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automatic-speech-recognition | transformers |
# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:
- [CORAA dataset](https://github.com/nilc-nlp/CORAA)
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz).
- [Mul... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["CORAA", "common_voice", "mls", "cetuc", "voxforge"], "metrics": ["wer"]} | alefiury/wav2vec2-large-xlsr-53-coraa-brazilian-portuguese-gain-normalization | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"dataset:CORAA",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:voxforge",
"license:apache-2.0",
"model-index",
"endpoints_compatib... | null | 2022-03-27T15:34:54+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-CORAA #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-voxforge #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:
- CORAA dataset
- CETUC.
- Multilingual Librispeech (MLS).
- VoxForge.
- Common Voice 6.1.
## Repository
The repository ... | [
"# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets\r\n\r\nThis a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:\r\n\r\n- CORAA dataset\r\n- CETUC.\r\n- Multilingual Librispeech (MLS).\r\n- VoxForge.\r\n- Common Voice 6.1.",
"## Reposito... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-CORAA #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-voxforge #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2vec 2.0 trained with CORAA Po... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_data_augmented
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_data_augmented", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_data_augmented | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T16:08:43+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_data\_augmented
===================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5016
* Wer: 0.4656
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
sentence-similarity | sentence-transformers |
# sentence-bert-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train).
If you li... | {"language": ["it"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "sentence-similarity"} | efederici/sentence-bert-base | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"it",
"dataset:stsb_multi_mt",
"doi:10.57967/hf/0248",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T16:17:51+00:00 | [] | [
"it"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #it #dataset-stsb_multi_mt #doi-10.57967/hf/0248 #endpoints_compatible #region-us
|
# sentence-bert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on stsb.
If you like this project, consider supporting it with a cup of coffee!
![Buy me a coffee](URL
## ... | [
"# sentence-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on stsb. \n\nIf you like this project, consider supporting it with a cup of coffee! \n![Buy me a coffee]... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #it #dataset-stsb_multi_mt #doi-10.57967/hf/0248 #endpoints_compatible #region-us \n",
"# sentence-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vecto... |
null | transformers |
# imgbeddings
The HF repo where the models for [imgbeddings](https://github.com/minimaxir/imgbeddings) are loaded.
The ONNX files were generated using [this export Notebook](https://github.com/minimaxir/imgbeddings/blob/main/examples/export.ipynb).
## License
MIT
| {"language": ["en"], "license": "mit", "tags": ["ai", "transformers", "onnx", "images", "image-processing", "embeddings", "clip"]} | minimaxir/imgbeddings | null | [
"transformers",
"onnx",
"ai",
"images",
"image-processing",
"embeddings",
"clip",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T16:23:51+00:00 | [] | [
"en"
] | TAGS
#transformers #onnx #ai #images #image-processing #embeddings #clip #en #license-mit #endpoints_compatible #region-us
|
# imgbeddings
The HF repo where the models for imgbeddings are loaded.
The ONNX files were generated using this export Notebook.
## License
MIT
| [
"# imgbeddings\r\n\r\nThe HF repo where the models for imgbeddings are loaded.\r\n\r\nThe ONNX files were generated using this export Notebook.",
"## License\r\n\r\nMIT"
] | [
"TAGS\n#transformers #onnx #ai #images #image-processing #embeddings #clip #en #license-mit #endpoints_compatible #region-us \n",
"# imgbeddings\r\n\r\nThe HF repo where the models for imgbeddings are loaded.\r\n\r\nThe ONNX files were generated using this export Notebook.",
"## License\r\n\r\nMIT"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 675420038
- CO2 Emissions (in grams): 2.6332836871905054
## Validation Metrics
- Loss: 0.8747465014457703
- Accuracy: 0.7085201793721974
- Macro F1: 0.579743989078862
- Micro F1: 0.7085201793721974
- Weighted F1: 0.69137865222712... | {"language": "unk", "tags": "autotrain", "datasets": ["ikram54/autotrain-data-harassement"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.6332836871905054} | ikram54/autotrain-harassement-675420038 | null | [
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"dataset:ikram54/autotrain-data-harassement",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T17:06:02+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-ikram54/autotrain-data-harassement #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 675420038
- CO2 Emissions (in grams): 2.6332836871905054
## Validation Metrics
- Loss: 0.8747465014457703
- Accuracy: 0.7085201793721974
- Macro F1: 0.579743989078862
- Micro F1: 0.7085201793721974
- Weighted F1: 0.69137865222712... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 675420038\n- CO2 Emissions (in grams): 2.6332836871905054",
"## Validation Metrics\n\n- Loss: 0.8747465014457703\n- Accuracy: 0.7085201793721974\n- Macro F1: 0.579743989078862\n- Micro F1: 0.7085201793721974\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-ikram54/autotrain-data-harassement #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 675420038\n- CO2 Emissions ... |
automatic-speech-recognition | transformers |
# Wav2vec2-xls-r-1b for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://a... | {"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-1b-finnish-lm-v2", "result... | Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-27T17:10:56+00:00 | [
"2111.09296"
] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2vec2-xls-r-1b for Finnish ASR
=================================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-1b for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.... | [
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. Howe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\n... |
null | null |
Model card will be added soon... | {"license": "mit"} | almostagi/quantum-layered-tl | null | [
"license:mit",
"region:us"
] | null | 2022-03-27T17:11:30+00:00 | [] | [] | TAGS
#license-mit #region-us
|
Model card will be added soon... | [] | [
"TAGS\n#license-mit #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# T5 (base) fine-tuned on IteraTeR
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an [IteraTeR... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "IteraTeR"], "datasets": ["wanyu/IteraTeR_full_sent"], "widget": [{"text": "<clarity> Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered."}],... | mrm8488/t5-base-iterater | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"IteraTeR",
"en",
"dataset:wanyu/IteraTeR_full_sent",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T17:48:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #IteraTeR #en #dataset-wanyu/IteraTeR_full_sent #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5 (base) fine-tuned on IteraTeR
================================
This model is a fine-tuned version of t5-base on an IteraTeR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2580
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #IteraTeR #en #dataset-wanyu/IteraTeR_full_sent #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-fdRE
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chin... | {"tags": ["generated_from_trainer"], "datasets": ["sem_eval2010_task8"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-chinese-finetuned-fdRE", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "sem_eval2010_task8", "type": "sem_eval2010_task8", "arg... | leonadase/bert-base-chinese-finetuned-fdRE | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:sem_eval2010_task8",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T18:04:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-sem_eval2010_task8 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-chinese-finetuned-fdRE
================================
This model is a fine-tuned version of bert-base-chinese on the sem\_eval2010\_task8 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2716
* Accuracy: 0.9081
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-... |
question-answering | transformers | # 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1
This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
`{"exact_match": 84.673, "f1": 91.174}`
For f... | {"language": "en", "license": "apache-2.0"} | Intel/bert-large-uncased-squadv1.1-sparse-80-1x4-block-pruneofa | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"question-answering",
"en",
"arxiv:2111.05754",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T19:17:27+00:00 | [
"2111.05754"
] | [
"en"
] | TAGS
#transformers #pytorch #onnx #bert #question-answering #en #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us
| # 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1
This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
'{"exact_match": 84.673, "f1": 91.174}'
For f... | [
"# 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1\nThis model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.\nThis model yields the following results on SQuADv1.1 development set:<br>\n'{\"exact_match\": 84.673, \"f1\": 91.... | [
"TAGS\n#transformers #pytorch #onnx #bert #question-answering #en #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1\nThis model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Cybonto-distilbert-base-uncased-finetuned-ner-v0.1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["few_nerd"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Cybonto-distilbert-base-uncased-finetuned-ner-v0.1", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {... | theResearchNinja/Cybonto-distilbert-base-uncased-finetuned-ner-v0.1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:few_nerd",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T19:34:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-few_nerd #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Cybonto-distilbert-base-uncased-finetuned-ner-v0.1
==================================================
This model is a fine-tuned version of distilbert-base-uncased on the few\_nerd dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1930
* Precision: 0.7378
* Recall: 0.7818
* F1: 0.7591
* Ac... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 36\n* eval\\_batch\\_size: 36\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-few_nerd #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
text-generation | transformers |
# Pecorine dialog model | {"tags": ["conversational"]} | Garsic/DialoGPT-medium-pecorine | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T20:46:56+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Pecorine dialog model | [
"# Pecorine dialog model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Pecorine dialog model"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | andyjennings/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T21:22:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1352
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
sentence-similarity | sentence-transformers |
# sentence-BERTino
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs ([squad-it](https://github.com/crux82/squ... | {"language": ["it"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | efederici/sentence-BERTino | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T21:35:17+00:00 | [] | [
"it"
] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #it #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-BERTino
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs (squad-it) and tags/news-article pairs (via scraping).
