonesian-summarization-cased' model is based on 't5-base-bahasa-summarization-cased' by huseinzol05, finetuned using indosum dataset.",
"## Load Finetuned Model",
"## Code S... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-indosum #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Indonesian T5 Summarization Base Model\n\nFinetuned T5 base summarization model for Indonesi... |
summarization | transformers |
# Indonesian T5 Summarization Small Model
Finetuned T5 small summarization model for Indonesian.
## Finetuning Corpus
`t5-small-indonesian-summarization-cased` model is based on `t5-small-bahasa-summarization-cased` by [huseinzol05](https://huggingface.co/huseinzol05), finetuned using [indosum](https://github.com/... | {"language": "id", "tags": ["pipeline:summarization", "summarization", "t5"], "datasets": ["indosum"]} | panggi/t5-small-indonesian-summarization-cased | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"pipeline:summarization",
"summarization",
"id",
"dataset:indosum",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-indosum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Indonesian T5 Summarization Small Model
Finetuned T5 small summarization model for Indonesian.
## Finetuning Corpus
't5-small-indonesian-summarization-cased' model is based on 't5-small-bahasa-summarization-cased' by huseinzol05, finetuned using indosum dataset.
## Load Finetuned Model
## Code Sample
Outp... | [
"# Indonesian T5 Summarization Small Model\n\nFinetuned T5 small summarization model for Indonesian.",
"## Finetuning Corpus\n\n't5-small-indonesian-summarization-cased' model is based on 't5-small-bahasa-summarization-cased' by huseinzol05, finetuned using indosum dataset.",
"## Load Finetuned Model",
"## Co... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-indosum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Indonesian T5 Summarization Small Model\n\nFinetuned T5 small summarization model for Indonesian.",
"## Fi... |
text-classification | transformers |
# xlm-roberta-base-language-detection
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) dataset.
## Model description
This model is an XLM-RoBERTa ... | {"language": ["multilingual", "ar", "bg", "de", "el", "en", "es", "fr", "hi", "it", "ja", "nl", "pl", "pt", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": "papluca/language-identification", "metrics": ["accuracy", "f1"], "base_model": "xlm-roberta-base", "mo... | papluca/xlm-roberta-base-language-detection | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"multilingual",
"ar",
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"ja",
"nl",
"pl",
"pt",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh",
"dataset:pap... | null | 2022-03-02T23:29:05+00:00 | [
"1911.02116"
] | [
"multilingual",
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"hi",
"it",
"ja",
"nl",
"pl",
"pt",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh"
] | TAGS
#transformers #pytorch #tf #safetensors #xlm-roberta #text-classification #generated_from_trainer #multilingual #ar #bg #de #el #en #es #fr #hi #it #ja #nl #pl #pt #ru #sw #th #tr #ur #vi #zh #dataset-papluca/language-identification #arxiv-1911.02116 #base_model-xlm-roberta-base #license-mit #autotrain_compatible ... | xlm-roberta-base-language-detection
===================================
This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.
Model description
-----------------
This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of... | [
"### Benchmarks\n\n\nAs a baseline to compare 'xlm-roberta-base-language-detection' against, we have used the Python langid library. Since it comes pre-trained on 97 languages, we have used its '.set\\_languages()' method to constrain the language set to our 20 languages. The average accuracy of langid on the test ... | [
"TAGS\n#transformers #pytorch #tf #safetensors #xlm-roberta #text-classification #generated_from_trainer #multilingual #ar #bg #de #el #en #es #fr #hi #it #ja #nl #pl #pt #ru #sw #th #tr #ur #vi #zh #dataset-papluca/language-identification #arxiv-1911.02116 #base_model-xlm-roberta-base #license-mit #autotrain_compa... |
text-generation | transformers |
# Jake Peralta DialoGPT Model | {"tags": ["conversational"]} | parigaswetha/DialoGPT-small-jakeperalta | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jake Peralta DialoGPT Model | [
"# Jake Peralta DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jake Peralta DialoGPT Model"
] |
text-generation | transformers |
#Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | parthsinha/DialoGPT-small-rickandmorty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick and Morty DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# iron man 2 | {"tags": ["conversational"]} | pashin/DialoGPT-small-ironman-2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# iron man 2 | [
"# iron man 2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# iron man 2"
] |
text-generation | transformers |
# iron man 3 | {"tags": ["conversational"]} | pashin/DialoGPT-small-ironman-3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# iron man 3 | [
"# iron man 3"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# iron man 3"
] |
text-generation | transformers |
#iron man 1 DialoGPT Model | {"tags": ["conversational"]} | pashin/DialoGPT-small-ironman1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#iron man 1 DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Harry Potter DialoGPT MOdel | {"tags": ["conversational"]} | pastlecry/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT MOdel | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | # Librispeech dev-clean dataset
This is the Libirspeech "dev-clean" dataset that can also be obtained by running:
```
curl https://www.openslr.org/resources/12/dev-clean.tar.gz --output dev-clean.tar.gz
tar xf dev-clean.tar.gz
``` | {} | patrickvonplaten/LibriSpeechTest | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Librispeech dev-clean dataset
This is the Libirspeech "dev-clean" dataset that can also be obtained by running:
| [
"# Librispeech dev-clean dataset\n\nThis is the Libirspeech \"dev-clean\" dataset that can also be obtained by running:"
] | [
"TAGS\n#region-us \n",
"# Librispeech dev-clean dataset\n\nThis is the Libirspeech \"dev-clean\" dataset that can also be obtained by running:"
] |
text-classification | transformers | # Bert-base-cased Fine Tuned Glue Mrpc Demo
This checkpoint was initialized from the pre-trained checkpoint bert-base-cased and subsequently fine-tuned on GLUE task: mrpc using [this](https://colab.research.google.com/drive/162pW3wonGcMMrGxmA-jdxwy1rhqXd90x?usp=sharing) notebook.
