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null | null | # This repo contains some useful scripts
## Confidence Scoring
Read https://x-lance.sjtu.edu.cn/papers/zhc00-chen-icassp17.pdf
Run `create_confidence_scores.py`
## Mass PR to update README
Run `update_model_card_mass_pr.py` . Make sure you use the following branch: https://github.com/huggingface/huggingface_hub/p... | {} | patrickvonplaten/codesnippets | null | [
"region:us"
] | null | 2022-03-22T17:47:07+00:00 | [] | [] | TAGS
#region-us
| # This repo contains some useful scripts
## Confidence Scoring
Read URL
Run 'create_confidence_scores.py'
## Mass PR to update README
Run 'update_model_card_mass_pr.py' . Make sure you use the following branch: URL
| [
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] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Prototypeu/bart-base-finetuned-tldrhq-cnn-dailymail
This model is a fine-tuned version of [Prototypeu/bart-base-finetuned-xsum](https:... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "Prototypeu/bart-base-finetuned-tldrhq-cnn-dailymail", "results": []}]} | Prototypeu/bart-base-finetuned-tldrhq-cnn-dailymail | null | [
"transformers",
"tf",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T18:11:10+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| Prototypeu/bart-base-finetuned-tldrhq-cnn-dailymail
===================================================
This model is a fine-tuned version of Prototypeu/bart-base-finetuned-xsum on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.5049
* Train Logits Loss: 1.5049
* Train R... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', '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. -->
# wav2vec2_common_voice_accents_us
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2_common_voice_accents_us", "results": []}]} | willcai/wav2vec2_common_voice_accents_us | null | [
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T18:14:42+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2\_common\_voice\_accents\_us
====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2722
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 32\n*... | [
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token-classification | transformers | # CES BERT sysform model
Fine-tuned BERT cased model | {} | blckwdw61/sysformver1 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T18:35:28+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # CES BERT sysform model
Fine-tuned BERT cased model | [
"# CES BERT sysform model\nFine-tuned BERT cased model"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# CES BERT sysform model\nFine-tuned BERT cased model"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xlsum-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xlsum datas... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["xlsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xlsum-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xlsum", "type"... | ahmeddbahaa/t5-small-finetuned-xlsum-en | null | [
"transformers",
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"tensorboard",
"t5",
"text2text-generation",
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"dataset:xlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-22T19:35:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xlsum-en
===========================
This model is a fine-tuned version of t5-small on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6629
* Rouge1: 23.7508
* Rouge2: 5.5427
* Rougel: 18.6777
* Rougelsum: 18.652
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were u... |
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_common_voice_accents_scotland
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2_common_voice_accents_scotland", "results": []}]} | willcai/wav2vec2_common_voice_accents_scotland | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-22T19:55:53+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2\_common\_voice\_accents\_scotland
==========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2752
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 32\n*... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
automatic-speech-recognition | espnet |
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue Mar 22 13:50:31 UTC 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `1991a25855821b8b61d775681aa0cdfd6161bbc8`... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["mediaspeech"]} | espnet/mediaspeech-fr-hubert | null | [
"espnet",
"tensorboard",
"audio",
"automatic-speech-recognition",
"dataset:mediaspeech",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-22T21:02:26+00:00 | [] | [
"noinfo"
] | TAGS
#espnet #tensorboard #audio #automatic-speech-recognition #dataset-mediaspeech #license-cc-by-4.0 #region-us
| RESULTS
=======
Environments
------------
* date: 'Tue Mar 22 13:50:31 UTC 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.10.1'
* Git hash: '1991a25855821b8b61d775681aa0cdfd6161bbc8'
+ Commit date: 'Mon Mar 21 22:19:19... | [
"### WER",
"### CER",
"### TER"
] | [
"TAGS\n#espnet #tensorboard #audio #automatic-speech-recognition #dataset-mediaspeech #license-cc-by-4.0 #region-us \n",
"### WER",
"### CER",
"### TER"
] |
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. -->
# codeparrot-ds-sample
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieve... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-sample", "results": []}]} | mimicheng/codeparrot-ds-sample | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-22T22:13:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| codeparrot-ds-sample
====================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6003
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
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question-answering | transformers |
# Graphcore/roberta-base-squad
BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Re... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "Graphcore/roberta-base-squad", "results": []}]} | Graphcore/roberta-base-squad | null | [
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"pytorch",
"optimum_graphcore",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"arxiv:1907.11692",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T00:03:07+00:00 | [
"1907.11692"
] | [] | TAGS
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|
# Graphcore/roberta-base-squad
BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Re... | [
"# Graphcore/roberta-base-squad\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabelled texts. It enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Enti... | [
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"# Graphcore/roberta-base-squad\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model... |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the ๐คSpaces ([documentation here... | {"tags": ["fastai"]} | ITESM/fastai_model | null | [
"fastai",
"region:us"
] | null | 2022-03-23T00:35:15+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #region-us \n",
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"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n... |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the ๐คSpaces ([documentation here... | {"tags": ["fastai"]} | espejelomar/fastai_model | null | [
"fastai",
"region:us"
] | null | 2022-03-23T00:37:01+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n... |
text-generation | transformers |
# DEMON_SLAYER DialoGPT Model v5 | {"tags": ["conversational"]} | duanxingjuan/DialoGPT-large-DEMON1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T00:59:38+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DEMON_SLAYER DialoGPT Model v5 | [
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] |
null | transformers |
This is the GPT2-Large-initialized prompt model used in the paper Fine-Grained Controllable Text Generation Using Non-Residual Prompting. It is loaded automatically in the GitHub repository below, if you want to try it out!
Paper: https://aclanthology.org/2022.acl-long.471
Official GitHub: https://github.com/FreddeF... | {"title": "README", "emoji": "\ud83d\ude3b", "colorFrom": "indigo", "colorTo": "purple", "sdk": "gradio", "pinned": false} | Non-Residual-Prompting/GPT2-Large | null | [
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T01:05:03+00:00 | [] | [] | TAGS
#transformers #tf #endpoints_compatible #region-us
|
This is the GPT2-Large-initialized prompt model used in the paper Fine-Grained Controllable Text Generation Using Non-Residual Prompting. It is loaded automatically in the GitHub repository below, if you want to try it out!
Paper: URL
Official GitHub: URL | [] | [
"TAGS\n#transformers #tf #endpoints_compatible #region-us \n"
] |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | aaraki/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T04:52:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9949
* Accuracy: 0.6958
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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. -->
# codeparrot-ds-sample
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieve... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-sample", "results": []}]} | Pavithra/codeparrot-ds-sample | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T05:12:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# codeparrot-ds-sample
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5219
- eval_runtime: 603.3856
- eval_samples_per_second: 154.402
- eval_steps_per_second: 4.826
- epoch: 0.15
- step: 10000
## Model description
More inf... | [
"# codeparrot-ds-sample\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.5219\n- eval_runtime: 603.3856\n- eval_samples_per_second: 154.402\n- eval_steps_per_second: 4.826\n- epoch: 0.15\n- step: 10000",
"## Model descri... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# codeparrot-ds-sample\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following resu... |
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. -->
# led-base-16384-100-MDS
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "led-base-16384-100-MDS", "results": []}]} | cammy/led-base-16384-100-MDS | null | [
"transformers",
"pytorch",
"tensorboard",
"led",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T05:32:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #led #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| led-base-16384-100-MDS
======================
This model is a fine-tuned version of allenai/led-base-16384 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.1425
* Rouge1: 16.7324
* Rouge2: 5.8501
* Rougel: 13.908
* Rougelsum: 13.8469
* Gen Len: 20.0
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #led #text2text-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: 5e-05\n* train\\_batch\\... |
feature-extraction | transformers |
# Model Card for UniXcoder-base
# Model Details
## Model Description
UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation.