If you li... | [
"# sentence-BERTino\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs (squad-it) and tags/news-article pairs (via scraping). \n\nI... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #it #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-BERTino\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be us... |
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/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/baguioni-elonmusk-jacobe/1648421056394/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/baguioni-elonmusk-jacobe | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T21:43:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Rowel Atienza & baguio
@baguioni-elonmusk-jacobe
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: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/1506662013707046914/hVtC... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/baguioni/1648421716784/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/baguioni | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T21:54:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
AI BOT
baguio
@baguioni
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 #has_space #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/1025926108984664064/2ZHT... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jacobe/1648422127637/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jacobe | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T22:01:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
AI BOT
Rowel Atienza
@jacobe
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 #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# harry Potter DialoGPT Model | {"tags": ["conversational"]} | CallForEcho/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T23:21:31+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# harry Potter DialoGPT Model | [
"# harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# harry Potter DialoGPT Model"
] |
fill-mask | transformers |
<!-- This model 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. -->
# python-gpt2-large-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "python-gpt2-large-issues-128", "results": []}]} | aytugkaya/python-gpt2-large-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T23:55:31+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| python-gpt2-large-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2286
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 16",
"### Trainin... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batc... |
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... | danhsf/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 | 2022-03-28T01:00:07+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: 0.2201
* Accuracy: 0.9265
* F1: 0.9266
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... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | 21iridescent/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T02:09:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3466
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract
This model is a fine-tuned version of [distilbert-base-uncased](http... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract", "results": []}]} | timhbach/Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T02:21:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0231
- eval_precision: 0.7448
- eval_recall: 0.75
- eval_f1: 0.7474
- eval_accuracy: 0.9942
- eva... | [
"# Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0231\n- eval_precision: 0.7448\n- eval_recall: 0.75\n- eval_f1: 0.7474\n- eval_accuracy: 0.... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract\n\nThis model is a fine-tuned version of distilbert-base-uncased on an... |
fill-mask | transformers | test | {} | Katster/dummy-model | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T02:52:21+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| test | [] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# ProtBert-BFD finetuned on Rosetta 20AA dataset
This model is finetuned to predict Rosetta fold energy using a dataset of 100k 20AA sequences.
Current model in this repo: `prot_bert_bfd-finetuned-032722_1752`
## Performance
- 20AA sequences (1k eval set):\
Metrics: 'mae': 0.090115, 'r2': 0.991208, 'mse': 0.013034... | {"language": "protein", "tags": ["protein language model"], "datasets": ["BFD", "Custom Rosetta"]} | rampasek/prot_bert_bfd_rosetta20aa | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"protein language model",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T03:13:53+00:00 | [] | [
"protein"
] | TAGS
#transformers #pytorch #bert #text-classification #protein language model #autotrain_compatible #endpoints_compatible #region-us
|
# ProtBert-BFD finetuned on Rosetta 20AA dataset
This model is finetuned to predict Rosetta fold energy using a dataset of 100k 20AA sequences.