Training was conducted for 3 epochs, u... | {"language": "en", "license": "apache-2.0", "datasets": ["glue"]} | patrickvonplaten/bert-base-cased_fine_tuned_glue_mrpc_demo | null | [
"transformers",
"jax",
"bert",
"text-classification",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #jax #bert #text-classification #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Bert-base-cased Fine Tuned Glue Mrpc Demo
This checkpoint was initialized from the pre-trained checkpoint bert-base-cased and subsequently fine-tuned on GLUE task: mrpc using this notebook.
Training was conducted for 3 epochs, using a linear decaying learning rate of 2e-05, and a total batch size of 32.
The model h... | [
"# Bert-base-cased Fine Tuned Glue Mrpc Demo\n\nThis checkpoint was initialized from the pre-trained checkpoint bert-base-cased and subsequently fine-tuned on GLUE task: mrpc using this notebook.\nTraining was conducted for 3 epochs, using a linear decaying learning rate of 2e-05, and a total batch size of 32.\n\nT... | [
"TAGS\n#transformers #jax #bert #text-classification #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bert-base-cased Fine Tuned Glue Mrpc Demo\n\nThis checkpoint was initialized from the pre-trained checkpoint bert-base-cased and subsequently fine-tuned on GLUE... |
text2text-generation | transformers | # Bert2Bert Summarization with 🤗 EncoderDecoder Framework
This model is a Bert2Bert model fine-tuned on summarization.
Bert2Bert is a `EncoderDecoderModel`, meaning that both the encoder and the decoder are `bert-base-uncased`
BERT models. Leveraging the [EncoderDecoderFramework](https://huggingface.co/transformers... | {} | patrickvonplaten/bert2bert-cnn_dailymail-fp16 | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Bert2Bert Summarization with EncoderDecoder Framework
=====================================================
This model is a Bert2Bert model fine-tuned on summarization.
Bert2Bert is a 'EncoderDecoderModel', meaning that both the encoder and the decoder are 'bert-base-uncased'
BERT models. Leveraging the EncoderDeco... | [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
summarization | transformers |
Bert2Bert Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *BERT2BERT* model fine-tuned on the *CNN/Dailymail* summarization dataset.
The model achieves a **18.22** ROUGE-2 score on *CNN/Dailymail*'s test dataset.
For more details on how the model was fine-tuned, please refer to
[this](htt... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "patrickvonplaten/bert2bert_cnn_daily_mail", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailymail", "config": "3.... | patrickvonplaten/bert2bert_cnn_daily_mail | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Bert2Bert Summarization with EncoderDecoder Framework
This model is a warm-started *BERT2BERT* model fine-tuned on the *CNN/Dailymail* summarization dataset.
The model achieves a 18.22 ROUGE-2 score on *CNN/Dailymail*'s test dataset.
For more details on how the model was fine-tuned, please refer to
this notebook.
| [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | # Bert2GPT2 Summarization with 🤗 EncoderDecoder Framework
This model is a Bert2Bert model fine-tuned on summarization.
Bert2GPT2 is a `EncoderDecoderModel`, meaning that the encoder is a `bert-base-uncased`
BERT model and the decoder is a `gpt2` GPT2 model. Leveraging the [EncoderDecoderFramework](https://huggingfa... | {} | patrickvonplaten/bert2gpt2-cnn_dailymail-fp16 | null | [
"transformers",
"pytorch",
"jax",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #encoder_decoder #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Bert2GPT2 Summarization with EncoderDecoder Framework
=====================================================
This model is a Bert2Bert model fine-tuned on summarization.