- **Developed by:** Microsoft Team
- **Shared by [Optional]:** Hugging Face
- **Model type:** ... | {"language": ["en"], "license": "apache-2.0"} | microsoft/unixcoder-base | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"en",
"arxiv:2203.03850",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-23T05:47:38+00:00 | [
"2203.03850",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #en #arxiv-2203.03850 #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Model Card for UniXcoder-base
# Model Details
## Model Description
UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation.
- Developed by: Microsoft Team
- Shared by [Optional]: Hugging Face
- Model type: Feature Engi... | [
"# Model Card for UniXcoder-base",
"# Model Details",
"## Model Description\nUniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. \n \n- Developed by: Microsoft Team \n- Shared by [Optional]: Hugging Face\n- Model type: ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #en #arxiv-2203.03850 #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Model Card for UniXcoder-base",
"# Model Details",
"## Model Description\nUniXcoder is a unified cross-modal pre-trained model that leverag... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-gpt-model-3
This model is a fine-tuned version of [bigmorning/my-gpt-model](https://huggingface.co/bigmorning/my-gpt-model) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-gpt-model-3", "results": []}]} | bigmorning/my-gpt-model-3 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T05:52:35+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my-gpt-model-3
==============
This model is a fine-tuned version of bigmorning/my-gpt-model on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.1163
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeig... |
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": []}]} | pinot/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 | 2022-03-23T05:58:32+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: 0.4548
* Wer: 0.3373
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 660519466
- CO2 Emissions (in grams): 35.865521343923916
## Validation Metrics
- Loss: 1.3210543394088745
- Rouge1: 52.1593
- Rouge2: 34.5464
- RougeL: 50.1141
- RougeLsum: 50.1067
- Gen Len: 11.93
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autonlp", "datasets": ["sumedh/autotrain-data-MeQSum-1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 35.865521343923916} | sumedh/autonlp-MeQSum-1-660519466 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"unk",
"dataset:sumedh/autotrain-data-MeQSum-1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T06:43:11+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-sumedh/autotrain-data-MeQSum-1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 660519466
- CO2 Emissions (in grams): 35.865521343923916
## Validation Metrics
- Loss: 1.3210543394088745
- Rouge1: 52.1593
- Rouge2: 34.5464
- RougeL: 50.1141
- RougeLsum: 50.1067
- Gen Len: 11.93
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 660519466\n- CO2 Emissions (in grams): 35.865521343923916",
"## Validation Metrics\n\n- Loss: 1.3210543394088745\n- Rouge1: 52.1593\n- Rouge2: 34.5464\n- RougeL: 50.1141\n- RougeLsum: 50.1067\n- Gen Len: 11.93",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-sumedh/autotrain-data-MeQSum-1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 660519466\n- CO2 Emissions (in grams): 35.86... |
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... | krishnayogik/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 | 2022-03-23T07:14:35+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: 0.2258
* Accuracy: 0.9245
* F1: 0.9248
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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | Gare/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T07:47:15+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2878
* Bleu: 28.0527
* Gen Len: 34.079
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
token-classification | transformers | TODO | {"license": "apache-2.0"} | Alvenir/bert-punct-restoration-de | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T07:59:01+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TODO | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | leixu/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T09:02:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2173
* Accuracy: 0.9255
* F1: 0.9255
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 661319476
- CO2 Emissions (in grams): 0.5712537632313806
## Validation Metrics
- Loss: 0.859619140625
- Accuracy: 0.8
- Macro F1: 0.6
- Micro F1: 0.8000000000000002
- Weighted F1: 0.72
- Macro Precision: 0.5555555555555555
- Micro ... | {"language": "en", "tags": "autonlp", "datasets": ["FuriouslyAsleep/autotrain-data-markingClassifier"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.5712537632313806} | FuriouslyAsleep/markingMultiClass | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:FuriouslyAsleep/autotrain-data-markingClassifier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T09:21:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-FuriouslyAsleep/autotrain-data-markingClassifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 661319476
- CO2 Emissions (in grams): 0.5712537632313806
## Validation Metrics
- Loss: 0.859619140625
- Accuracy: 0.8
- Macro F1: 0.6
- Micro F1: 0.8000000000000002
- Weighted F1: 0.72
- Macro Precision: 0.5555555555555555
- Micro ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 661319476\n- CO2 Emissions (in grams): 0.5712537632313806",
"## Validation Metrics\n\n- Loss: 0.859619140625\n- Accuracy: 0.8\n- Macro F1: 0.6\n- Micro F1: 0.8000000000000002\n- Weighted F1: 0.72\n- Macro Precision: 0.555555... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-FuriouslyAsleep/autotrain-data-markingClassifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 661319476\n-... |
text-to-image | generic | # Digit generation using DCGAN | {"library_name": "generic", "tags": ["text-to-image"]} | huggan/dcgan-mnist | null | [
"generic",
"pytorch",
"text-to-image",
"has_space",
"region:us"
] | null | 2022-03-23T09:24:40+00:00 | [] | [] | TAGS
#generic #pytorch #text-to-image #has_space #region-us
| # Digit generation using DCGAN | [
"# Digit generation using DCGAN"
] | [
"TAGS\n#generic #pytorch #text-to-image #has_space #region-us \n",
"# Digit generation using DCGAN"
] |
token-classification | transformers | # ๐ Keyphrase Extraction Model: distilbert-kptimes
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was firs... | {"language": "en", "license": "mit", "tags": ["keyphrase-extraction"], "datasets": ["midas/kptimes"], "metrics": ["seqeval"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content... | ml6team/keyphrase-extraction-distilbert-kptimes | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"keyphrase-extraction",
"en",
"dataset:midas/kptimes",
"arxiv:1911.12559",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-23T10:05:53+00:00 | [
"1911.12559"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #keyphrase-extraction #en #dataset-midas/kptimes #arxiv-1911.12559 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Keyphrase Extraction Model: distilbert-kptimes
==============================================
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading ... | [
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific and will perform very well on news articles from NY Times. It's not recommended to use this model for other domains, but you are free to test it out.\n* Limited amount of predicted keyphrases.\n* Only works for English documents.",
"#... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #keyphrase-extraction #en #dataset-midas/kptimes #arxiv-1911.12559 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific and wi... |
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. -->
# Graphcore/gpt2-wikitext-103
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-opt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikitext"], "model-index": [{"name": "clm_output", "results": []}]} | Graphcore/gpt2-wikitext-103 | null | [
"transformers",
"pytorch",
"safetensors",
"optimum_graphcore",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:wikitext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T10:06:52+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #optimum_graphcore #gpt2 #text-generation #generated_from_trainer #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Graphcore/gpt2-wikitext-103
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Grap... | [
"# Graphcore/gpt2-wikitext-103\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | [
"TAGS\n#transformers #pytorch #safetensors #optimum_graphcore #gpt2 #text-generation #generated_from_trainer #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Graphcore/gpt2-wikitext-103\n\nOptimum Graphcore is a new open-sou... |
null | null |
---
language:
- python 3.7
---
libraries:
- keras==2.0.2
- tensorflow==2.4.1 | {"license": "afl-3.0"} | Newt007/multi-class-attacks | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-03-23T10:28:31+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
|
---
language:
- python 3.7
---
libraries:
- keras==2.0.2
- tensorflow==2.4.1 | [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# aradia-ctc-v1
This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1](https://huggingface.co//l... | {"tags": ["automatic-speech-recognition", "abdusahmbzuai/arabic_speech_massive_300hrs", "generated_from_trainer"], "model-index": [{"name": "aradia-ctc-v1", "results": []}]} | abdusah/aradia-ctc-v1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_300hrs",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T10:58:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #abdusahmbzuai/arabic_speech_massive_300hrs #generated_from_trainer #endpoints_compatible #region-us
| aradia-ctc-v1
=============
This model is a fine-tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1 on the ABDUSAHMBZUAI/ARABIC\_SPEECH\_MASSIVE\_300HRS - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7171
* Wer: 0.3336
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #abdusahmbzuai/arabic_speech_massive_300hrs #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batc... |
null | null | # Text preprocessing
This tokenizer has been trained with tweets that have been preprocessed as follows:
1) User mentions (@user_name) have been replaced with the word *user*.
