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text-generation | transformers |
# Sheldon GPT Model | {"tags": ["conversational"]} | piyushdubey/DialoGPT-Mi | 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
|
# Sheldon GPT Model | [
"# Sheldon GPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sheldon GPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | pjheslin/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2227
* Accuracy: 0.9255
* F1: 0.9255
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned_xsum
This model is a fine-tuned version of [pki/t5-small-finetuned_xsum](https://huggingface.co/pki/t5-small-... | {"tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned_xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "default"}, "metrics": [{"type... | pki/t5-small-finetuned_xsum | null | [
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"generated_from_trainer",
"dataset:xsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned\_xsum
========================
This model is a fine-tuned version of pki/t5-small-finetuned\_xsum on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0479
* Rouge1: 34.0559
* Rouge2: 12.7506
* Rougel: 27.6762
* Rougelsum: 27.68
* Gen Len: 18.7924
Model descri... | [
"### 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: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
null | null | # Pre-trained Comformer-CTC model for aishell with icefall | {} | pkufool/icefall_asr_aishell_conformer_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Pre-trained Comformer-CTC model for aishell with icefall | [
"# Pre-trained Comformer-CTC model for aishell with icefall"
] | [
"TAGS\n#region-us \n",
"# Pre-trained Comformer-CTC model for aishell with icefall"
] |
null | null | # TDNN-LSTM model for aishell with icefall | {} | pkufool/icefall_asr_aishell_tdnn_lstm_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # TDNN-LSTM model for aishell with icefall | [
"# TDNN-LSTM model for aishell with icefall"
] | [
"TAGS\n#region-us \n",
"# TDNN-LSTM model for aishell with icefall"
] |
null | null |
# Pre-trained Conformer-CTC models for the librispeech dataset with icefall.
The model was trained on full [LibriSpeech](http://openslr.org/12/) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/pull/13) for more details of this model.
## How to use
See (https:... | {"language": ["en"], "license": "apache-2.0"} | pkufool/icefall_asr_librispeech_conformer_ctc | null | [
"en",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#en #license-apache-2.0 #has_space #region-us
| Pre-trained Conformer-CTC models for the librispeech dataset with icefall.
==========================================================================
The model was trained on full LibriSpeech with the scripts in icefall.
See (URL for more details of this model.
How to use
----------
See (URL
Training procedu... | [] | [
"TAGS\n#en #license-apache-2.0 #has_space #region-us \n"
] |
null | null | # Pre-trained TDNN-LSTM-CTC models for the librispeech dataset with icefall.
The model was trained on full [LibriSpeech](http://openslr.org/12/) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/tdnn_lstm_ctc) for more details of t... | {} | pkufool/icefall_asr_librispeech_tdnn-lstm_ctc | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Pre-trained TDNN-LSTM-CTC models for the librispeech dataset with icefall.
==========================================================================
The model was trained on full LibriSpeech with the scripts in icefall.
See (URL for more details of this model.
How to use
----------
See (URL
Training procedu... | [] | [
"TAGS\n#region-us \n"
] |
null | null | * Install requirements
```
pip install jieba
```
* Generate words.txt
```bash
data_dir=/path/to/wenetspeech
# the data_dir contains:
# tree -L 2 .
# .
# |-- TERMS_OF_ACCESS
# |-- WenetSpeech.json
# |-- audio
# |-- dev
# |-- test_meeting
# |-- test_net
# `-- train
grep "\"text\":" $data_dir/WenetSpeech.json... | {} | pkufool/wenet_speech_lm | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| * Install requirements
* Generate URL
* Generate N-gram model
| [] | [
"TAGS\n#region-us \n"
] |
summarization | transformers |
# French T5 Abstractive Text Summarization
~~Version 1.0 (I will keep improving the model's performances.)~~
Version 2.0 is here! (with improved performances of course)
I trained the model on 13x more data than v1.
ROUGE-1: 44.5252
ROUGE-2: 22.652
ROUGE-L: 29.8866
## Model description
This model is a T5 Transf... | {"language": "fr", "tags": ["pytorch", "t5", "seq2seq", "summarization"], "datasets": "cnn_dailymail", "widget": [{"text": "Apollo 11 est une mission du programme spatial am\u00e9ricain Apollo au cours de laquelle, pour la premi\u00e8re fois, des hommes se sont pos\u00e9s sur la Lune, le lundi 21 juillet 1969. L'agence... | plguillou/t5-base-fr-sum-cnndm | null | [
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"fr"
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|
# French T5 Abstractive Text Summarization
~~Version 1.0 (I will keep improving the model's performances.)~~
Version 2.0 is here! (with improved performances of course)
I trained the model on 13x more data than v1.
ROUGE-1: 44.5252
ROUGE-2: 22.652
ROUGE-L: 29.8866
## Model description
This model is a T5 Transf... | [
"# French T5 Abstractive Text Summarization\n\n~~Version 1.0 (I will keep improving the model's performances.)~~\n\nVersion 2.0 is here! (with improved performances of course)\n\nI trained the model on 13x more data than v1.\n\nROUGE-1: 44.5252\n\nROUGE-2: 22.652\n\nROUGE-L: 29.8866",
"## Model description\n\nThi... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #summarization #fr #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# French T5 Abstractive Text Summarization\n\n~~Version 1.0 (I will keep improving the model's performances.... |
text-classification | transformers | language: en
tags:
- sentiment
- distilbert-
pipeline_tag: text-classification
| {} | poipii/yelp_sentiment_distilbert-base-uncased_tuned | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| language: en
tags:
- sentiment
- distilbert-
pipeline_tag: text-classification
| [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #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. -->
# Wav2Vec2-XLSR-300m-es
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav... | {"language": ["es"], "license": "apache-2.0", "tags": ["common_voice_8_0", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wave2vec-xls-r-300m-es", "results": [{"task": {"type": ... | polodealvarado/xls-r-300m-es | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice_8_0",
"generated_from_trainer",
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"robust-speech-event",
"es",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice_8_0 #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #es #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-XLSR-300m-es
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the spanish common\_voice dataset thanks to the GPU credits generously given by the OVHcloud for the Speech Recognition challenge.