Current model in this repo: 'prot_bert_bfd-finetuned-032722_1752'
## Performance
- 20AA sequences (1k eval set):\
Metrics: 'mae': 0.090115, 'r2': 0.991208, 'mse': 0.013034... | [
"# ProtBert-BFD finetuned on Rosetta 20AA dataset\n\nThis model is finetuned to predict Rosetta fold energy using a dataset of 100k 20AA sequences.\n\nCurrent model in this repo: 'prot_bert_bfd-finetuned-032722_1752'",
"## Performance\n\n- 20AA sequences (1k eval set):\\\nMetrics: 'mae': 0.090115, 'r2': 0.991208,... | [
"TAGS\n#transformers #pytorch #bert #text-classification #protein language model #autotrain_compatible #endpoints_compatible #region-us \n",
"# ProtBert-BFD finetuned on Rosetta 20AA dataset\n\nThis model is finetuned to predict Rosetta fold energy using a dataset of 100k 20AA sequences.\n\nCurrent model in this ... |
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/1443547125770559488/QNDa... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/freudwarrior123/1648441457881/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/freudwarrior123 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T03:23:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
freudwarrior123
@freudwarrior123
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-imdb", "results": []}]} | lkm2835/distilbert-imdb | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T03:29:26+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-imdb
===============
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_... |
text-classification | transformers | # Electra-base-emotion
## Model description:
## Model Performance Comparision on Emotion Dataset from Twitter:
| Model | Accuracy | F1 Score | Test Sample per Second |
| --- | --- | --- | --- |
| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion"], "metrics": ["Accuracy, F1 Score"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4", "model-index": [{"name": "bhadresh-savan... | bhadresh-savani/electra-base-emotion | null | [
"transformers",
"pytorch",
"tf",
"jax",
"electra",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T03:34:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #electra #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Electra-base-emotion
====================
Model description:
------------------
Model Performance Comparision on Emotion Dataset from Twitter:
--------------------------------------------------------------
How to Use the model:
---------------------
Dataset:
--------
Twitter-Sentiment-Analysis.
Training pr... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #electra #text-classification #emotion #en #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
## PythonGPT
A GPT2-type neural network trained on 16 gigabytes of Pyhon scripts from scratch. It has 50 million parameters.
Made as a toy. | {"language": ["en", "code", "multilingual"], "license": "mpl-2.0"} | 0x7o/pyGPT-50M | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"en",
"code",
"multilingual",
"license:mpl-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T03:50:30+00:00 | [] | [
"en",
"code",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #en #code #multilingual #license-mpl-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## PythonGPT
A GPT2-type neural network trained on 16 gigabytes of Pyhon scripts from scratch. It has 50 million parameters.
Made as a toy. | [
"## PythonGPT\nA GPT2-type neural network trained on 16 gigabytes of Pyhon scripts from scratch. It has 50 million parameters.\n\nMade as a toy."
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #en #code #multilingual #license-mpl-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## PythonGPT\nA GPT2-type neural network trained on 16 gigabytes of Pyhon scripts from scratch. It has 50 million param... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
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-large-xls-r-300m-turkish-colab", "results": []}]} | dennisowusuk/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T04:29:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
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: 0.3863
* Wer: 0.3095
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
translation | fairseq |
# MTee translation model for general domain
A general domain translation model for the MTee machine translation platform. The platform was developed in 2021 as a collaboration between the [TartuNLP](https://tartunlp.ai), the NLP research group at the University of Tartu, and [Tilde](https://tilde.com). More informati... | {"language": ["et", "en", "de", "ru"], "tags": ["translation", "modularNMT", "fairseq", "MTee", "general"], "inference": false} | tartuNLP/mtee-general | null | [
"fairseq",
"translation",
"modularNMT",
"MTee",
"general",
"et",
"en",
"de",
"ru",
"region:us"
] | null | 2022-03-28T04:32:11+00:00 | [] | [
"et",
"en",
"de",
"ru"
] | TAGS
#fairseq #translation #modularNMT #MTee #general #et #en #de #ru #region-us
| MTee translation model for general domain
=========================================
A general domain translation model for the MTee machine translation platform. The platform was developed in 2021 as a collaboration between the TartuNLP, the NLP research group at the University of Tartu, and Tilde. More information a... | [] | [
"TAGS\n#fairseq #translation #modularNMT #MTee #general #et #en #de #ru #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. -->
# sentiment-model-sample-27go-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["go_emotions"], "metrics": ["accuracy"], "model-index": [{"name": "sentiment-model-sample-27go-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "go_emotions", "type": "go_emotions... | jkhan447/sentiment-model-sample-27go-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:go_emotions",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T05:05:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-go_emotions #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# sentiment-model-sample-27go-emotion
This model is a fine-tuned version of bert-base-uncased on the go_emotions dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1765
- Accuracy: 0.5889
## Model description
More information needed
## Intended uses & limitations
More information needed... | [
"# sentiment-model-sample-27go-emotion\n\nThis model is a fine-tuned version of bert-base-uncased on the go_emotions dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 4.1765\n- Accuracy: 0.5889",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-go_emotions #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# sentiment-model-sample-27go-emotion\n\nThis model is a fine-tuned version of bert-base-uncased on the g... |
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. -->
# sentiment-model-sample-offline-goemotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "sentiment-model-sample-offline-goemotion", "results": []}]} | jkhan447/sentiment-model-sample-offline-goemotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T05:33:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sentiment-model-sample-offline-goemotion
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0183
- Accuracy: 0.7109
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# sentiment-model-sample-offline-goemotion\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.0183\n- Accuracy: 0.7109",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore i... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# sentiment-model-sample-offline-goemotion\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the... |
translation | fairseq | # A Modular Translation Model for 7 Languages
This model supports translation in all directions between the following languages: et, en, de, ru, fi, lt, lv.