2) URLs have been replace with the word *url*.
3) WIP.
If you are going to use this tokenizer, we recommend you to preprocess your own datase... | {} | jcollado/english-tweet-tokenizer | null | [
"region:us"
] | null | 2022-03-23T12:10:39+00:00 | [] | [] | TAGS
#region-us
| # Text preprocessing
This tokenizer has been trained with tweets that have been preprocessed as follows:
1) User mentions (@user_name) have been replaced with the word *user*.
2) URLs have been replace with the word *url*.
3) WIP.
If you are going to use this tokenizer, we recommend you to preprocess your own datase... | [
"# Text preprocessing\n\nThis tokenizer has been trained with tweets that have been preprocessed as follows:\n\n1) User mentions (@user_name) have been replaced with the word *user*.\n2) URLs have been replace with the word *url*.\n3) WIP.\n\nIf you are going to use this tokenizer, we recommend you to preprocess yo... | [
"TAGS\n#region-us \n",
"# Text preprocessing\n\nThis tokenizer has been trained with tweets that have been preprocessed as follows:\n\n1) User mentions (@user_name) have been replaced with the word *user*.\n2) URLs have been replace with the word *url*.\n3) WIP.\n\nIf you are going to use this tokenizer, we recom... |
question-answering | transformers |
# Graphcore/roberta-base-squad2
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gra... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "roberta-base-squad2", "results": []}]} | Graphcore/roberta-base-squad2 | null | [
"transformers",
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"generated_from_trainer",
"dataset:squad_v2",
"arxiv:1907.11692",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T12:32:51+00:00 | [
"1907.11692"
] | [] | TAGS
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|
# Graphcore/roberta-base-squad2
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gra... | [
"# Graphcore/roberta-base-squad2\n\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #arxiv-1907.11692 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Graphcore/roberta-base-squad2\n\n\nOptimum Graphcore is a new open-source library and toolkit that enables develope... |
null | transformers |
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaub... | {"language": "fr", "license": "mit", "tags": ["bert", "language-model", "flaubert", "french", "flaubert-base", "uncased", "asr", "speech", "oral", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding"]} | nherve/flaubert-oral-ft | null | [
"transformers",
"pytorch",
"bert",
"language-model",
"flaubert",
"french",
"flaubert-base",
"uncased",
"asr",
"speech",
"oral",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"fr",
"license:mit",
"endpoints_compatible",
"region... | null | 2022-03-23T12:33:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #bert #language-model #flaubert #french #flaubert-base #uncased #asr #speech #oral #natural language understanding #NLU #spoken language understanding #SLU #understanding #fr #license-mit #endpoints_compatible #region-us
|
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
FlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the FlauBERT software using the same parameters as the flaubert... | [
"# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling\r\n\r\nFlauBERT-Oral are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the FlauBERT software using the same parameters as the f... | [
"TAGS\n#transformers #pytorch #bert #language-model #flaubert #french #flaubert-base #uncased #asr #speech #oral #natural language understanding #NLU #spoken language understanding #SLU #understanding #fr #license-mit #endpoints_compatible #region-us \n",
"# FlauBERT-Oral models: Using ASR-Generated Text for Spok... |
image-classification | null |
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycl).
Disclaimer: The team releasing RegNet did not write a model card for this model so this ... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}]} | zuppif/dummy | null | [
"vision",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"license:apache-2.0",
"region:us"
] | null | 2022-03-23T12:39:10+00:00 | [
"2003.13678"
] | [] | TAGS
#vision #image-classification #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #region-us
|
# RegNet
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.
Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The... | [
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model desc... | [
"TAGS\n#vision #image-classification #dataset-imagenet-1k #arxiv-2003.13678 #license-apache-2.0 #region-us \n",
"# RegNet\n\nRegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. \n\nDisclaimer: The team releasing RegNet did not ... |
text2text-generation | transformers | ## Model description
โT5 Model for generating paraphrases of english sentences. Trained on the [Quora Paraphrase dataset](https://www.kaggle.com/c/quora-question-pairs).
## Online demo website
Click [https://huggingface.co/spaces/Deep1994/t5-paraphrase](https://huggingface.co/spaces/Deep1994/t5-paraphrase) to have... | {"license": "afl-3.0"} | Deep1994/t5-paraphrase-quora | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T12:51:27+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## Model description
โT5 Model for generating paraphrases of english sentences. Trained on the Quora Paraphrase dataset.
## Online demo website
Click URL to have a try online.
## How to use
For more reference on training your own T5 model, do check out t5-paraphrase-generation. | [
"## Model description\r\nโT5 Model for generating paraphrases of english sentences. Trained on the Quora Paraphrase dataset.",
"## Online demo website\r\nClick URL to have a try online.",
"## How to use\r\n\r\n\r\n\r\nFor more reference on training your own T5 model, do check out t5-paraphrase-generation."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Model description\r\nโT5 Model for generating paraphrases of english sentences. Trained on the Quora Paraphrase dataset.",
"## Online dem... |
text2text-generation | transformers | This is a t5-base model (init from pretrained weights) and finetuned on WikiKG90Mv2 dataset. Please see https://github.com/apoorvumang/kgt5/ for more details on the method.
This model was trained on the tail entity prediction task ie. given subject entity and relation, predict the object entity. Input should be pro... | {"license": "mit", "widget": [{"text": "Apoorv Umang Saxena| family name", "example_title": "Family name prediction"}, {"text": "Apoorv Saxena| country", "example_title": "Country prediction"}, {"text": "World War 2| followed by", "example_title": "followed by"}]} | apoorvumang/kgt5-base-wikikg90mv2 | null | [
"transformers",
"pytorch",
"tf",
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"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T13:16:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This is a t5-base model (init from pretrained weights) and finetuned on WikiKG90Mv2 dataset. Please see URL for more details on the method.
This model was trained on the tail entity prediction task ie. given subject entity and relation, predict the object entity. Input should be provided in the form of "\<entity te... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | null | # Text preprocessing
This tokenizer has been trained with tweets that have been preprocessed as follows:
1) User mentions (@user_name) have been replaced with the word *user*.
2) URLs have been replace with the word *url*.
3) WIP.
If you are going to use this tokenizer, we recommend you to preprocess your own dataset... | {} | jcollado/spanish-tweet-tokenizer | null | [
"region:us"
] | null | 2022-03-23T13:24:57+00:00 | [] | [] | TAGS
#region-us
| # Text preprocessing
This tokenizer has been trained with tweets that have been preprocessed as follows:
1) User mentions (@user_name) have been replaced with the word *user*.