It achieves the following results on the evaluation set
Without LM:
*... | [
"### Usage with 5-gram.\n\n\nThe model can be used with n-gram (n=5) included in the processor as follows.\n\n\nOn the other, you can execute the URL file for evaluation",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice_8_0 #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #es #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"###... |
text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | pompeiifreckles/DialoGPT-medium-Rick | 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 Sanchez DialoGPT Model | [
"# Rick Sanchez DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez DialoGPT Model"
] |
text-classification | transformers |
Created only for study :)
| {"language": ["th"], "license": "apache-2.0", "tags": ["sentiment-analysis"], "datasets": ["wongnai_reviews", "wisesight_sentiment", "generated_reviews_enth"], "widget": [{"text": "\u0e42\u0e2d\u0e42\u0e2b\u0e49 \u0e0a\u0e48\u0e2d\u0e07\u0e19\u0e35\u0e49\u0e40\u0e1b\u0e34\u0e14\u0e42\u0e25\u0e01\u0e40\u0e23\u0e32\u0e21... | poom-sci/WangchanBERTa-finetuned-sentiment | null | [
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"tensorboard",
"camembert",
"text-classification",
"sentiment-analysis",
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"dataset:wongnai_reviews",
"dataset:wisesight_sentiment",
"dataset:generated_reviews_enth",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region... | null | 2022-03-02T23:29:05+00:00 | [] | [
"th"
] | TAGS
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|
Created only for study :)
| [] | [
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] |
translation | transformers |
created for study | {"language": ["en"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["go_emotions"]} | poom-sci/bert-base-uncased-multi-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"translation",
"en",
"dataset:go_emotions",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #translation #en #dataset-go_emotions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
created for study | [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #translation #en #dataset-go_emotions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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... | pooyaphoenix/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-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: 0.7904
* Matthews Correlation: 0.5227
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... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
audio-to-audio | asteroid |
## Asteroid model `Samuele Cornell/FasNetTAC_TACDataset_separatenoisy`
Imported from [Zenodo](https://zenodo.org/record/4557489)
### Description:
This model was trained by popcornell using the TAC/TAC recipe in Asteroid. It was trained on the separate_noisy task of the TACDataset dataset.
### Training config:
```yam... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "FasNet-TAC", "audio-to-audio", "multichannel", "beamforming"], "datasets": ["TACDataset", "sep_noisy"]} | popcornell/FasNetTAC-paper | null | [
"asteroid",
"pytorch",
"audio",
"FasNet-TAC",
"audio-to-audio",
"multichannel",
"beamforming",
"dataset:TACDataset",
"dataset:sep_noisy",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #FasNet-TAC #audio-to-audio #multichannel #beamforming #dataset-TACDataset #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'Samuele Cornell/FasNetTAC_TACDataset_separatenoisy'
Imported from Zenodo
### Description:
This model was trained by popcornell using the TAC/TAC recipe in Asteroid. It was trained on the separate_noisy task of the TACDataset dataset.
### Training config:
### Results:
### License notice:
This w... | [
"## Asteroid model 'Samuele Cornell/FasNetTAC_TACDataset_separatenoisy'\nImported from Zenodo",
"### Description:\nThis model was trained by popcornell using the TAC/TAC recipe in Asteroid. It was trained on the separate_noisy task of the TACDataset dataset.",
"### Training config:",
"### Results:",
"### Li... | [
"TAGS\n#asteroid #pytorch #audio #FasNet-TAC #audio-to-audio #multichannel #beamforming #dataset-TACDataset #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'Samuele Cornell/FasNetTAC_TACDataset_separatenoisy'\nImported from Zenodo",
"### Description:\nThis model was trained by popcor... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xlsum dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xlsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xlsum", "type": "xlsum", "args": "c... | porpaul/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2188
* Rouge1: 0.5217
* Rouge2: 0.0464
* Rougel: 0.527
* Rougelsum: 0.5215
* Gen Len: 6.7441
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 5\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
null | null | TTAI | {} | pouryajj/TTQnA | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| TTAI | [] | [
"TAGS\n#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-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": []}]} | pourzare/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: 0.3821
* Wer: 0.3841
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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_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: 1... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | ppn/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 | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
fill-mask | transformers | This model is pre-trained on blog articles from AWS Blogs.
## Pre-training corpora
The input text contains around 3000 blog articles on [AWS Blogs website](https://aws.amazon.com/blogs/) technical subject matter including AWS products, tools and tutorials.
## Pre-training details
I picked a Roberta architecture for ... | {} | pradhyra/AWSBlogBert | 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
| This model is pre-trained on blog articles from AWS Blogs.
## Pre-training corpora
The input text contains around 3000 blog articles on AWS Blogs website technical subject matter including AWS products, tools and tutorials.
## Pre-training details
I picked a Roberta architecture for masked language modeling (6-layer... | [
"## Pre-training corpora\nThe input text contains around 3000 blog articles on AWS Blogs website technical subject matter including AWS products, tools and tutorials.",
"## Pre-training details\nI picked a Roberta architecture for masked language modeling (6-layer, 768-hidden, 12-heads, 82M parameters) and its co... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## Pre-training corpora\nThe input text contains around 3000 blog articles on AWS Blogs website technical subject matter including AWS products, tools and tutorials.",
"## Pre-training details\nI ... |
text-classification | transformers | If you use the model, please consider citing this paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
... | {} | prajjwal1/albert-base-v1-mnli | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"arxiv:2110.01518",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01518"
] | [] | TAGS
#transformers #pytorch #albert #text-classification #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us
| If you use the model, please consider citing this paper
| [] | [
"TAGS\n#transformers #pytorch #albert #text-classification #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
... | {} | prajjwal1/albert-base-v2-mnli | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"text-classification",
"arxiv:2110.01518",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01518"
] | [] | TAGS
#transformers #pytorch #safetensors #albert #text-classification #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us
| If you use the model, please consider citing the paper
Original Implementation and more info can be found in this Github repository. | [] | [
"TAGS\n#transformers #pytorch #safetensors #albert #text-classification #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... | {} | prajjwal1/bert-medium-mnli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962",
"2110.01518"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-1908.08962 #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us
| The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. These models are trained on MNLI.