Bert2GPT2 is a 'EncoderDecoderModel', meaning that the encoder is a 'bert-base-uncased'
BERT model and the decoder is a 'gpt2' GPT2 model. Leveragi... | [] | [
"TAGS\n#transformers #pytorch #jax #encoder_decoder #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers |
# Data2Vec-Audio-Base
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: T... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | patrickvonplaten/data2vec-base | null | [
"transformers",
"pytorch",
"data2vec-audio",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Base
Facebook's Data2Vec
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokeniz... | [
"# Data2Vec-Audio-Base\n\nFacebook's Data2Vec\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition,... | [
"TAGS\n#transformers #pytorch #data2vec-audio #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Base\n\nFacebook's Data2Vec\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sur... |
null | null | This repository is used to debug models, functionalities in transformers, etc...
# 1. Generation
...
# 2. Flax Wav2Vec2 Pretraining
Go into the `flax_wav2vec2` folder.
1. **Check PT loss works correctly**
`./run_pt_fsq_comp.sh` shows that HF PyTorch and Fairseq PT yield equivalent loss. Make sure to use the corre... | {} | patrickvonplaten/debug_repo | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This repository is used to debug models, functionalities in transformers, etc...
# 1. Generation
...
# 2. Flax Wav2Vec2 Pretraining
Go into the 'flax_wav2vec2' folder.
1. Check PT loss works correctly
'./run_pt_fsq_comp.sh' shows that HF PyTorch and Fairseq PT yield equivalent loss. Make sure to use the correct l... | [
"# 1. Generation\n\n...",
"# 2. Flax Wav2Vec2 Pretraining\n\nGo into the 'flax_wav2vec2' folder.\n\n1. Check PT loss works correctly\n\n'./run_pt_fsq_comp.sh' shows that HF PyTorch and Fairseq PT yield equivalent loss. Make sure to use the correct library versions as defined 'branches_to_use.txt'.\n\n2. Check Fla... | [
"TAGS\n#region-us \n",
"# 1. Generation\n\n...",
"# 2. Flax Wav2Vec2 Pretraining\n\nGo into the 'flax_wav2vec2' folder.\n\n1. Check PT loss works correctly\n\n'./run_pt_fsq_comp.sh' shows that HF PyTorch and Fairseq PT yield equivalent loss. Make sure to use the correct library versions as defined 'branches_to_... |
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. -->
# distilhubert-timit
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) o... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "distilhubert-timit", "results": []}]} | patrickvonplaten/distilhubert-timit | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
| distilhubert-timit
==================
This model is a fine-tuned version of ntu-spml/distilhubert on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3601
* Wer: 0.6776
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000... |
text2text-generation | transformers | Dummy T5 Test | {} | patrickvonplaten/dummy-t5-test | null | [
"transformers",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Dummy T5 Test | [] | [
"TAGS\n#transformers #jax #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # roberta-base-als-demo
**roberta-base-als-demo** is a model trained by Patrick von Platen to demonstrate how to train a roberta-base model from scratch on the Alemannic language.
This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp... | {} | patrickvonplaten/gpt2-als-demo | null | [
"transformers",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # roberta-base-als-demo
roberta-base-als-demo is a model trained by Patrick von Platen to demonstrate how to train a roberta-base model from scratch on the Alemannic language.
This is part of the
Flax/Jax Community Week, organised by HuggingFace and TPU usage sponsored by Google.
## Useful links
- Community Week ti... | [
"# roberta-base-als-demo\n\nroberta-base-als-demo is a model trained by Patrick von Platen to demonstrate how to train a roberta-base model from scratch on the Alemannic language.\n\nThis is part of the\nFlax/Jax Community Week, organised by HuggingFace and TPU usage sponsored by Google.",
"## Useful links\n- Com... | [
"TAGS\n#transformers #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# roberta-base-als-demo\n\nroberta-base-als-demo is a model trained by Patrick von Platen to demonstrate how to train a roberta-base model from scratch on the Alemannic ... |
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. -->
# hello_2b
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) o... | {"language": ["tr"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "hello_2b", "results": []}]} | patrickvonplaten/hello_2b | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us
| hello\_2b
=========
This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2725
* Wer: 0.9531
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n*... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_s... |
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. -->
# hello_2b_2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b)... | {"language": ["tr"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "hello_2b_2", "results": []}]} | patrickvonplaten/hello_2b_2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us
| hello\_2b\_2
============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5324
* Wer: 0.5109
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n*... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_s... |
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. -->
# hello_2b_3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b)... | {"language": ["tr"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "hello_2b_3", "results": []}]} | patrickvonplaten/hello_2b_3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us
| hello\_2b\_3
============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5615
* Wer: 0.9808
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n*... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_s... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert-librispeech-clean-100h-demo-dist
This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.... | {"license": "apache-2.0", "tags": ["speech-recognition", "librispeech_asr", "generated_from_trainer"], "model-index": [{"name": "hubert-librispeech-clean-100h-demo-dist", "results": []}]} | patrickvonplaten/hubert-librispeech-clean-100h-demo-dist | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| hubert-librispeech-clean-100h-demo-dist
=======================================
This model is a fine-tuned version of facebook/hubert-large-ll60k on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0984
* Wer: 0.0883
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 64\n* o... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #speech-recognition #librispeech_asr #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... |
text2text-generation | transformers |
## Introduction
[Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer).