The model uses a modular architecture, where each language has its own encoder and decoder that is used for all translation direction combinations. The model can... | {"language": ["et", "en", "de", "ru", "fi", "lt", "lv"], "tags": ["translation", "modularNMT", "fairseq"], "inference": false} | tartuNLP/septilang | null | [
"fairseq",
"translation",
"modularNMT",
"et",
"en",
"de",
"ru",
"fi",
"lt",
"lv",
"region:us"
] | null | 2022-03-28T05:48:48+00:00 | [] | [
"et",
"en",
"de",
"ru",
"fi",
"lt",
"lv"
] | TAGS
#fairseq #translation #modularNMT #et #en #de #ru #fi #lt #lv #region-us
| A Modular Translation Model for 7 Languages
===========================================
This model supports translation in all directions between the following languages: et, en, de, ru, fi, lt, lv.
The model uses a modular architecture, where each language has its own encoder and decoder that is used for all trans... | [] | [
"TAGS\n#fairseq #translation #modularNMT #et #en #de #ru #fi #lt #lv #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. -->
# wav2vec2hindia
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2hindia", "results": []}]} | SAGAR4REAL/wav2vec2hindia | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T06:17:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2hindia
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hype... | [
"# wav2vec2hindia\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pr... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2hindia\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Mo... |
feature-extraction | transformers |
Replicated [SPECTER model](https://huggingface.co/allenai/specter) based on w/o leakage training corpus with `seed=0`. See [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://arxiv.org/abs/2202.06671).
| {"license": "mit"} | malteos/specter-wol | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2202.06671",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T06:44:00+00:00 | [
"2202.06671"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2202.06671 #license-mit #endpoints_compatible #region-us
|
Replicated SPECTER model based on w/o leakage training corpus with 'seed=0'. See Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings.
| [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2202.06671 #license-mit #endpoints_compatible #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/1508184022052184064/yqLU... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/nsawaikar/1648454046318/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/nsawaikar | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T06:52:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
URL
@nsawaikar
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"
] |
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. -->
# tf-bert-finetuned-squad
This model is a fine-tuned version of [peterhsu/tf-bert-finetuned-squad](https://huggingface.co/peterhsu/tf-be... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-bert-finetuned-squad", "results": []}]} | peterhsu/tf-bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T07:00:46+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
|
# tf-bert-finetuned-squad
This model is a fine-tuned version of peterhsu/tf-bert-finetuned-squad 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
... | [
"# tf-bert-finetuned-squad\n\nThis model is a fine-tuned version of peterhsu/tf-bert-finetuned-squad 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 a... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"# tf-bert-finetuned-squad\n\nThis model is a fine-tuned version of peterhsu/tf-bert-finetuned-squad on an unknown dataset.\nIt achieves the following results on the evaluati... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-squad2-lwt
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilroberta-base-finetuned-squad2-lwt", "results": []}]} | 21iridescent/distilroberta-base-finetuned-squad2-lwt | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T07:54:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilroberta-base-finetuned-squad2-lwt
=======================================
This model is a fine-tuned version of distilroberta-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1356
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "c... | Chikashi/t5-small-finetuned-cnndm | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T08:07:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm
========================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6854
* Rouge1: 24.417
* Rouge2: 11.6924
* Rougel: 20.1756
* Rougelsum: 23.0414
* Gen Len: 18.9996
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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
translation | null |
# mBART 25 SentencePiece tokenizer
This tokenizer is used for Mideind's mBART translation models.
It is based on Facebooks mBART-25 SentencePiece model.