If you u... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-1908.08962 #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-tiny](https://huggingface.co/prajjwal1/bert-tiny), [bert... | {"language": ["en"], "license": ["mit"], "tags": ["BERT", "MNLI", "NLI", "transformer", "pre-training"]} | prajjwal1/bert-medium | null | [
"transformers",
"pytorch",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962",
"2110.01518"
] | [
"en"
] | TAGS
#transformers #pytorch #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #has_space #region-us
|
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository.
This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-small. They were introduced in the study 'Well-Read Students Learn Better: ... | [] | [
"TAGS\n#transformers #pytorch #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... | {} | prajjwal1/bert-mini-mnli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962",
"2110.01518"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-1908.08962 #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us
| The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. These models are trained on MNLI.
If you us... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-1908.08962 #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-small](https://huggingface.co/prajjwal1/bert-small) and ... | {"language": ["en"], "license": ["mit"], "tags": ["BERT", "MNLI", "NLI", "transformer", "pre-training"]} | prajjwal1/bert-mini | null | [
"transformers",
"pytorch",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962",
"2110.01518"
] | [
"en"
] | TAGS
#transformers #pytorch #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #has_space #region-us
|
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository.
This is one of the smaller pre-trained BERT variants, together with bert-small and bert-medium. They were introduced in the study 'Well-Read Students Learn Better: On the Im... | [] | [
"TAGS\n#transformers #pytorch #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... | {} | prajjwal1/bert-small-mnli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962",
"2110.01518"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-1908.08962 #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us
| The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. These models are trained on MNLI.
If you us... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-1908.08962 #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-tiny](https://huggingface.co/prajjwal1/bert-small), [ber... | {"language": ["en"], "license": ["mit"], "tags": ["BERT", "MNLI", "NLI", "transformer", "pre-training"]} | prajjwal1/bert-small | null | [
"transformers",
"pytorch",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962",
"2110.01518"
] | [
"en"
] | TAGS
#transformers #pytorch #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #has_space #region-us
|
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository.
This is one of the smaller pre-trained BERT variants, together with bert-tiny, bert-mini and bert-medium. They were introduced in the study 'Well-Read Students Learn Better:... | [] | [
"TAGS\n#transformers #pytorch #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... | {} | prajjwal1/bert-tiny-mnli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962",
"2110.01518"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-1908.08962 #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us
| The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. These models are trained on MNLI.
If you us... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-1908.08962 #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-... | {"language": ["en"], "license": ["mit"], "tags": ["BERT", "MNLI", "NLI", "transformer", "pre-training"]} | prajjwal1/bert-tiny | null | [
"transformers",
"pytorch",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962",
"2110.01518"
] | [
"en"
] | TAGS
#transformers #pytorch #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #region-us
|
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository.
This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium. They were introduced in the study 'Well-Read Students Learn Better:... | [] | [
"TAGS\n#transformers #pytorch #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
... | {} | prajjwal1/bert_small | null | [
"transformers",
"pytorch",
"arxiv:2110.01518",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01518"
] | [] | TAGS
#transformers #pytorch #arxiv-2110.01518 #endpoints_compatible #region-us
| If you use the model, please consider citing the paper
Original Implementation and more info can be found in this Github repository. | [] | [
"TAGS\n#transformers #pytorch #arxiv-2110.01518 #endpoints_compatible #region-us \n"
] |
text-generation | transformers | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
Paper: https://arxiv.org/abs/2210.12478
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | {} | prajjwal1/ctrl_discovery_1 | null | [
"transformers",
"pytorch",
"ctrl",
"text-generation",
"arxiv:2210.12478",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2210.12478"
] | [] | TAGS
#transformers #pytorch #ctrl #text-generation #arxiv-2210.12478 #autotrain_compatible #endpoints_compatible #region-us
| Please refer to this repository (URL for usage instructions.
Paper: URL
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [] | [
"TAGS\n#transformers #pytorch #ctrl #text-generation #arxiv-2210.12478 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | {} | prajjwal1/ctrl_discovery_2 | null | [
"transformers",
"pytorch",
"ctrl",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Please refer to this repository (URL for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [] | [
"TAGS\n#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | {} | prajjwal1/ctrl_discovery_3 | null | [
"transformers",
"pytorch",
"ctrl",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Please refer to this repository (URL for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [] | [
"TAGS\n#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | {} | prajjwal1/ctrl_discovery_4 | null | [
"transformers",
"pytorch",
"ctrl",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Please refer to this repository (URL for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [] | [
"TAGS\n#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | {} | prajjwal1/ctrl_discovery_5 | null | [
"transformers",
"pytorch",
"ctrl",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Please refer to this repository (URL for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [] | [
"TAGS\n#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | Please refer to this repository (https://github.com/prajjwal1/discosense) for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | {} | prajjwal1/ctrl_discovery_flipped_1 | null | [
"transformers",
"pytorch",
"ctrl",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Please refer to this repository (URL for usage instructions.
---
language:
- en
tags:
- conditional
- text
- generation
license: "mit"
datasets:
- discofuse
- discovery
metrics:
- perplexity
- ppl
--- | [] | [
"TAGS\n#transformers #pytorch #ctrl #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | Roberta-base trained on MNLI.
| Task | Accuracy |
|---------|----------|
| MNLI | 86.32 |
| MNLI-mm | 86.43 |
You can also check out:
- `prajjwal1/roberta-base-mnli`
- `prajjwal1/roberta-large-mnli`
- `prajjwal1/albert-base-v2-mnli`
- `prajjwal1/albert-base-v1-mnli`
- `prajjwal1/albert-large-v2-mnli`
[@... | {} | prajjwal1/roberta-base-mnli | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Roberta-base trained on MNLI.