This is an unofficial *led-large-16384* checkpoint that is fine-tuned on the [pubmed dataset](https://huggingface.co/datasets/scientific_papers).
The model was fine-tuned and evaluated as detailed in [thi... | {"language": "en", "license": "apache-2.0", "datasets": ["scientific_papers"]} | patrickvonplaten/led-large-16384-pubmed | null | [
"transformers",
"pytorch",
"tf",
"led",
"text2text-generation",
"en",
"dataset:scientific_papers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #led #text2text-generation #en #dataset-scientific_papers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Introduction
Allenai's Longformer Encoder-Decoder (LED).
This is an unofficial *led-large-16384* checkpoint that is fine-tuned on the pubmed dataset.
The model was fine-tuned and evaluated as detailed in this notebook
## Results
The model achieves a Rouge-2 score of 19.33 on Pubmed which is competitive to stat... | [
"## Introduction\n\nAllenai's Longformer Encoder-Decoder (LED).\n\nThis is an unofficial *led-large-16384* checkpoint that is fine-tuned on the pubmed dataset.\n\nThe model was fine-tuned and evaluated as detailed in this notebook",
"## Results\n\nThe model achieves a Rouge-2 score of 19.33 on Pubmed which is com... | [
"TAGS\n#transformers #pytorch #tf #led #text2text-generation #en #dataset-scientific_papers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Introduction\n\nAllenai's Longformer Encoder-Decoder (LED).\n\nThis is an unofficial *led-large-16384* checkpoint that is fine-... |
text2text-generation | transformers | # Longformer2Roberta Summarization with 🤗 EncoderDecoder Framework
This model is a Longformer2Roberta model fine-tuned on summarization.
Longformer2Roberta is a `EncoderDecoderModel`, meaning that both the encoder is a `allenai/longformer-base-4096` model and the decoder is a `roberta-base` model. Leveraging the [En... | {} | patrickvonplaten/longformer2roberta-cnn_dailymail-fp16 | null | [
"transformers",
"pytorch",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder_decoder #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Longformer2Roberta Summarization with EncoderDecoder Framework
==============================================================
This model is a Longformer2Roberta model fine-tuned on summarization.
Longformer2Roberta is a 'EncoderDecoderModel', meaning that both the encoder is a 'allenai/longformer-base-4096' model a... | [] | [
"TAGS\n#transformers #pytorch #encoder_decoder #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | ## Roberta-Base
This repo trains [roberta-base](https://huggingface.co/roberta-base) from scratch on the [Norwegian training subset of Oscar](https://oscar-corpus.com/) containing roughly 4.7 GB of data according to [this](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling) example... | {} | patrickvonplaten/norwegian-roberta-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## Roberta-Base
This repo trains roberta-base from scratch on the Norwegian training subset of Oscar containing roughly 4.7 GB of data according to this example.
Training is done on a TPUv3-8 in Flax. More statistics on the training run can be found under URL.
| [
"## Roberta-Base\n\nThis repo trains roberta-base from scratch on the Norwegian training subset of Oscar containing roughly 4.7 GB of data according to this example.\n\nTraining is done on a TPUv3-8 in Flax. More statistics on the training run can be found under URL."
] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## Roberta-Base\n\nThis repo trains roberta-base from scratch on the Norwegian training subset of Oscar containing roughly 4.7 GB of data according to this example.\n\nTraining is done on a TPUv3-8 ... |
fill-mask | transformers | ## Roberta-Large
This repo trains [roberta-large](https://huggingface.co/roberta-large) from scratch on the [Norwegian training subset of Oscar](https://oscar-corpus.com/) containing roughly 4.7 GB of data.
A ByteLevelBPETokenizer as shown in [this]( ) blog post was trained on the whole [Norwegian training subset of ... | {} | patrickvonplaten/norwegian-roberta-large | null | [
"transformers",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## Roberta-Large
This repo trains roberta-large from scratch on the Norwegian training subset of Oscar containing roughly 4.7 GB of data.
A ByteLevelBPETokenizer as shown in this blog post was trained on the whole Norwegian training subset of Oscar.