2) URLs have been replace with the word *url*.
3) WIP.
If you are going to use this tokenizer, we recommend you to preprocess your own dataset... | [
"# Text preprocessing\n\nThis tokenizer has been trained with tweets that have been preprocessed as follows:\n\n1) User mentions (@user_name) have been replaced with the word *user*.\n2) URLs have been replace with the word *url*.\n3) WIP.\nIf you are going to use this tokenizer, we recommend you to preprocess your... | [
"TAGS\n#region-us \n",
"# Text preprocessing\n\nThis tokenizer has been trained with tweets that have been preprocessed as follows:\n\n1) User mentions (@user_name) have been replaced with the word *user*.\n2) URLs have been replace with the word *url*.\n3) WIP.\nIf you are going to use this tokenizer, we recomme... |
text2text-generation | transformers |
**Context**
Most of the business name generator systems based on Rule based approach and only take as input a name or keyword not context. The present trained model its aim is to take in a summary for a business idea (1-2 sentences, could be even keywords) and generate a viable business name for users.
**Introductio... | {"tags": ["Text2Text Generation", "Business names", "Recommendation system"], "datasets": ["BSD-1"], "metrics": ["Rouge"]} | abdelhalim/Rec_Business_Names | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Text2Text Generation",
"Business names",
"Recommendation system",
"dataset:BSD-1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T13:25:14+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #Text2Text Generation #Business names #Recommendation system #dataset-BSD-1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Context
Most of the business name generator systems based on Rule based approach and only take as input a name or keyword not context. The present trained model its aim is to take in a summary for a business idea (1-2 sentences, could be even keywords) and generate a viable business name for users.
Introduction
The... | [
"# Usage\nIn order to use the model in your Python script just copy the following code:"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #Text2Text Generation #Business names #Recommendation system #dataset-BSD-1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Usage\nIn order to use the model in your Python script just copy the following code:"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Zarkit/classificationEsp2
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Zarkit/classificationEsp2", "results": []}]} | Zarkit/classificationEsp2 | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T14:22:12+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Zarkit/classificationEsp2
=========================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1649
* Validation Loss: 0.7498
* Epoch: 2
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 8979, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
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. -->
# bart-med-term-conditional-masking
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bar... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-med-term-conditional-masking", "results": []}]} | gayanin/bart-med-term-conditional-masking | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T14:24:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-med-term-conditional-masking
=================================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5115
* Rouge2 Precision: 0.7409
* Rouge2 Recall: 0.5343
* Rouge2 Fmeasure: 0.6025
Model description... | [
"### 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: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-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\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/mt5-small-finetuned-amazon-en-es", "results": []}]} | Rocketknight1/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T14:34:02+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Rocketknight1/mt5-small-finetuned-amazon-en-es
==============================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 10.2613
* Validation Loss: 4.5342
* Epoch: 0
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 9672, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
automatic-speech-recognition | transformers |
# Fine-Tune Wav2Vec2 large model for English ASR
### Data for fine-tune
| Dataset | Duration in hours |
|--------------|-------------------|
| Common Voice | 1667 |
| Europarl | 85 |
| How2 | 356 |
| Librispeech | 936 |
| MuST-C v... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["common_voice", "librispeech_asr", "how2", "must-c-v1", "must-c-v2", "europarl", "tedlium"]} | nguyenvulebinh/iwslt-asr-wav2vec-large-4500h | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
"audio",
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"dataset:librispeech_asr",
"dataset:how2",
"dataset:must-c-v1",
"dataset:must-c-v2",
"dataset:europarl",
"dataset:tedlium",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"reg... | null | 2022-03-23T14:53:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-common_voice #dataset-librispeech_asr #dataset-how2 #dataset-must-c-v1 #dataset-must-c-v2 #dataset-europarl #dataset-tedlium #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| Fine-Tune Wav2Vec2 large model for English ASR
==============================================
### Data for fine-tune
### Evaluation result
### Usage
![Open In Colab](URL
### Model Parameters License
The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Co... | [
"### Data for fine-tune",
"### Evaluation result",
"### Usage\n\n\n![Open In Colab](URL",
"### Model Parameters License\n\n\nThe ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-common_voice #dataset-librispeech_asr #dataset-how2 #dataset-must-c-v1 #dataset-must-c-v2 #dataset-europarl #dataset-tedlium #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"### Data for fine-tune",
"### Evalua... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Horovod_Tweet_Sentiment_10k_2eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Horovod_Tweet_Sentiment_10k_2eps", "results": []}]} | joe5campbell/Horovod_Tweet_Sentiment_10k_2eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T15:07:55+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Horovod\_Tweet\_Sentiment\_10k\_2eps
====================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.701302
* Train Accuracy: 0.49375
* Validation Loss: 0.69441336
* Validation Accuracy: 0.51... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 0.0003, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results"... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# classificationEsp1_Attraction
This model was trained from scratch on an unknown dataset.
It achieves the following results on the eval... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "classificationEsp1_Attraction", "results": []}]} | javilonso/classificationEsp1_Attraction | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T15:27:21+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# classificationEsp1_Attraction
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## T... | [
"# classificationEsp1_Attraction\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore in... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# classificationEsp1_Attraction\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model d... |
audio-classification | transformers |
# Wav2vec 2.0 XLS-R For Spontaneous Speech Emotion Recognition
This is the model that got first place in the SER track of the Automatic Speech Recognition for spontaneous and prepared speech & Speech Emotion Recognition in Portuguese (SE&R 2022) Workshop.
The following datasets were used in the training:
- [CORAA S... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "italian-speech-corpus", "english-speech-corpus", "arabic-speech-corpus", "spontaneous", "speech", "PyTorch"], "datasets": ["coraa_ser", "emovo", "ravdess", "baved"], "metrics": ["f1"], "model_index": {... | alefiury/wav2vec2-xls-r-300m-pt-br-spontaneous-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"italian-speech-corpus",
"english-speech-corpus",
"arabic-speech-corpus",
"spontaneous",
"PyTorch",
"dataset:coraa_ser",
"dataset:emovo",
"dataset:ravdess",
"dataset:ba... | null | 2022-03-23T15:29:36+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #audio #speech #pt #portuguese-speech-corpus #italian-speech-corpus #english-speech-corpus #arabic-speech-corpus #spontaneous #PyTorch #dataset-coraa_ser #dataset-emovo #dataset-ravdess #dataset-baved #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2vec 2.0 XLS-R For Spontaneous Speech Emotion Recognition
This is the model that got first place in the SER track of the Automatic Speech Recognition for spontaneous and prepared speech & Speech Emotion Recognition in Portuguese (SE&R 2022) Workshop.
The following datasets were used in the training:
- CORAA SE... | [
"# Wav2vec 2.0 XLS-R For Spontaneous Speech Emotion Recognition\n\nThis is the model that got first place in the SER track of the Automatic Speech Recognition for spontaneous and prepared speech & Speech Emotion Recognition in Portuguese (SE&R 2022) Workshop.\n\nThe following datasets were used in the training:\n\n... | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #audio #speech #pt #portuguese-speech-corpus #italian-speech-corpus #english-speech-corpus #arabic-speech-corpus #spontaneous #PyTorch #dataset-coraa_ser #dataset-emovo #dataset-ravdess #dataset-baved #license-apache-2.0 #endpoints_compatible #region-us ... |
text2text-generation | transformers | This is a control model. Converted directly from the original TF dataset format.