You can also check out:
* 'prajjwal1/roberta-base-mnli'
* 'prajjwal1/roberta-large-mnli'
* 'prajjwal1/albert-base-v2-mnli'
* 'prajjwal1/albert-base-v1-mnli'
* 'prajjwal1/albert-large-v2-mnli'
@prajjwal\_1
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
... | {} | prajjwal1/roberta-large-mnli | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:2110.01518",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01518"
] | [] | TAGS
#transformers #pytorch #roberta #text-classification #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us
| If you use the model, please consider citing the paper
Original Implementation and more info can be found in this Github repository.
Roberta-large trained on MNLI.
---
You can also check out:
* 'prajjwal1/roberta-base-mnli'
* 'prajjwal1/roberta-large-mnli'
* 'prajjwal1/albert-base-v2-mnli'
* 'prajjwal1/alb... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #arxiv-2110.01518 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
multiple-choice | transformers |
`RoBERTa` trained on HellaSwag dataset (`MultipleChoiceModel`). HellaSwag has a multiple choice questions format.
It gets around 74.99% accuracy.
[@prajjwal_1](https://twitter.com/prajjwal_1/)
| {"tags": ["pytorch", "commonsense-reasoning", "sentence-completion"], "datasets": ["hellaswag"]} | prajjwal1/roberta_hellaswag | null | [
"transformers",
"pytorch",
"roberta",
"multiple-choice",
"commonsense-reasoning",
"sentence-completion",
"dataset:hellaswag",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #multiple-choice #commonsense-reasoning #sentence-completion #dataset-hellaswag #endpoints_compatible #region-us
|
'RoBERTa' trained on HellaSwag dataset ('MultipleChoiceModel'). HellaSwag has a multiple choice questions format.
It gets around 74.99% accuracy.
@prajjwal_1
| [] | [
"TAGS\n#transformers #pytorch #roberta #multiple-choice #commonsense-reasoning #sentence-completion #dataset-hellaswag #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | pranav1015/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"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 #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| 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: 0.8486
* Matthews Correlation: 0.5209
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... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
text-generation | transformers |
# GPT2 Genre Based Story Generator
## Model description
GPT2 fine-tuned on genre-based story generation.
## Intended uses
Used to generate stories based on user inputted genre and starting prompts.
## How to use
#### Supported Genres
superhero, action, drama, horror, thriller, sci_fi
#### Input text format
\<BOS... | {} | pranavpsv/gpt2-genre-story-generator | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT2 Genre Based Story Generator
## Model description
GPT2 fine-tuned on genre-based story generation.
## Intended uses
Used to generate stories based on user inputted genre and starting prompts.
## How to use
#### Supported Genres
superhero, action, drama, horror, thriller, sci_fi
#### Input text format
\<BOS... | [
"# GPT2 Genre Based Story Generator",
"## Model description\n\nGPT2 fine-tuned on genre-based story generation.",
"## Intended uses\n\nUsed to generate stories based on user inputted genre and starting prompts.",
"## How to use",
"#### Supported Genres\nsuperhero, action, drama, horror, thriller, sci_fi",
... | [
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"# GPT2 Genre Based Story Generator",
"## Model description\n\nGPT2 fine-tuned on genre-based story generation.",
"## Intended uses\n\nUsed to generate st... |
text-generation | transformers |
# Test Model
| {"tags": ["conversational"]} | pranavtharoor/test | 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 Model
| [
"# Test Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test Model"
] |
feature-extraction | transformers | # Ancient Greek BERT
<img src="https://ichef.bbci.co.uk/images/ic/832xn/p02m4gzb.jpg"/>
The first and only available Ancient Greek sub-word BERT model!
State-of-the-art post fine-tuning on Part-of-Speech Tagging and Morphological Analysis.
Pre-trained weights are made available for a standard 12 layer, 768d BERT-ba... | {} | pranaydeeps/Ancient-Greek-BERT | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #endpoints_compatible #region-us
| # Ancient Greek BERT
<img src="URL
The first and only available Ancient Greek sub-word BERT model!
State-of-the-art post fine-tuning on Part-of-Speech Tagging and Morphological Analysis.
Pre-trained weights are made available for a standard 12 layer, 768d BERT-base model.
Further scripts for using the model and fi... | [
"# Ancient Greek BERT\n\n<img src=\"URL\n\nThe first and only available Ancient Greek sub-word BERT model!\n\nState-of-the-art post fine-tuning on Part-of-Speech Tagging and Morphological Analysis.\n\nPre-trained weights are made available for a standard 12 layer, 768d BERT-base model.\n\nFurther scripts for using ... | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #endpoints_compatible #region-us \n",
"# Ancient Greek BERT\n\n<img src=\"URL\n\nThe first and only available Ancient Greek sub-word BERT model!\n\nState-of-the-art post fine-tuning on Part-of-Speech Tagging and Morphological Analysis.\n\nPre-tr... |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | prao/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0586
* Precision: 0.9293
* Recall: 0.9385
* F1: 0.9339
* Accuracy: 0.9843
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "model-index": [{"name": "", "results": []}]} | preetham18/xls-r-hi-300m-8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5258
* Wer: 1.0073
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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_model_ner_skills` |
| **Version** | `0.0.2` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | premrawat/en_model_ner_skills | null | [
"spacy",
"token-classification",
"en",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #has_space #region-us
|
### Label Scheme
View label scheme (1 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #has_space #region-us \n",
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] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_ner_model` |
| **Version** | `0.1.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Aut... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | premrawat/en_ner_model | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (1 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_ner_skills` |
| **Version** | `0.1.0` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Au... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | premrawat/en_ner_skills | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (1 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | princebansal42/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6623
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
feature-extraction | transformers |
# Model Card for baikal-sentiment-ball
# Model Details
## Model Description
More information needed
- **Developed by:** Princeton NLP group
- **Shared by [Optional]:** Princeton NLP group
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More information needed
- **License:** More information ne... | {"tags": ["feature-extraction", "bert"]} | princeton-nlp/sup-simcse-bert-large-uncased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"arxiv:2104.08821",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #arxiv-2104.08821 #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for baikal-sentiment-ball
# Model Details
## Model Description
More information needed
- Developed by: Princeton NLP group
- Shared by [Optional]: Princeton NLP group
- Model type: Feature Extraction
- Language(s) (NLP): More information needed