Training is done on a TPUv3-8 in Flax. The training script as well ... | [
"## Roberta-Large\n\nThis repo trains roberta-large from scratch on the Norwegian training subset of Oscar containing roughly 4.7 GB of data.\n\nA ByteLevelBPETokenizer as shown in this blog post was trained on the whole Norwegian training subset of Oscar.\n\nTraining is done on a TPUv3-8 in Flax. The training scri... | [
"TAGS\n#transformers #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## Roberta-Large\n\nThis repo trains roberta-large from scratch on the Norwegian training subset of Oscar containing roughly 4.7 GB of data.\n\nA ByteLevelBPETokenizer as shown in this blog post was ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-300m-mls-german-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "multilingual_librispeech", "generated_from_trainer"], "datasets": ["multilingual_librispeech"], "model-index": [{"name": "wav2vec2-300m-mls-german-ft", "results": []}]} | patrickvonplaten/phoneme_test_5_sv | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"multilingual_librispeech",
"generated_from_trainer",
"dataset:multilingual_librispeech",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-300m-mls-german-ft
===========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MULTILINGUAL\_LIBRISPEECH - GERMAN 10h dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2398
* Wer: 0.1520
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
text2text-generation | transformers | # Roberta2Roberta Summarization with 🤗 EncoderDecoder Framework
This model is a Roberta2Roberta model fine-tuned on summarization.
Roberta2Roberta is a `EncoderDecoderModel`, meaning that both the encoder and the decoder are `roberta-base`
RoBERTa models. Leveraging the [EncoderDecoderFramework](https://huggingface... | {} | patrickvonplaten/roberta2roberta-cnn_dailymail-fp16 | null | [
"transformers",
"pytorch",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder_decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Roberta2Roberta Summarization with EncoderDecoder Framework
===========================================================
This model is a Roberta2Roberta model fine-tuned on summarization.
Roberta2Roberta is a 'EncoderDecoderModel', meaning that both the encoder and the decoder are 'roberta-base'
RoBERTa models. Leve... | [] | [
"TAGS\n#transformers #pytorch #encoder_decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | # Shared Roberta2Roberta Summarization with 🤗 EncoderDecoder Framework
This model is a shared Roberta2Roberta model, meaning that the encoder and decoder weights are tied, fine-tuned on summarization.
Roberta2Roberta is a `EncoderDecoderModel`, meaning that both the encoder and the decoder are `roberta-base`
RoBERT... | {} | patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16 | null | [
"transformers",
"pytorch",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder_decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Shared Roberta2Roberta Summarization with EncoderDecoder Framework
==================================================================
This model is a shared Roberta2Roberta model, meaning that the encoder and decoder weights are tied, fine-tuned on summarization.
Roberta2Roberta is a 'EncoderDecoderModel', meaning ... | [] | [
"TAGS\n#transformers #pytorch #encoder_decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
Shared RoBERTa2RoBERTa Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* model fine-tuned on the *BBC XSum* summarization dataset.
The model achieves a **16.89** ROUGE-2 score on *BBC XSUM*'s test dataset.
For more details on how the model was fine-tuned, please refer to
[th... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["xsum"]} | patrickvonplaten/roberta_shared_bbc_xsum | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Shared RoBERTa2RoBERTa Summarization with EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* model fine-tuned on the *BBC XSum* summarization dataset.
The model achieves a 16.89 ROUGE-2 score on *BBC XSUM*'s test dataset.
For more details on how the model was fine-tuned, please refer to
this note... | [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sat-base
This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co/microsoft/unispeech-sat-bas... | {"tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "sat-base", "results": []}]} | patrickvonplaten/sat-base | null | [
"transformers",
"pytorch",
"tensorboard",
"unispeech-sat",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #unispeech-sat #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us
| sat-base
========
This model is a fine-tuned version of microsoft/unispeech-sat-base on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7014
* Wer: 0.5374
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\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 #unispeech-sat #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_b... |
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. -->
# sew-d-mid-400k-librispeech-clean-100h-ft
This model is a fine-tuned version of [asapp/sew-d-mid-400k](https://huggingface.co/asa... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer"], "model-index": [{"name": "sew-d-mid-400k-librispeech-clean-100h-ft", "results": []}]} | patrickvonplaten/sew-d-mid-400k-librispeech-clean-100h-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"sew-d",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #sew-d #automatic-speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| sew-d-mid-400k-librispeech-clean-100h-ft
========================================
This model is a fine-tuned version of asapp/sew-d-mid-400k on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3540
* Wer: 1.0536
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 64\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #sew-d #automatic-speech-recognition #librispeech_asr #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: 3e-05\n* train\\_ba... |
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. -->
# sew-d-small-100k-ft-timit-2
This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-sm... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "sew-d-small-100k-ft-timit-2", "results": []}]} | patrickvonplaten/sew-d-small-100k-ft-timit-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"sew-d",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #sew-d #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