````
gsutil cp -R gs://t5-data/pretrained_models/small/ .
wget https://huggingface.co/t5-small/raw/main/config.json
python3 convert_t5_original_tf_checkpoint_to_pytorch.py --tf_checkpoint_path "dump/small/" --config_file "config.json" -... | {} | pere/test-t5-small-direct | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T15:42:00+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a control model. Converted directly from the original TF dataset format.
| [] | [
"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 Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves t... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Roberta", "results": []}]} | Mr-Wick/Roberta | null | [
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T16:08:46+00:00 | [] | [] | TAGS
#transformers #tf #roberta #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
|
# Roberta
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training... | [
"# Roberta\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informati... | [
"TAGS\n#transformers #tf #roberta #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"# Roberta\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMor... |
null | pytorch |
Ce modรจle est dรฉveloppรฉ pour KARA.
Ce modรจle est :
- Un outil de classification thรฉmatique des commentaires RH
- Entrainรฉ pour รชtre utilisรฉ en ANGLAIS (les commentaires doivent รชtres traduits)
- Spรฉcialisรฉ pour des commentaires entre 10 et 512 charactรจres
Ce modรจle n'est pas :
- Utilisable pour dรฉtecter u... | {"language": ["en"], "library_name": "pytorch", "tags": ["sentiment-analysis"], "metrics": ["satisfaction", "culture organisationnelle", "leadership", "conditions de travail"], "widget": [{"text": "My work is recognized by my superiors and I would even say that I feel like I have more recognition since we are on telewo... | VincentC12/rh_classification_kara | null | [
"pytorch",
"distilbert",
"sentiment-analysis",
"en",
"region:us"
] | null | 2022-03-23T16:19:02+00:00 | [] | [
"en"
] | TAGS
#pytorch #distilbert #sentiment-analysis #en #region-us
|
Ce modรจle est dรฉveloppรฉ pour KARA.
Ce modรจle est :
- Un outil de classification thรฉmatique des commentaires RH
- Entrainรฉ pour รชtre utilisรฉ en ANGLAIS (les commentaires doivent รชtres traduits)
- Spรฉcialisรฉ pour des commentaires entre 10 et 512 charactรจres
Ce modรจle n'est pas :
- Utilisable pour dรฉtecter u... | [] | [
"TAGS\n#pytorch #distilbert #sentiment-analysis #en #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/temp-colab-upload-test
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/temp-colab-upload-test", "results": []}]} | Rocketknight1/temp-colab-upload-test | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T16:28:11+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/temp-colab-upload-test
====================================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5386
* Validation Loss: 0.0000
* Epoch: 0
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate':... |
text-generation | transformers |
# Graphcore/gpt2-medium-wikitext-103
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikitext"], "model-index": [{"name": "clm_output_medium", "results": []}]} | Graphcore/gpt2-medium-wikitext-103 | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:wikitext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T16:30:12+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #gpt2 #text-generation #generated_from_trainer #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Graphcore/gpt2-medium-wikitext-103
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models o... | [
"# Graphcore/gpt2-medium-wikitext-103\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run mod... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #gpt2 #text-generation #generated_from_trainer #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Graphcore/gpt2-medium-wikitext-103\n\nOptimum Graphcore is a new open-source library and t... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1479780096483512323/LmKF... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pierreavdb/1648054135143/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pierreavdb | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T16:43:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Pierre
@pierreavdb
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Horovod_Tweet_Sentiment_100k_2eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Horovod_Tweet_Sentiment_100k_2eps", "results": []}]} | joe5campbell/Horovod_Tweet_Sentiment_100k_2eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T16:49:20+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Horovod\_Tweet\_Sentiment\_100k\_2eps
=====================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.35511288
* Train Accuracy: 0.8470289
* Validation Loss: 0.42278787
* Validation Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 0.0003, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results"... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/temp-colab-upload-test2
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/temp-colab-upload-test2", "results": []}]} | Rocketknight1/temp-colab-upload-test2 | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T17:02:59+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/temp-colab-upload-test2
=====================================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6931
* Validation Loss: 0.6931
* Epoch: 1
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate':... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500999718331199496/yhpq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/stedmanhalliday | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T17:16:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
SODI
@stedmanhalliday
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-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-hotel-reviews
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-hotel-reviews", "results": []}]} | Zohar/distilgpt2-finetuned-hotel-reviews | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T17:17:12+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-hotel-reviews
==================================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6253
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #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: 2e-05\n* train... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1493720826935398408/hB4n... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/metakuna/1648057688512/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/metakuna | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T17:35:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
metakuna (8/100 blog posts)
@metakuna
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | # Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/gbert-hasoc-german-2019")
model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/gbert-hasoc-german-2019")
```
# Dataset
```bibtext
@in... | {"language": "de", "tags": ["hate-speech-classification"], "widget": [{"text": "Das ist der absolute Gipfel! L\u00e4cherliche 2,5 Jahre Haft f\u00fcr einen extremst sadistischen Mord. Ich fasse es nicht. Das sitzt der Killer auf der linken Arschbacke ab und lacht sich dabei kaputt. Unsere Justiz ist nur noch zum K... | shahrukhx01/gbert-hasoc-german-2019 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"hate-speech-classification",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-23T17:41:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bert #text-classification #hate-speech-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Usage
# Dataset
---
license: mit
---
| [
"# Usage",
"# Dataset\r\n\r\n\r\n\r\n---\r\nlicense: mit\r\n---"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #hate-speech-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Usage",
"# Dataset\r\n\r\n\r\n\r\n---\r\nlicense: mit\r\n---"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-base-multilingual-cased-squad", "results": []}]} | muhammedshihebi/bert-base-multilingual-cased-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T17:48:32+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-multilingual-cased-squad
==================================
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5271
* Epoch: 2
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 18600, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1385231541278855171/lgH-... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rickyflows/1648058984275/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rickyflows | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T17:53:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
โ ricky flowstate โ
@rickyflows
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | # Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/gbert-germeval-2021")
model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/gbert-germeval-2021")
```
# Dataset
```bibtext
@proceeding... | {"language": "de", "tags": ["hate-speech-classification"], "widget": [{"text": "Als jemand, der im real existierenden Sozialismus aufgewachsen ist, kann ich \u00fcber George Weineberg nur sagen, dass er ein Voll...t ist. Finde es schon gut, dass der eingeladen wurde. Hat gezeigt, dass er viel Meinung hat, aber offensic... | shahrukhx01/gbert-germeval-2021 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"hate-speech-classification",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-23T18:02:39+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #hate-speech-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Usage
# Dataset
---
license: mit
---
| [
"# Usage",
"# Dataset\r\n\r\n\r\n\r\n---\r\nlicense: mit\r\n---"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #hate-speech-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Usage",
"# Dataset\r\n\r\n\r\n\r\n---\r\nlicense: mit\r\n---"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1475818681628246021/sf4z... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/lucca_dev/1648059357338/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/lucca_dev | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T18:07:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Lucca
@lucca\_dev
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers | # opus-mt-tc-base-uk-ces_slk
Neural machine translation model for translating from Ukrainian (uk) to Czech and Slovak (cs+sk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in... | {"language": ["cs", "sk", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-uk-ces_slk", "results": [{"task": {"type": "translation", "name": "Translation ukr-ces"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ukr ces devtest"}, "... | Helsinki-NLP/opus-mt-tc-base-uk-ces_slk | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"cs",
"sk",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-23T18:14:15+00:00 | [] | [
"cs",
"sk",
"uk"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #cs #sk #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-uk-ces\_slk
===========================
Neural machine translation model for translating from Ukrainian (uk) to Czech and Slovak (cs+sk).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. Al... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #cs #sk #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1434246328788398081/M7Ht... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/mattiasinspace | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T18:30:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Mattias in Deep