- License: More information needed
- Parent Model:... | [
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"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: Princeton NLP group\n- Shared by [Optional]:... |
feature-extraction | transformers | # Model Card for sup-simcse-roberta-large
# Model Details
## Model Description
- **Developed by:** Princeton-nlp
- **Shared by [Optional]:** More information needed
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Related Model... | {"tags": ["feature-extraction"]} | princeton-nlp/sup-simcse-roberta-large | null | [
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"2104.08821",
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] | [] | TAGS
#transformers #pytorch #jax #roberta #feature-extraction #arxiv-2104.08821 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
| # Model Card for sup-simcse-roberta-large
# Model Details
## Model Description
- Developed by: Princeton-nlp
- Shared by [Optional]: More information needed
- Model type: Feature Extraction
- Language(s) (NLP): More information needed
- License: More information needed
- Related Models:
- Parent Model: ... | [
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"# Model Details",
"## Model Description\n \n \n \n- Developed by: Princeton-nlp\n- Shared by [Optional]: More infor... |
feature-extraction | transformers | # Model Card for unsup-simcse-bert-base-uncased
# Model Details
## Model Description
More information needed
- **Developed by:** Princeton NLP group
- **Shared by [Optional]:** Hugging Face
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More information needed
- **License:** More information ne... | {"tags": ["feature-extraction", "bert"]} | princeton-nlp/unsup-simcse-bert-base-uncased | null | [
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"2104.08821",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #arxiv-2104.08821 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
| # Model Card for unsup-simcse-bert-base-uncased
# Model Details
## Model Description
More information needed
- Developed by: Princeton NLP group
- Shared by [Optional]: Hugging Face
- Model type: Feature Extraction
- Language(s) (NLP): More information needed
- License: More information needed
- Related Model... | [
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"# Model Details",
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"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: Princeton NLP group\n- S... |
feature-extraction | transformers |
# Model Card for unsup-simcse-bert-large-uncased
# Model Details
## Model Description
More information needed
- **Developed by:** Princeton NLP group
- **Shared by [Optional]:** Princeton NLP group
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More information needed
- **License:** More infor... | {"tags": ["feature-extraction", "bert"]} | princeton-nlp/unsup-simcse-bert-large-uncased | null | [
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"1910.09700"
] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #arxiv-2104.08821 #arxiv-1910.09700 #endpoints_compatible #region-us
| Model Card for unsup-simcse-bert-large-uncased
==============================================
Model Details
=============
Model Description
-----------------
More information needed
* Developed by: Princeton NLP group
* Shared by [Optional]: Princeton NLP group
* Model type: Feature Extraction
* Language(s) (NL... | [
"### Preprocessing\n\n\nMore information needed",
"### Speeds, Sizes, Times\n\n\nHyperparameters\nThe model craters note in the associated GitHub Repo :\n\n\n\nEvaluation\n==========\n\n\nTesting Data, Factors & Metrics\n-------------------------------",
"### Testing Data\n\n\nThe model craters note in the asso... | [
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"### Speeds, Sizes, Times\n\n\nHyperparameters\nThe model craters note in the associated GitHub Repo :\n\n\n\nEvaluation\n=====... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PubMedBert-abstract-cord19-v2
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fullte... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["pritamdeka/cord-19-abstract"], "metrics": ["accuracy"], "base_model": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", "model-index": [{"name": "pubmedbert-abstract-cord19", "results": [{"task": {"type": "fill-mask", "name": "Masked La... | pritamdeka/PubMedBert-abstract-cord19-v2 | null | [
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"license:mit",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| PubMedBert-abstract-cord19-v2
=============================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the pritamdeka/cord-19-abstract dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2371
* Accuracy: 0.7247
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
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"### Training hyperparameters\n\n\nThe followi... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pubmedbert-abstract-cord19
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext]... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["pritamdeka/cord-19-abstract"], "model-index": [{"name": "PubMedBert-abstract-cord19", "results": []}]} | pritamdeka/PubMedBert-abstract-cord19 | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-pritamdeka/cord-19-abstract #license-mit #autotrain_compatible #endpoints_compatible #region-us
| pubmedbert-abstract-cord19
==========================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the pritamdeka/cord-19-abstract dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3005
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pubmedbert-fulltext-cord19
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext]... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["pritamdeka/cord-19-fulltext"], "metrics": ["accuracy"], "model-index": [{"name": "pubmedbert-fulltext-cord19", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "pritamdeka/cord-19-fulltext", "type": "... | pritamdeka/PubMedBert-fulltext-cord19 | null | [
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"dataset:pritamdeka/cord-19-fulltext",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-pritamdeka/cord-19-fulltext #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| pubmedbert-fulltext-cord19
==========================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the pritamdeka/cord-19-fulltext dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2667
* Accuracy: 0.7175
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-0... |
sentence-similarity | sentence-transformers |
# S-BioBert-snli-multinli-stsb
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model be... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pritamdeka/S-BioBert-snli-multinli-stsb | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# S-BioBert-snli-multinli-stsb
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
... | [
"# S-BioBert-snli-multinli-stsb\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers in... | [
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sentence-similarity | sentence-transformers |
# S-Biomed-Roberta-snli-multinli-stsb
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The base model used is [allenai/biomed_roberta_base](https://huggingface.co/allenai... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pritamdeka/S-Biomed-Roberta-snli-multinli-stsb | null | [
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"pytorch",
"roberta",
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"transformers",
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# S-Biomed-Roberta-snli-multinli-stsb
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The base model used is allenai/biomed_roberta_base which has been fine-tuned for sentence similarity.