| sew-d-small-100k-ft-timit-2
===========================
This model is a fine-tuned version of asapp/sew-d-small-100k on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7357
* Wer: 0.7935
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\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 #sew-d #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001... |
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. -->
# sew-d-small-100k-ft-timit
This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-smal... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "sew-d-small-100k-ft-timit", "results": []}]} | patrickvonplaten/sew-d-small-100k-ft-timit | null | [
"transformers",
"pytorch",
"tensorboard",
"sew-d",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #sew-d #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
| sew-d-small-100k-ft-timit
=========================
This model is a fine-tuned version of asapp/sew-d-small-100k on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7482
* Wer: 0.7987
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\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 #sew-d #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001... |
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. -->
# sew-d-small-100k-timit
This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-1... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "sew-d-small-100k-timit", "results": []}]} | patrickvonplaten/sew-d-small-100k-timit | null | [
"transformers",
"pytorch",
"tensorboard",
"sew-d",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #sew-d #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
| sew-d-small-100k-timit
======================
This model is a fine-tuned version of asapp/sew-d-small-100k on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7541
* Wer: 0.8061
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\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 #sew-d #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001... |
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. -->
# sew-mid-100k-librispeech-clean-100h-ft
This model is a fine-tuned version of [asapp/sew-mid-100k](https://huggingface.co/asapp/s... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer"], "model-index": [{"name": "sew-mid-100k-librispeech-clean-100h-ft", "results": []}]} | patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"sew",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #sew #automatic-speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| sew-mid-100k-librispeech-clean-100h-ft
======================================
This model is a fine-tuned version of asapp/sew-mid-100k on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1976
* Wer: 0.1665
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 64\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #sew #automatic-speech-recognition #librispeech_asr #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: 3e-05\n* train\\_batc... |
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. -->
# sew-small-100k-timit
This model is a fine-tuned version of [asapp/sew-small-100k](https://huggingface.co/asapp/sew-small-100k) o... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "sew-small-100k-timit", "results": []}]} | patrickvonplaten/sew-small-100k-timit | null | [
"transformers",
"pytorch",
"tensorboard",
"sew",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #sew #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
| sew-small-100k-timit
====================
This model is a fine-tuned version of asapp/sew-small-100k on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4926
* Wer: 0.2988
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #sew #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n... |
text2text-generation | transformers | This model trains [T5-V1_1-base](https://huggingface.co/google/t5-v1_1-base) on the Norwegian dataset of [Oscar](https://huggingface.co/datasets/oscar). Note that the original configuration is slightly change (dropout is set to 0).
The official [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/blob/mast... | {} | patrickvonplaten/t5-base-norwegian | null | [
"transformers",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model trains T5-V1_1-base on the Norwegian dataset of Oscar. Note that the original configuration is slightly change (dropout is set to 0).
The official run_t5_mlm_flax.py is copied into the repository and is run using the hyperparameters as defined in *run_t5.sh*.
Training loss can be seen directly on the model... | [] | [
"TAGS\n#transformers #jax #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | This is an example repo for a possible integration with TensorSpeech, see [here](https://github.com/TensorSpeech/TensorFlowTTS/pull/555). | {} | patrickvonplaten/tf_tts_fast_speech_2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is an example repo for a possible integration with TensorSpeech, see here. | [] | [
"TAGS\n#region-us \n"
] |
null | transformers | # Model Card for tiny-wav2vec2-no-tokenizer
# Model Details
## Model Description
- **Developed by:** More information needed
- **Shared by [Optional]:** Patrick von Platen
- **Model type:** Automatic Speech Recognition
- **Language(s) (NLP):** en
- **License:** More information needed
- **Related Models:**
... | {"language": ["en"]} | patrickvonplaten/tiny-wav2vec2-no-tokenizer | null | [
"transformers",
"pytorch",
"tf",
"wav2vec2",
"en",
"arxiv:2006.11477",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #wav2vec2 #en #arxiv-2006.11477 #arxiv-1910.09700 #endpoints_compatible #region-us
| # Model Card for tiny-wav2vec2-no-tokenizer
# Model Details
## Model Description
- Developed by: More information needed
- Shared by [Optional]: Patrick von Platen
- Model type: Automatic Speech Recognition
- Language(s) (NLP): en
- License: More information needed
- Related Models:
- Parent Model: Wav2Ve... | [
"# Model Card for tiny-wav2vec2-no-tokenizer",
"# Model Details",
"## Model Description\n \n \n- Developed by: More information needed\n- Shared by [Optional]: Patrick von Platen\n- Model type: Automatic Speech Recognition\n- Language(s) (NLP): en\n- License: More information needed\n- Related Models:\n - Par... | [
"TAGS\n#transformers #pytorch #tf #wav2vec2 #en #arxiv-2006.11477 #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for tiny-wav2vec2-no-tokenizer",