@mattiasinspace
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-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_ONION_prefinetune_4.0
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It ach... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2_ONION_prefinetune_4.0", "results": []}]} | ScandinavianMrT/gpt2_ONION_prefinetune_4.0 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T18:34:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2\_ONION\_prefinetune\_4.0
=============================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6484
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: 4",
"### Training... | [
"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 |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/615582548010229761/0zg9a... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/eigenrobot-moridinamael/1648060937936/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/eigenrobot-moridinamael | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T18:37:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Twisted Mentat Matt & eigenrobot
@eigenrobot-moridinamael
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d6d96651b423fa5a83c38ee2a4c6c93... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/kendrick-lamar"], "widget": [{"text": "I am"}]} | huggingartists/kendrick-lamar | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/kendrick-lamar",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T18:37:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/kendrick-lamar #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from Kendrick Lamar.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/kendrick-lamar #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B ... |
translation | transformers | # opus-mt-tc-base-uk-hu
Neural machine translation model for translating from Ukrainian (uk) to Hungarian (hu).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All... | {"language": ["hu", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-uk-hu", "results": [{"task": {"type": "translation", "name": "Translation ukr-hun"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ukr hun devtest"}, "metrics": [... | Helsinki-NLP/opus-mt-tc-base-uk-hu | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"hu",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-23T18:44:44+00:00 | [] | [
"hu",
"uk"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #hu #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-base-uk-hu
=====================
Neural machine translation model for translating from Ukrainian (uk) to Hungarian (hu).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originall... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #hu #uk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# uncased_L-12_H-128_A-2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the follow... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "uncased_L-12_H-128_A-2", "results": []}]} | negfir/uncased_L-12_H-128_A-2 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T18:49:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #pretraining #generated_from_keras_callback #endpoints_compatible #region-us
|
# uncased_L-12_H-128_A-2
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
##... | [
"# uncased_L-12_H-128_A-2\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore ... | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# uncased_L-12_H-128_A-2\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore inf... |
fill-mask | transformers | Nystromformer for sequence length 1024 trained on WikiText-103 v1 for 150 epochs. | {} | uw-madison/nystromformer-1024 | null | [
"transformers",
"pytorch",
"nystromformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T18:56:40+00:00 | [] | [] | TAGS
#transformers #pytorch #nystromformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Nystromformer for sequence length 1024 trained on WikiText-103 v1 for 150 epochs. | [] | [
"TAGS\n#transformers #pytorch #nystromformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Zarkit/bert-base-multilingual-uncased-sentiment1
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentime... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Zarkit/bert-base-multilingual-uncased-sentiment1", "results": []}]} | Zarkit/bert-base-multilingual-uncased-sentiment1 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T18:58:36+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Zarkit/bert-base-multilingual-uncased-sentiment1
================================================
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.4891
* Validation Loss: 0.5448
* Ep... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7980, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1502292592914046984/F1N4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/interrogami/1648064415193/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/interrogami | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T19:19:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
interrobang
@interrogami
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1424813722011410434/73S-... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ryiacy/1648065062687/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/ryiacy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T19:28:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
cyriac
@ryiacy
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
Th... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | spacy | English pipeline for part-of-speech and rhetorical tagging.
| Feature | Description |
| --- | --- |
| **Name** | `en_docusco_spacy` |
| **Version** | `1.3` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `ner` |
| **Components** | `tok2vec`, `tagger`, `ner` |
| **Vectors** | 0 keys, 0 ... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | browndw/en_docusco_spacy | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-23T19:48:02+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #has_space #region-us
| English pipeline for part-of-speech and rhetorical tagging.
### Label Scheme
View label scheme (308 labels for 2 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (308 labels for 2 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #has_space #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (308 labels for 2 components)",
"### Accuracy"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-gpt-model-4
This model is a fine-tuned version of [bigmorning/my-gpt-model-3](https://huggingface.co/bigmorning/my-gpt-model-3) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-gpt-model-4", "results": []}]} | bigmorning/my-gpt-model-4 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T19:52:49+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my-gpt-model-4
==============
This model is a fine-tuned version of bigmorning/my-gpt-model-3 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.0556
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeig... |
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-med-term-conditional-masking
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-med-term-conditional-masking", "results": []}]} | gayanin/t5-small-med-term-conditional-masking | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T20:16:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-med-term-conditional-masking
=====================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6808
* Rouge2 Precision: 0.6855
* Rouge2 Recall: 0.486
* Rouge2 Fmeasure: 0.5507
Model description
--... | [
"### 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: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-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... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1477531697814011904/6OQ-... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/thanksthoth | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T20:22:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rod ()
@thanksthoth
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362404255798280192/yIKM... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/radagasttbrown/1648071147429/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/radagasttbrown | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T21:13:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Radagast
@radagasttbrown
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1363260889164623877/vz-U... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/coscorrodrift/1648073956402/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/coscorrodrift | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T21:14:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
coscorrodrift
@coscorrodrift
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | <!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Mar 23 05:58:21 UTC 2022`
- python version: `3.9.10 | packaged by conda-forge | (main, Feb 1 2022, 21:24:11) [GCC 9.4.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `1991a25855821b8b6... | {} | espnet/marathi_openslr64_wav2vec2_asrconformer5 | null | [
"tensorboard",
"region:us"
] | null | 2022-03-23T21:14:55+00:00 | [] | [] | TAGS
#tensorboard #region-us
| RESULTS
=======
Environments
------------
* date: 'Wed Mar 23 05:58:21 UTC 2022'
* python version: '3.9.10 | packaged by conda-forge | (main, Feb 1 2022, 21:24:11) [GCC 9.4.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.10.1'
* Git hash: '1991a25855821b8b61d775681aa0cdfd6161bbc8'
+ Commit da... | [
"### WER",
"### CER",
"### TER"
] | [
"TAGS\n#tensorboard #region-us \n",
"### WER",
"### CER",
"### TER"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-gpt-model-5
This model is a fine-tuned version of [bigmorning/my-gpt-model-3](https://huggingface.co/bigmorning/my-gpt-model-3) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my-gpt-model-5", "results": []}]} | bigmorning/my-gpt-model-5 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T22:04:49+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my-gpt-model-5
==============
This model is a fine-tuned version of bigmorning/my-gpt-model-3 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.9979
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeig... |
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. -->
# codet5-base
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on the... | {"license": "apache-2.0", "tags": ["dis2py", "generated_from_trainer"], "model-index": [{"name": "codet5-base", "results": []}]} | simonnedved/codet5-base | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dis2py",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-23T22:11:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #dis2py #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# codet5-base
This model is a fine-tuned version of Salesforce/codet5-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
### Training hyperparameters
The ... | [
"# codet5-base\n\nThis model is a fine-tuned version of Salesforce/codet5-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",
"## Training procedure",
"### ... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #dis2py #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# codet5-base\n\nThis model is a fine-tuned version of Salesforce/codet5-base on the None dataset.",
"#... |
token-classification | transformers |
# roberta-large-ner-english: model fine-tuned from roberta-large for NER task
## Introduction
[roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset.
Model was validated on emails/chat data and outperformed other models on this type of data specifically.