## ... | [
"# S-Biomed-Roberta-snli-multinli-stsb\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The base model used is allenai/biomed_roberta_base which has been fine-tuned for sentence similarity."... | [
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"# S-Biomed-Roberta-snli-multinli-stsb\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be... |
sentence-similarity | sentence-transformers |
# pritamdeka/S-Bluebert-snli-multinli-stsb
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using t... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pritamdeka/S-Bluebert-snli-multinli-stsb | null | [
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"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# pritamdeka/S-Bluebert-snli-multinli-stsb
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers ... | [
"# pritamdeka/S-Bluebert-snli-multinli-stsb\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
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sentence-similarity | sentence-transformers |
# S-PubMedBert-MS-MARCO-SCIFACT
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model b... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pritamdeka/S-PubMedBert-MS-MARCO-SCIFACT | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# S-PubMedBert-MS-MARCO-SCIFACT
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
... | [
"# S-PubMedBert-MS-MARCO-SCIFACT\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers i... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
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sentence-similarity | sentence-transformers |
# pritamdeka/S-PubMedBert-MS-MARCO
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This is the [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://h... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pritamdeka/S-PubMedBert-MS-MARCO | null | [
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"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# pritamdeka/S-PubMedBert-MS-MARCO
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This is the microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model which has been fine-tuned over... | [
"# pritamdeka/S-PubMedBert-MS-MARCO\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis is the microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model which has been fine-tu... | [
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"# pritamdeka/S-PubMedBert-MS-MARCO\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used ... |
sentence-similarity | sentence-transformers |
# pritamdeka/S-Scibert-snli-multinli-stsb
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using t... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pritamdeka/S-Scibert-snli-multinli-stsb | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# pritamdeka/S-Scibert-snli-multinli-stsb
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers ... | [
"# pritamdeka/S-Scibert-snli-multinli-stsb \n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
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text2text-generation | transformers | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer)
| {} | prithivida/active_to_passive_styletransfer | null | [
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"autotrain_compatible",
"endpoints_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## This model belongs to the Styleformer project
Please refer to github page
| [
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] |
null | null | # NeuSpell: A Neural Spelling Correction Toolkit
This model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.
- [Refer to the Fork of the library (with HF hub support) in GitHub:](https://github.com/PrithivirajDamodaran/neuspell)
- ... | {"language": ["en"], "license": "MIT", "tags": ["BERT", "RNN"]} | prithivida/bertscrnn-probwordnoise | null | [
"pytorch",
"BERT",
"RNN",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#pytorch #BERT #RNN #en #region-us
| # NeuSpell: A Neural Spelling Correction Toolkit
This model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.
- Refer to the Fork of the library (with HF hub support) in GitHub:
- Refer to the original library in GitHub:
| [
"# NeuSpell: A Neural Spelling Correction Toolkit\nThis model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.\n- Refer to the Fork of the library (with HF hub support) in GitHub:\n- Refer to the original library in GitHub:"
] | [
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null | null | # NeuSpell: A Neural Spelling Correction Toolkit
This model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.
- [Refer to the Fork of the library (with HF hub support) in GitHub:](https://github.com/PrithivirajDamodaran/neuspell)
- ... | {"language": ["en"], "license": "MIT", "tags": ["CNN", "LSTM"]} | prithivida/cnn-lstm-probwordnoise | null | [
"pytorch",
"CNN",
"LSTM",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#pytorch #CNN #LSTM #en #region-us
| # NeuSpell: A Neural Spelling Correction Toolkit
This model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.
- Refer to the Fork of the library (with HF hub support) in GitHub:
- Refer to the original library in GitHub:
| [
"# NeuSpell: A Neural Spelling Correction Toolkit\nThis model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.\n- Refer to the Fork of the library (with HF hub support) in GitHub:\n- Refer to the original library in GitHub:"
] | [
"TAGS\n#pytorch #CNN #LSTM #en #region-us \n",
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null | null |
# NeuSpell: A Neural Spelling Correction Toolkit
This model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.
- [Refer to the Fork of the library (with HF hub support) in GitHub:](https://github.com/PrithivirajDamodaran/neuspell)... | {"language": ["en"], "license": "MIT", "tags": ["ELMo", "RNN"]} | prithivida/elmoscrnn-probwordnoise | null | [
"pytorch",
"ELMo",
"RNN",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#pytorch #ELMo #RNN #en #region-us
|
# NeuSpell: A Neural Spelling Correction Toolkit
This model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.
- Refer to the Fork of the library (with HF hub support) in GitHub:
- Refer to the original library in GitHub:
| [
"# NeuSpell: A Neural Spelling Correction Toolkit\n\nThis model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.\n\n- Refer to the Fork of the library (with HF hub support) in GitHub:\n- Refer to the original library in GitHub:... | [
"TAGS\n#pytorch #ELMo #RNN #en #region-us \n",
"# NeuSpell: A Neural Spelling Correction Toolkit\n\nThis model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.\n\n- Refer to the Fork of the library (with HF hub support) in Gi... |
text2text-generation | transformers | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer)
| {} | prithivida/formal_to_informal_styletransfer | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## This model belongs to the Styleformer project
Please refer to github page
| [
"## This model belongs to the Styleformer project\n\nPlease refer to github page"
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] |
text2text-generation | transformers | **This model is part of the Gramformer library** please refer to https://github.com/PrithivirajDamodaran/Gramformer/
| {} | prithivida/grammar_error_correcter_v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is part of the Gramformer library please refer to URL
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer)
| {} | prithivida/informal_to_formal_styletransfer | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## This model belongs to the Styleformer project
Please refer to github page
| [
"## This model belongs to the Styleformer project\n\nPlease refer to github page"
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"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## This model belongs to the Styleformer project\n\nPlease refer to github page"
] |
text2text-generation | transformers | # Parrot
## 1. What is Parrot?
Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. For more details on the library and usage please refer to the [github page](https://github.com/PrithivirajDamodar... | {} | prithivida/parrot_paraphraser_on_T5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Parrot