A language token from the original model has been replaced with "is_IS".
Usage example (for debugging):
```python
import sys
from transformers.models import mbart
... | {"language": ["is", "en"], "license": "mit", "tags": ["translation"]} | mideind/tokenizer-mbart-25-enis | null | [
"translation",
"is",
"en",
"license:mit",
"region:us"
] | null | 2022-03-28T09:01:18+00:00 | [] | [
"is",
"en"
] | TAGS
#translation #is #en #license-mit #region-us
|
# mBART 25 SentencePiece tokenizer
This tokenizer is used for Mideind's mBART translation models.
It is based on Facebooks mBART-25 SentencePiece model.
A language token from the original model has been replaced with "is_IS".
Usage example (for debugging):
| [
"# mBART 25 SentencePiece tokenizer\nThis tokenizer is used for Mideind's mBART translation models.\nIt is based on Facebooks mBART-25 SentencePiece model.\nA language token from the original model has been replaced with \"is_IS\".\n\nUsage example (for debugging):"
] | [
"TAGS\n#translation #is #en #license-mit #region-us \n",
"# mBART 25 SentencePiece tokenizer\nThis tokenizer is used for Mideind's mBART translation models.\nIt is based on Facebooks mBART-25 SentencePiece model.\nA language token from the original model has been replaced with \"is_IS\".\n\nUsage example (for deb... |
text-classification | transformers | # Suicidal-BERT
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).
## Data
The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/nikhileswarkomati/suicide-watch) obtained from Kaggle. The dataset was scraped from Reddit and consi... | {} | gooohjy/suicidal-bert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T09:17:22+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Suicidal-BERT
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).
## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes ... | [
"# Suicidal-BERT\r\nThis text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).",
"## Data\r\nThe model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Suicidal-BERT\r\nThis text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).",
"## Data\r\nThe model was trained on the Suicide and Depression Dat... |
text2text-generation | transformers |
## Model objective
Spanish is a beautiful language and it has many ways of referring to people, neutralizing the genders and using some of the resources inside the language. One would say *Todas las personas asistentes* instead of *Todos los asistentes* and it would end in a more inclusive way for talking about peopl... | {"language": ["es"], "license": "apache-2.0", "tags": ["Text2Text Generation", "Inclusive Language", "Text Neutralization", "pytorch"], "datasets": ["hackathon-pln-es/neutral-es"], "metrics": ["sacrebleu"], "base_model": "spanish-t5-small", "model-index": [{"name": "es_text_neutralizer", "results": [{"task": {"type": "... | hackathon-pln-es/es_text_neutralizer | null | [
"transformers",
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"safetensors",
"t5",
"text2text-generation",
"Text2Text Generation",
"Inclusive Language",
"Text Neutralization",
"es",
"dataset:hackathon-pln-es/neutral-es",
"base_model:spanish-t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints... | null | 2022-03-28T10:05:44+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #Text2Text Generation #Inclusive Language #Text Neutralization #es #dataset-hackathon-pln-es/neutral-es #base_model-spanish-t5-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #regi... |
## Model objective
Spanish is a beautiful language and it has many ways of referring to people, neutralizing the genders and using some of the resources inside the language. One would say *Todas las personas asistentes* instead of *Todos los asistentes* and it would end in a more inclusive way for talking about peopl... | [
"## Model objective\n\nSpanish is a beautiful language and it has many ways of referring to people, neutralizing the genders and using some of the resources inside the language. One would say *Todas las personas asistentes* instead of *Todos los asistentes* and it would end in a more inclusive way for talking about... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #Text2Text Generation #Inclusive Language #Text Neutralization #es #dataset-hackathon-pln-es/neutral-es #base_model-spanish-t5-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference... |
feature-extraction | transformers |
## Sem-mmmBERT
This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from mBERT (https://huggingface.co/bert-base-multilingual-cased).