In ... | {"language": "en", "datasets": ["conll2003"], "widget": [{"text": "My name is jean-baptiste and I live in montreal"}, {"text": "My name is clara and I live in berkeley, california."}, {"text": "My name is wolfgang and I live in berlin"}]} | ydshieh/roberta-large-ner-english | null | [
"transformers",
"tf",
"roberta",
"token-classification",
"en",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T22:13:16+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #roberta #token-classification #en #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-ner-english: model fine-tuned from roberta-large for NER task
===========================================================================
Introduction
------------
[roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset.
Model was validated on em... | [
"##### Load camembert-ner and its sub-word tokenizer :\n\n\nModel performances\n------------------\n\n\nModel performances computed on conll2003 validation dataset (computed on the tokens predictions)\n\n\n\nOn private dataset (email, chat, informal discussion), computed on word predictions:\n\n\n\nBy comparison on... | [
"TAGS\n#transformers #tf #roberta #token-classification #en #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us \n",
"##### Load camembert-ner and its sub-word tokenizer :\n\n\nModel performances\n------------------\n\n\nModel performances computed on conll2003 validation dataset (computed o... |
null | null | # DualStyleGAN
- https://arxiv.org/abs/2203.13248
- https://github.com/williamyang1991/DualStyleGAN
- weights
- https://drive.google.com/drive/folders/1GZQ6Gs5AzJq9lUL-ldIQexi0JYPKNy8b
| {} | public-data/DualStyleGAN | null | [
"arxiv:2203.13248",
"has_space",
"region:us"
] | null | 2022-03-23T22:27:49+00:00 | [
"2203.13248"
] | [] | TAGS
#arxiv-2203.13248 #has_space #region-us
| # DualStyleGAN
- URL
- URL
- weights
- URL
| [
"# DualStyleGAN\n\n- URL\n- URL\n- weights\n - URL"
] | [
"TAGS\n#arxiv-2203.13248 #has_space #region-us \n",
"# DualStyleGAN\n\n- URL\n- URL\n- weights\n - URL"
] |
question-answering | transformers |
# roberta-base for QA
NOTE: This is version 2 of the model. See [this github issue](https://github.com/deepset-ai/FARM/issues/552) from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify `revision="v1.0"` when loading the model in Transformers 3.5. For exmaple:
```
mode... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"]} | ydshieh/roberta-base-squad2 | null | [
"transformers",
"tf",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T22:29:51+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# roberta-base for QA
NOTE: This is version 2 of the model. See this github issue from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify 'revision="v1.0"' when loading the model in Transformers 3.5. For exmaple:
## Overview
Language model: roberta-base
Language: En... | [
"# roberta-base for QA \n\nNOTE: This is version 2 of the model. See this github issue from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify 'revision=\"v1.0\"' when loading the model in Transformers 3.5. For exmaple:",
"## Overview\nLanguage model: roberta-base \... | [
"TAGS\n#transformers #tf #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# roberta-base for QA \n\nNOTE: This is version 2 of the model. See this github issue from the FARM repository for an explanation of why we updated. If you'd like to use version 1,... |
null | null | # dlib face landmark model
- http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
| {} | public-data/dlib_face_landmark_model | null | [
"has_space",
"region:us"
] | null | 2022-03-23T22:52:02+00:00 | [] | [] | TAGS
#has_space #region-us
| # dlib face landmark model
- URL
| [
"# dlib face landmark model\n\n- URL"
] | [
"TAGS\n#has_space #region-us \n",
"# dlib face landmark model\n\n- URL"
] |
automatic-speech-recognition | espnet |
## ESPnet2 model
### ``
This model was trained by Chaitanya Narisetty using recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/ms_indic_is18/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/chai_microsof... | {"language": "te", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["microsoft_indian_languages_interspeech2018"]} | espnet/chai_microsoft_indian_langs_te | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"te",
"dataset:microsoft_indian_languages_interspeech2018",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-23T23:36:26+00:00 | [
"1804.00015"
] | [
"te"
] | TAGS
#espnet #audio #automatic-speech-recognition #te #dataset-microsoft_indian_languages_interspeech2018 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 model
-------------
### ''
This model was trained by Chaitanya Narisetty using recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Mar 22 13:38:24 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]'
* espnet version: 'e... | [
"### ''\n\n\nThis model was trained by Chaitanya Narisetty using recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Mar 22 13:38:24 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]'\n* espnet version: 'espnet ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #te #dataset-microsoft_indian_languages_interspeech2018 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### ''\n\n\nThis model was trained by Chaitanya Narisetty using recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnviro... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/russian_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout fa1b865352475b744c37f70440de1cc6b257ba70
pip install -e .
cd egs2/commonvoice/asr1
... | {"language": "ru", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/russian_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"ru",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-23T23:59:42+00:00 | [
"1804.00015"
] | [
"ru"
] | TAGS
#espnet #audio #automatic-speech-recognition #ru #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/russian\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Mar 23 19:56:59 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, ... | [
"### 'espnet/russian\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Mar 23 19:56:59 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GC... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #ru #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/russian\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\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-model1-torgo
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-model1-torgo", "results": []}]} | modhp/wav2vec2-model1-torgo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T00:36:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-model1-torgo
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
### Training hyperparame... | [
"# wav2vec2-model1-torgo\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",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-model1-torgo\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore infor... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | radev/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T00:44:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-samsum
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\n... |
null | transformers |
# Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
This model card contains the AMOS model (**base++** version) proposed in [this paper](). The official GitHub repository can be found [here](https://github.com/microsoft/AMOS).
# Citation
If you find this model card useful for yo... | {"license": "mit"} | microsoft/amos | null | [
"transformers",
"pytorch",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T01:16:31+00:00 | [] | [] | TAGS
#transformers #pytorch #license-mit #endpoints_compatible #region-us
|
# Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
This model card contains the AMOS model (base++ version) proposed in [this paper](). The official GitHub repository can be found here.