"# Model Details",
"## Model Description\n \n \n- Developed by: More information needed\n- Shared by [Optional]: Patrick von Platen\n- Model ... |
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. -->
# unispeech-large-1500h-cv-timit
This model is a fine-tuned version of [microsoft/unispeech-large-1500h-cv](https://huggingface.co... | {"tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "unispeech-large-1500h-cv-timit", "results": []}]} | patrickvonplaten/unispeech-large-1500h-cv-timit | null | [
"transformers",
"pytorch",
"tensorboard",
"unispeech",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #unispeech #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us
| unispeech-large-1500h-cv-timit
==============================
This model is a fine-tuned version of microsoft/unispeech-large-1500h-cv on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3099
* Wer: 0.2196
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\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 #unispeech #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batc... |
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. -->
# unispeech-sat-base-plus-timit-ft
This model is a fine-tuned version of [microsoft/unispeech-sat-base-plus](https://huggingface.c... | {"tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "unispeech-sat-base-plus-timit-ft", "results": []}]} | patrickvonplaten/unispeech-sat-base-plus-timit-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"unispeech-sat",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #unispeech-sat #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us
| unispeech-sat-base-plus-timit-ft
================================
This model is a fine-tuned version of microsoft/unispeech-sat-base-plus on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6549
* Wer: 0.4051
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\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 #unispeech-sat #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_... |
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. -->
# unispeech-sat-base-timit-ft
This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co/microsof... | {"tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "unispeech-sat-base-timit-ft", "results": []}]} | patrickvonplaten/unispeech-sat-base-timit-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"unispeech-sat",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #unispeech-sat #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us
| unispeech-sat-base-timit-ft
===========================
This model is a fine-tuned version of microsoft/unispeech-sat-base on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6712
* Wer: 0.4101
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\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 #unispeech-sat #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_... |
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. -->
# unispeech-sat-large-timit-ft
This model is a fine-tuned version of [microsoft/unispeech-sat-large](https://huggingface.co/micros... | {"tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "unispeech-sat-large-timit-ft", "results": []}]} | patrickvonplaten/unispeech-sat-large-timit-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"unispeech-sat",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #unispeech-sat #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #has_space #region-us
| unispeech-sat-large-timit-ft
============================
This model is a fine-tuned version of microsoft/unispeech-sat-large on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6074
* Wer: 0.3880
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #unispeech-sat #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-100m-mls-german-ft-2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-100m](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "multilingual_librispeech", "generated_from_trainer"], "datasets": ["multilingual_librispeech"], "model-index": [{"name": "wav2vec2-100m-mls-german-ft-2", "results": []}]} | patrickvonplaten/wav2vec2-100m-mls-german-ft-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"multilingual_librispeech",
"generated_from_trainer",
"dataset:multilingual_librispeech",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-100m-mls-german-ft-2
=============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-100m on the MULTILINGUAL\_LIBRISPEECH - GERMAN dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9304
* Wer: 1.0
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\n* o... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-100m-mls-german-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-100m](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "multilingual_librispeech", "generated_from_trainer"], "datasets": ["multilingual_librispeech"], "model-index": [{"name": "wav2vec2-100m-mls-german-ft", "results": []}]} | patrickvonplaten/wav2vec2-100m-mls-german-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"multilingual_librispeech",
"generated_from_trainer",
"dataset:multilingual_librispeech",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-100m-mls-german-ft
===========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-100m on the MULTILINGUAL\_LIBRISPEECH - GERMAN dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9325
* Wer: 1.0
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\n* op... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers |
To rerun this experiment, please clone this directory and run:
```bash
python create_model.py
```
followed by
```bash
./run_librispeech.sh
```
<!-- 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... | {"tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer", "asr_seq2esq"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingface.co/speech_sa... | patrickvonplaten/wav2vec2-2-bart-base | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"asr_seq2esq",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #librispeech_asr #generated_from_trainer #asr_seq2esq #endpoints_compatible #region-us
|
To rerun this experiment, please clone this directory and run:
followed by
# wav2vec2-2-bart-base
This model is a fine-tuned version of facebook/wav2vec2-base and bart-base on the librispeech_asr - clean dataset.