The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retra... | {"language": ["multilingual"], "tags": ["STILT", "retraining", "multi-task learning"], "datasets": ["SemEval 2022"]} | robvanderg/Sem-mmmBERT | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"STILT",
"retraining",
"multi-task learning",
"multilingual",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T10:15:17+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #bert #feature-extraction #STILT #retraining #multi-task learning #multilingual #endpoints_compatible #region-us
|
## Sem-mmmBERT
This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from mBERT (URL
The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (URL a toolkit focusing... | [
"## Sem-mmmBERT\n\nThis is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from mBERT (URL \n\nThe retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (URL a toolkit f... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #STILT #retraining #multi-task learning #multilingual #endpoints_compatible #region-us \n",
"## Sem-mmmBERT\n\nThis is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from mBERT (URL \n\nThe retraining is done based on all SemE... |
feature-extraction | transformers |
## Sem-RemmmBERT
This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from remBERT (https://huggingface.co/google/rembertased).
The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining ... | {"language": ["multilingual"], "tags": ["STILT", "retraining", "multi-task learning"], "datasets": ["SemEval 2022"]} | robvanderg/Sem-RemmmBERT | null | [
"transformers",
"pytorch",
"rembert",
"feature-extraction",
"STILT",
"retraining",
"multi-task learning",
"multilingual",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T10:20:13+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #rembert #feature-extraction #STILT #retraining #multi-task learning #multilingual #endpoints_compatible #region-us
|
## Sem-RemmmBERT
This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from remBERT (URL
The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (URL a toolkit focu... | [
"## Sem-RemmmBERT\n\nThis is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from remBERT (URL \n\nThe retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (URL a toolk... | [
"TAGS\n#transformers #pytorch #rembert #feature-extraction #STILT #retraining #multi-task learning #multilingual #endpoints_compatible #region-us \n",
"## Sem-RemmmBERT\n\nThis is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from remBERT (URL \n\nThe retraining is done based on a... |
null | null | Fatima Fellowship Quick Coding Challenge
Computer Vision | {} | mosymosy/Fatima_Fellowship_Quick_Coding_Challenge | null | [
"region:us"
] | null | 2022-03-28T10:27:02+00:00 | [] | [] | TAGS
#region-us
| Fatima Fellowship Quick Coding Challenge
Computer Vision | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# CzeGPT-2
CzeGPT-2 is a Czech version of GPT-2 language model by OpenAI with LM Head on top. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimensions) resulting in 124 M trainable parameters. It was trained on a 5 G... | {"language": "cs", "license": "cc-by-nc-sa-4.0", "datasets": ["csTenTen17"]} | MU-NLPC/CzeGPT-2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"cs",
"dataset:csTenTen17",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T10:50:42+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #cs #dataset-csTenTen17 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CzeGPT-2
CzeGPT-2 is a Czech version of GPT-2 language model by OpenAI with LM Head on top. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimensions) resulting in 124 M trainable parameters. It was trained on a 5 G... | [
"# CzeGPT-2\nCzeGPT-2 is a Czech version of GPT-2 language model by OpenAI with LM Head on top. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimensions) resulting in 124 M trainable parameters. It was trained on ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #cs #dataset-csTenTen17 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CzeGPT-2\nCzeGPT-2 is a Czech version of GPT-2 language model by OpenAI with LM Head on top. The model has the same archit... |
text-generation | transformers |
# CzeGPT-2_summarizer
CzeGPT-2 summarizer is a Czech summarizer built upon the <a href="https://huggingface.co/MU-NLPC/CzeGPT-2">CzeGPT-2</a> model. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimensions) resulting... | {"language": "cs", "license": "cc-by-nc-sa-4.0", "datasets": ["csTenTen17"]} | MU-NLPC/CzeGPT-2_summarizer | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"cs",
"dataset:csTenTen17",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T11:07:57+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #cs #dataset-csTenTen17 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| CzeGPT-2\_summarizer
====================
CzeGPT-2 summarizer is a Czech summarizer built upon the <a href="URL model. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimensions) resulting in 124M trainable parameters... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #cs #dataset-csTenTen17 #license-cc-by-nc-sa-4.0 #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/1765776666/s-abetwitter1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/abeshinzo/1648469983562/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/abeshinzo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T11:19:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
安倍晋三
@abeshinzo
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
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_data_fast_10pct
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https:/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_data_fast_10pct", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_data_fast_10pct | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T11:30:15+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_data\_fast\_10pct
=====================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6983
* Wer: 0.5026
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
token-classification | transformers |
This model is the combined camembert-base model, with the pretrained lilt checkpoint from the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding".