If you find this model card useful for your research, please cite the following paper:
| [
"# Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators\r\n\r\nThis model card contains the AMOS model (base++ version) proposed in [this paper](). The official GitHub repository can be found here.\r\n\r\nIf you find this model card useful for your research, please cite the following pap... | [
"TAGS\n#transformers #pytorch #license-mit #endpoints_compatible #region-us \n",
"# Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators\r\n\r\nThis model card contains the AMOS model (base++ version) proposed in [this paper](). The official GitHub repository can be found here.\r\n\r\n... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506402743296020484/X79Y... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/btohtoh | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T01:35:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
BToh
@btohtoh
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506402743296020484/X79Y... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/btohtoh-willitbetoomuch/1648087519902/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/btohtoh-willitbetoomuch | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T01:50:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
BToh & unloading
@btohtoh-willitbetoomuch
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Tra... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# House DialoGPT Model | {"tags": ["conversational"]} | issue89/DialoGPT-small-house | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T02:16:32+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# House DialoGPT Model | [
"# House DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# House DialoGPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": []}]} | clisi2000/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T03:43:46+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# distilbert-base-uncased-distilled-clinc\n\nThis model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-distilled-clinc\n\nThis model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dat... |
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. -->
# xlm-roberta-base-yelp-mlm
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on t... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["yelp_review_full"], "metrics": ["accuracy"], "model-index": [{"name": "xlm-roberta-base-yelp-mlm", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "yelp_review_full yelp_review_full", "type": "yelp_r... | Yaxin/xlm-roberta-base-yelp-mlm | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T04:10:58+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #generated_from_trainer #dataset-yelp_review_full #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-base-yelp-mlm
This model is a fine-tuned version of xlm-roberta-base on the yelp_review_full yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1743
- Accuracy: 0.7356
## Model description
More information needed
## Intended uses & limitations
More informa... | [
"# xlm-roberta-base-yelp-mlm\n\nThis model is a fine-tuned version of xlm-roberta-base on the yelp_review_full yelp_review_full dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1743\n- Accuracy: 0.7356",
"## Model description\n\nMore information needed",
"## Intended uses & limitati... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #generated_from_trainer #dataset-yelp_review_full #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-base-yelp-mlm\n\nThis model is a fine-tuned version of xlm-roberta-base on the yelp_review_full yelp_review_f... |
text2text-generation | transformers |
PLM: KoBART-base-v2 (https://huggingface.co/gogamza/kobart-base-v2)
Fine-tuning training data: https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=93
| {"license": "apache-2.0"} | MrBananaHuman/kobart-base-v2-summarization | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T04:17:02+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
PLM: KoBART-base-v2 (URL
Fine-tuning training data: URL
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 664919631
- CO2 Emissions (in grams): 0.6969569001670619
## Validation Metrics
- Loss: 0.022509008646011353
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
```
$ c... | {"language": "en", "tags": "autotrain", "datasets": ["FuriouslyAsleep/autotrain-data-techDataClassifeier"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.6969569001670619} | FuriouslyAsleep/unhappyZebra100 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:FuriouslyAsleep/autotrain-data-techDataClassifeier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-24T04:38:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-FuriouslyAsleep/autotrain-data-techDataClassifeier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 664919631
- CO2 Emissions (in grams): 0.6969569001670619
## Validation Metrics
- Loss: 0.022509008646011353
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
Or Py... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 664919631\n- CO2 Emissions (in grams): 0.6969569001670619",
"## Validation Metrics\n\n- Loss: 0.022509008646011353\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0",
"## Usage\n\nYou can use cURL to ac... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-FuriouslyAsleep/autotrain-data-techDataClassifeier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 664... |
null | transformers |
# ๐จ Important Note: This REPO is DEPRECATED since KcELECTRA-base v2023 Released ๐จ
## USE `https://huggingface.co/beomi/KcELECTRA-base` and `v2022` Revision if needed.
---
# KcELECTRA: Korean comments ELECTRA
** Updates on 2022.10.08 **
- KcELECTRA-base-v2022 (๊ตฌ v2022-dev) ๋ชจ๋ธ ์ด๋ฆ์ด ๋ณ๊ฒฝ๋์์ต๋๋ค.
- ์ ๋ชจ๋ธ์ ์ธ๋ถ ์ค์ฝ์ด๋ฅผ ์ถ๊ฐํ์์ต๋๋ค... | {"language": ["ko", "en"], "license": "mit", "tags": ["electra", "korean"]} | beomi/KcELECTRA-base-v2022 | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T05:38:50+00:00 | [] | [
"ko",
"en"
] | TAGS
#transformers #pytorch #electra #pretraining #korean #ko #en #license-mit #endpoints_compatible #region-us
| Important Note: This REPO is DEPRECATED since KcELECTRA-base v2023 Released
===========================================================================
USE 'URL and 'v2022' Revision if needed.
----------------------------------------
---
KcELECTRA: Korean comments ELECTRA
==================================
Up... | [
"### Requirements\n\n\n* 'pytorch ~= 1.8.0'\n* 'transformers ~= 4.11.3'\n* 'emoji ~= 0.6.0'\n* 'soynlp ~= 0.0.493'",
"### Default usage\n\n\n\n> \n> ์ด์ KcBERT ๊ด๋ จ ์ฝ๋๋ค์์ 'AutoTokenizer', 'AutoModel' ์ ์ฌ์ฉํ ๊ฒฝ์ฐ '.from\\_pretrained(\"beomi/kcbert-base\")' ๋ถ๋ถ์ '.from\\_pretrained(\"beomi/KcELECTRA-base\")' ๋ก๋ง ๋ณ๊ฒฝํด์ฃผ์๋ฉด ์ฆ์ ... | [
"TAGS\n#transformers #pytorch #electra #pretraining #korean #ko #en #license-mit #endpoints_compatible #region-us \n",
"### Requirements\n\n\n* 'pytorch ~= 1.8.0'\n* 'transformers ~= 4.11.3'\n* 'emoji ~= 0.6.0'\n* 'soynlp ~= 0.0.493'",
"### Default usage\n\n\n\n> \n> ์ด์ KcBERT ๊ด๋ จ ์ฝ๋๋ค์์ 'AutoTokenizer', 'AutoMod... |
token-classification | transformers |
# bert-large-slavic-cyrillic-upos
## Model Description
This is a BERT model pre-trained with Slavic-Cyrillic ([UD_Belarusian](https://universaldependencies.org/be/) [UD_Bulgarian](https://universaldependencies.org/bg/) [UD_Russian](https://universaldependencies.org/ru/) [UD_Serbian](https://universaldependencies.org... | {"language": ["be", "bg", "mk", "ru", "sr", "uk"], "license": "cc-by-sa-4.0", "tags": ["belarusian", "bulgarian", "macedonian", "russian", "serbian", "ukrainian", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"} | KoichiYasuoka/bert-large-slavic-cyrillic-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"belarusian",
"bulgarian",
"macedonian",
"russian",
"serbian",
"ukrainian",
"pos",
"dependency-parsing",
"be",
"bg",
"mk",
"ru",
"sr",
"uk",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compati... | null | 2022-03-24T05:44:45+00:00 | [] | [
"be",
"bg",
"mk",
"ru",
"sr",
"uk"
] | TAGS
#transformers #pytorch #bert #token-classification #belarusian #bulgarian #macedonian #russian #serbian #ukrainian #pos #dependency-parsing #be #bg #mk #ru #sr #uk #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-large-slavic-cyrillic-upos
## Model Description
This is a BERT model pre-trained with Slavic-Cyrillic (UD_Belarusian UD_Bulgarian UD_Russian UD_Serbian UD_Ukrainian) for POS-tagging and dependency-parsing, derived from ruBert-large. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or... | [
"# bert-large-slavic-cyrillic-upos",
"## Model Description\n\nThis is a BERT model pre-trained with Slavic-Cyrillic (UD_Belarusian UD_Bulgarian UD_Russian UD_Serbian UD_Ukrainian) for POS-tagging and dependency-parsing, derived from ruBert-large. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## Ho... | [
"TAGS\n#transformers #pytorch #bert #token-classification #belarusian #bulgarian #macedonian #russian #serbian #ukrainian #pos #dependency-parsing #be #bg #mk #ru #sr #uk #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-large-slavic-cyrillic... |
text-generation | transformers | # Docto Bot
## Usage (HuggingFace Transformers)
```
pip install -U transformers
```
```python
import random
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("docto/Docto-Bot")
model = AutoModelForCausalLM.from_pretrained("docto/Docto-Bot")
special_to... | {"license": "afl-3.0"} | docto/Docto-Bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-24T06:17:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Docto Bot
## Usage (HuggingFace Transformers)
## Training Data
The Docto-Bot was trained on Medical Question/Answer dataset | [
"# Docto Bot",
"## Usage (HuggingFace Transformers)",
"## Training Data\r\nThe Docto-Bot was trained on Medical Question/Answer dataset"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Docto Bot",
"## Usage (HuggingFace Transformers)",
"## Training Data\r\nThe Docto-Bot was trained on Medical Question/Answer dataset"
] |
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