## 1. What is Parrot?
Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. For more details on the library and usage please refer to the github page
### Installation
### Quickstart
... | [
"# Parrot",
"## 1. What is Parrot?\nParrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. For more details on the library and usage please refer to the github page",
"### Installation",
"##... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Parrot",
"## 1. What is Parrot?\nParrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphras... |
text2text-generation | transformers | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer)
| {} | prithivida/passive_to_active_styletransfer | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## This model belongs to the Styleformer project
Please refer to github page
| [
"## This model belongs to the Styleformer project\n\nPlease refer to github page"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## This model belongs to the Styleformer project\n\nPlease refer to github page"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-YTTranscriptTrial2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-YTTranscriptTrial2", "results": []}]} | pritoms/distilgpt2-YTTranscriptTrial2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-YTTranscriptTrial2
=============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.8738
Model description
-----------------
More information needed
Intended uses & limitations
--------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-irll2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []} | pritoms/distilgpt2-finetuned-irll2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-irll2
==========================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.1925
Model description
-----------------
More information needed
Intended uses & limitations
--------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-mit-lecture
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the No... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-mit-lecture", "results": []}]} | pritoms/distilgpt2-finetuned-mit-lecture | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-mit-lecture
================================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8377
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-pgt
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []} | pritoms/distilgpt2-finetuned-pgt | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-pgt
========================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.0132
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | pritoms/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0540
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-YTTranscript23
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilrobert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-YTTranscript23", "results": []}]} | pritoms/distilroberta-base-YTTranscript23 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-YTTranscript23
=================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9258
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilr... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | pritoms/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4690
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-125M-Byethon
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-1... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []} | pritoms/gpt-neo-125M-Byethon | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| gpt-neo-125M-Byethon
====================
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6609
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-125M-finetuned-pgt
This model is a fine-tuned version of [pritoms/gpt-neo-125M-finetuned-pgt](https://huggingface.co/pri... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []} | pritoms/gpt-neo-125M-finetuned-pgt | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| gpt-neo-125M-finetuned-pgt
==========================
This model is a fine-tuned version of pritoms/gpt-neo-125M-finetuned-pgt on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6026
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-125M-philosophical-investigation
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-125M-philosophical-investigation", "results": []}]} | pritoms/gpt-neo-125M-philosophical-investigation | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| gpt-neo-125M-philosophical-investigation
========================================
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4443
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-python2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieve... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-finetuned-python2", "results": []}]} | pritoms/gpt2-finetuned-python2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-finetuned-python2
======================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9454
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-group2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the follo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-group2", "results": []}]} | pritoms/gpt2-group2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-group2
===========
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6769
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Traini... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text2text-generation | transformers |
```
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
def set_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
set_seed(42)
model = T5ForConditionalGeneration.from_pretrained("priyank/Generate_instructions_t5")
to... | {} | priyank/Generate_instructions_t5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Reference: URL | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad2-finetuned-squad
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/d... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "roberta-base-squad2-finetuned-squad", "results": []}]} | prk/roberta-base-squad2-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
| roberta-base-squad2-finetuned-squad
===================================
This model is a fine-tuned version of deepset/roberta-base-squad2 on a custom dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_si... |
text-generation | transformers |
# Joshua DialoGPT model
| {"tags": ["conversational"]} | professional/DialoGPT-small-joshua | 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
|
# Joshua DialoGPT model
| [
"# Joshua DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Joshua DialoGPT model"
] |
text-classification | transformers |
Github repository [here](https://github.com/sinanuozdemir/oreilly-transformers-nlp) | {"language": ["en"], "license": "apache-2.0", "tags": ["classification", "sequence-classification"]} | profoz/mlops-demo | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"classification",
"sequence-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #classification #sequence-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Github repository here | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #classification #sequence-classification #en #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. -->
# wav2vec2-large-xlsr-53-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-demo-colab", "results": []}]} | project2you/wav2vec2-large-xlsr-53-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-demo-colab
=================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6901
* Wer: 1.6299
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
summarization | transformers | ## BART-Ca fine-tuned on the CaSum dataset for summarization
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
... | {"language": "ca", "license": "mit", "tags": ["summarization"], "datasets": ["projecte-aina/casum"], "widget": [{"text": "El projecte AINA generar\u00e0 els recursos digitals i ling\u00fc\u00edstics necessaris per facilitar el desenvolupament d\u2019aplicacions basades en la intel\u00b7lig\u00e8ncia artificial i les te... | projecte-aina/bart-base-ca-casum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"ca",
"dataset:projecte-aina/casum",
"arxiv:2202.06871",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.06871"
] | [
"ca"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #ca #dataset-projecte-aina/casum #arxiv-2202.06871 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BART-Ca fine-tuned on the CaSum dataset for summarization
---------------------------------------------------------
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training data
+ Training procedure
- Token... | [
"### Training data\n\n\nAs training data, we used the CaSum dataset extracted from a newswire corpus crawled from the Catalan News Agency.",
"### Training procedure",
"#### Tokenization\n\n\nThe training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) with a vocabulary size of 51,200 ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #ca #dataset-projecte-aina/casum #arxiv-2202.06871 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nAs training data, we used the CaSum dataset extracted from a newswire corpus crawled from the... |
token-classification | transformers |
# Catalan BERTa (RoBERTa-base) finetuned for Named Entity Recognition.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to Use](#how-to-use)
- [Training](#training)
- [Training data](... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "named entity recognition", "ner", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/ancora-ca-ner"], "metrics": ["f1"], "widget": [{"text": "Em dic Llu\u00efsa i visc a Santa Maria del Cam\u00ed."}, {"text": "L'Aina, la Berta i la Norma s... | projecte-aina/roberta-base-ca-cased-ner | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"catalan",
"named entity recognition",
"ner",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:projecte-aina/ancora-ca-ner",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compa... | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #token-classification #catalan #named entity recognition #ner #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/ancora-ca-ner #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa (RoBERTa-base) finetuned for Named Entity Recognition.