It achieves the following results on the evaluation set:
- Loss: 0.405
- Wer: 0.0728
## Model ... | [
"# wav2vec2-2-bart-base\n\nThis model is a fine-tuned version of facebook/wav2vec2-base and bart-base on the librispeech_asr - clean dataset.\n \nIt achieves the following results on the evaluation set:\n- Loss: 0.405\n- Wer: 0.0728",
"## Model description\n\nMore information needed",
"## Intended uses & limit... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #librispeech_asr #generated_from_trainer #asr_seq2esq #endpoints_compatible #region-us \n",
"# wav2vec2-2-bart-base\n\nThis model is a fine-tuned version of facebook/wav2vec2-base and bart-base on the librispeech_asr ... |
automatic-speech-recognition | transformers |
To rerun this experiment, please clone this directory and run:
```bash
python create_model.py
```
followed by
```bash
./run_librispeech.sh
```
<!-- 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... | {"tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer", "asr_seq2esq"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingface.co/speech_sa... | patrickvonplaten/wav2vec2-2-bart-large | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"asr_seq2esq",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #librispeech_asr #generated_from_trainer #asr_seq2esq #endpoints_compatible #region-us
|
To rerun this experiment, please clone this directory and run:
followed by
# wav2vec2-2-bart-large
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 and bart-large on the librispeech_asr - clean dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3204
- Wer: 0.0486
... | [
"# wav2vec2-2-bart-large\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-lv60 and bart-large on the librispeech_asr - clean dataset.\n \nIt achieves the following results on the evaluation set:\n- Loss: 0.3204\n- Wer: 0.0486",
"## Model description\n\nMore information needed",
"## Intended use... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #librispeech_asr #generated_from_trainer #asr_seq2esq #endpoints_compatible #region-us \n",
"# wav2vec2-2-bart-large\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-lv60 and bart-large on the librispe... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-300m-mls-german-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "multilingual_librispeech", "generated_from_trainer"], "datasets": ["multilingual_librispeech"], "model-index": [{"name": "wav2vec2-300m-mls-german-ft", "results": []}]} | patrickvonplaten/wav2vec2-300m-mls-german-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"multilingual_librispeech",
"generated_from_trainer",
"dataset:multilingual_librispeech",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-300m-mls-german-ft
===========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MULTILINGUAL\_LIBRISPEECH - GERMAN 10h dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2398
* Wer: 0.1520
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers | Fine-tuning of `wav2vec2-base` on 100h of Librispeech training data. Results on "clean" data are very similar to the ones of the [official model](https://huggingface.co/facebook/wav2vec2-base-100h). However, the result on "other" is significantly worse - the model seems to have overfitting to the "clean" data.
Model w... | {} | patrickvonplaten/wav2vec2-base-100h-13K-steps | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Fine-tuning of 'wav2vec2-base' on 100h of Librispeech training data. Results on "clean" data are very similar to the ones of the official model. However, the result on "other" is significantly worse - the model seems to have overfitting to the "clean" data.
Model was trained on *librispeech-clean-train.100* with foll... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
Second fine-tuning try of `wav2vec2-base`. Results are similar to the ones reported in https://huggingface.co/facebook/wav2vec2-base-100h.
Model was trained on *librispeech-clean-train.100* with following hyper-parameters:
- 2 GPUs Titan RTX
- Total update steps 11000
- Batch size per GPU: 32 corresponding to a *tot... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "IEMOCAP sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav"}, {"example_title": "IEMOCAP sample 2", "src": "https:... | patrickvonplaten/wav2vec2-base-100h-2nd-try | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us
| Second fine-tuning try of 'wav2vec2-base'. Results are similar to the ones reported in URL
Model was trained on *librispeech-clean-train.100* with following hyper-parameters:
* 2 GPUs Titan RTX
* Total update steps 11000
* Batch size per GPU: 32 corresponding to a *total batch size* of ca. ~750 seconds
* Adam with ... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | Hello | {} | patrickvonplaten/wav2vec2-base-100h-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us
| Hello | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
Test
| {"tags": ["correct"]} | patrickvonplaten/wav2vec2-base-es-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"correct",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #correct #endpoints_compatible #region-us
|
Test
| [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #correct #endpoints_compatible #region-us \n"
] |
null | transformers | https://wandb.ai/patrickvonplaten/test/reports/Wav2Vec2-Base--VmlldzoxMTUyODQ0?accessToken=rg6e8u9yizx964k8q47zctq1m4afpvtn1i3qi9exgdmzip6xwkfzvagfajpzj55n | {} | patrickvonplaten/wav2vec2-base-repro-960h-libri-85k-steps | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #endpoints_compatible #region-us
| URL | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-repro-timit
This model is a fine-tuned version of [patrickvonplaten/wav2vec2-base-repro-960h-libri-85k-steps](http... | {"tags": ["automatic-speech-recognition", "timit_asr", "generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "wav2vec2-base-repro-timit", "results": []}]} | patrickvonplaten/wav2vec2-base-repro-timit | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us
| wav2vec2-base-repro-timit
=========================
This model is a fine-tuned version of patrickvonplaten/wav2vec2-base-repro-960h-libri-85k-steps on the TIMIT\_ASR - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8562
* Wer: 0.5484
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 1\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 #timit_asr #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | patrickvonplaten/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4888
* Wer: 0.3392
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
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