====================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to Use
* Training
+ Training data
+ Training procedure
* Evaluati... | [
"### Author\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL)",
"### Contact information\n\n\nFor further information, send an email to aina@URL",
"### Copyright\n\n\nCopyright (c) 2021 Text Mining Unit at Barcelona Supercomputing Center",
"### Licensing Information\n\n\nApach... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #catalan #named entity recognition #ner #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/ancora-ca-ner #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Author\n\n\nText Mining ... |
token-classification | transformers |
# Catalan BERTa (roberta-base-ca) finetuned for Part-of-speech-tagging (POS)
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Trai... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "part of speech tagging", "pos", "CaText", "Catalan Textual Corpus"], "datasets": ["universal_dependencies"], "metrics": ["f1"], "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": [{"text": "Em dic Llu\u00efsa i visc a Santa Maria ... | projecte-aina/roberta-base-ca-cased-pos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"catalan",
"part of speech tagging",
"pos",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:universal_dependencies",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",... | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #token-classification #catalan #part of speech tagging #pos #CaText #Catalan Textual Corpus #ca #dataset-universal_dependencies #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Catalan BERTa (roberta-base-ca) finetuned for Part-of-speech-tagging (POS)
==========================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training da... | [
"### Training data\n\n\nWe used the POS dataset in Catalan from the Universal Dependencies Treebank we refer to *Ancora-ca-pos* for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint u... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #catalan #part of speech tagging #pos #CaText #Catalan Textual Corpus #ca #dataset-universal_dependencies #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training data\n\n\nW... |
question-answering | transformers |
# Catalan BERTa (roberta-base-ca) finetuned for Question Answering.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#tr... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "qa"], "datasets": ["xquad-ca", "viquiquad"], "metrics": ["f1", "exact match"], "widget": [{"text": "Quan va comen\u00e7ar el Super3?", "context": "El Super3 o Club Super3 \u00e9s un univers infantil catal\u00e0 creat a partir d'un programa em\u00e8s per... | projecte-aina/roberta-base-ca-cased-qa | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"catalan",
"qa",
"ca",
"dataset:xquad-ca",
"dataset:viquiquad",
"arxiv:1907.11692",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #question-answering #catalan #qa #ca #dataset-xquad-ca #dataset-viquiquad #arxiv-1907.11692 #license-apache-2.0 #endpoints_compatible #region-us
| Catalan BERTa (roberta-base-ca) finetuned for Question Answering.
=================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training data
+ Training pro... | [
"### Training data\n\n\nWe used the QA dataset in Catalan called CatalanQA for training and evaluation, and the XQuAD-ca test set for evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the do... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #catalan #qa #ca #dataset-xquad-ca #dataset-viquiquad #arxiv-1907.11692 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe used the QA dataset in Catalan called CatalanQA for training and evaluation, and the XQuAD-ca test... |
text-classification | transformers |
# Catalan BERTa (roberta-base-ca) finetuned for Semantic Textual Similarity.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Trai... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "semantic textual similarity", "sts-ca", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/sts-ca"], "metrics": ["combined_score"], "pipeline_tag": "text-classification", "model-index": [{"name": "roberta-base-ca-cased-sts", "results": [{"... | projecte-aina/roberta-base-ca-cased-sts | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"catalan",
"semantic textual similarity",
"sts-ca",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:projecte-aina/sts-ca",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compati... | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #text-classification #catalan #semantic textual similarity #sts-ca #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/sts-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa (roberta-base-ca) finetuned for Semantic Textual Similarity.
==========================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training da... | [
"### Training data\n\n\nWe used the STS dataset in Catalan called STS-ca for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding de... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #catalan #semantic textual similarity #sts-ca #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/sts-ca #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe used... |
text-classification | transformers |
# Catalan BERTa (roberta-base-ca) finetuned for Text Classification.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#t... | {"language": ["ca"], "tags": ["catalan", "text classification", "tecla", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/tecla"], "metrics": ["accuracy"], "widget": [{"text": "Els Pets presenten el seu nou treball al Palau Sant Jordi."}, {"text": "Els barcelonins incrementen un 23% l\u2019\u00fas del c... | projecte-aina/roberta-base-ca-cased-tc | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"catalan",
"text classification",
"tecla",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:projecte-aina/tecla",
"arxiv:1907.11692",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #text-classification #catalan #text classification #tecla #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/tecla #arxiv-1907.11692 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa (roberta-base-ca) finetuned for Text Classification.
==================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training data
+ Training p... | [
"### Training data\n\n\nWe used the TC dataset in Catalan called TeCla for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding deve... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #catalan #text classification #tecla #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/tecla #arxiv-1907.11692 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe used the TC dataset in Catalan cal... |
text-classification | transformers |
# Catalan BERTa (roberta-base-ca) finetuned for Textual Entailment.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#tr... | {"language": ["ca"], "license": "apache-2.0", "tags": ["catalan", "teca", "CaText", "Catalan Textual Corpus"], "datasets": ["projecte-aina/teca"], "metrics": ["accuracy"], "widget": [{"text": "M'agrades. T'estimo."}, {"text": "M'agrada el sol i la calor. A la Garrotxa plou molt."}, {"text": "El llibre va caure per la f... | projecte-aina/roberta-base-ca-cased-te | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"catalan",
"teca",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:projecte-aina/teca",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #text-classification #catalan #teca #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/teca #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Catalan BERTa (roberta-base-ca) finetuned for Textual Entailment.
=================================================================
Table of Contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training data
+ Training pro... | [
"### Training data\n\n\nWe used the TE dataset in Catalan called TE-ca for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding deve... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #catalan #teca #CaText #Catalan Textual Corpus #ca #dataset-projecte-aina/teca #arxiv-1907.11692 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nWe used the TE dataset in Catalan called... |
null | keras | TODO
gpt-code uses the weights and tokenizer of https://huggingface.co/Sentdex/GPyT as a starting point for pretraining | {} | prophetikai/gpt-code | null | [
"keras",
"pytorch",
"tf",
"gpt2",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #pytorch #tf #gpt2 #region-us
| TODO
gpt-code uses the weights and tokenizer of URL as a starting point for pretraining | [] | [
"TAGS\n#keras #pytorch #tf #gpt2 #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-timit-demo-test_jong
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-test_jong", "results": []}]} | prows12/wav2vec2-base-timit-demo-test_jong | 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-test_jong
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# wav2vec2-base-timit-demo-test_jong\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-test_jong\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | ps2102/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 | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
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