Original repository: https://github.com/jpWang/LiLT
To use it, it is necessary to fork the modeling and ... | {"language": ["fr"], "license": "mit", "tags": ["token-classification", "fill-mask"], "datasets": ["iit-cdip"]} | manu/lilt-camembert-base | null | [
"transformers",
"pytorch",
"liltrobertalike",
"fill-mask",
"token-classification",
"fr",
"dataset:iit-cdip",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T12:16:58+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #liltrobertalike #fill-mask #token-classification #fr #dataset-iit-cdip #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
This model is the combined camembert-base model, with the pretrained lilt checkpoint from the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding".
Original repository: URL
To use it, it is necessary to fork the modeling and configuration files from th... | [] | [
"TAGS\n#transformers #pytorch #liltrobertalike #fill-mask #token-classification #fr #dataset-iit-cdip #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | transformers |
## Model
Cross-Encoder for sentence-similarity
This model was trained using [sentence-transformers](https://www.SBERT.net) Cross-Encoder class.
## Training Data
This model was trained on the [STS benchmark dataset](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train). The model will predict a score betwee... | {"language": "fr", "license": "apache-2.0", "tags": ["Text", "Sentence Similarity", "Sentence-Embedding", "camembert-base"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "sentence-camembert-base by Van Tuan DANG", "results": [{"task": {"type": "Text Similarity", "name":... | dangvantuan/CrossEncoder-camembert-large | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"Text",
"Sentence Similarity",
"Sentence-Embedding",
"camembert-base",
"sentence-similarity",
"fr",
"dataset:stsb_multi_mt",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T12:19:00+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #Text #Sentence Similarity #Sentence-Embedding #camembert-base #sentence-similarity #fr #dataset-stsb_multi_mt #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
## Model
Cross-Encoder for sentence-similarity
This model was trained using sentence-transformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage (Sentence-Transform... | [
"## Model\n\nCross-Encoder for sentence-similarity\n\nThis model was trained using sentence-transformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage (Se... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #Text #Sentence Similarity #Sentence-Embedding #camembert-base #sentence-similarity #fr #dataset-stsb_multi_mt #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model\n\nCross-Encoder for sentence-similari... |
token-classification | transformers |
# anglicisms-spanish-mbert
This is a pretrained model for detecting unassimilated English lexical borrowings (a.k.a. anglicisms) on Spanish newswire. This model labels words of foreign origin (fundamentally from English) used in Spanish language, words such as *fake news*, *machine learning*, *smartwatch*, *influence... | {"language": ["es"], "license": "cc-by-4.0", "tags": ["anglicisms", "loanwords", "borrowing", "codeswitching", "arxiv:2203.16169"], "datasets": ["coalas"], "widget": [{"text": "Las fake news sobre la celebrity se reprodujeron por los 'mass media' en prime time."}, {"text": "Me gusta el cine noir y el anime."}, {"text":... | lirondos/anglicisms-spanish-mbert | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"anglicisms",
"loanwords",
"borrowing",
"codeswitching",
"arxiv:2203.16169",
"es",
"dataset:coalas",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-28T12:26:51+00:00 | [
"2203.16169"
] | [
"es"
] | TAGS
#transformers #pytorch #bert #token-classification #anglicisms #loanwords #borrowing #codeswitching #arxiv-2203.16169 #es #dataset-coalas #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| anglicisms-spanish-mbert
========================
This is a pretrained model for detecting unassimilated English lexical borrowings (a.k.a. anglicisms) on Spanish newswire. This model labels words of foreign origin (fundamentally from English) used in Spanish language, words such as *fake news*, *machine learning*, *... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #anglicisms #loanwords #borrowing #codeswitching #arxiv-2203.16169 #es #dataset-coalas #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# BERT base model (uncased)
## Model description
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does ... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | OWG/bert-base-uncased | null | [
"transformers",
"onnx",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T12:47:11+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #onnx #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base model (uncased)
## Model description
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
## Original implementation
... | [
"# BERT base model (uncased)",
"## Model description\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.",
"## Origina... | [
"TAGS\n#transformers #onnx #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base model (uncased)",
"## Model description\n\nPretrained model on English language using a masked language mo... |
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"t... | dhlee347/distilbert-imdb | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T13:01:20+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-imdb
===============
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1796
* Accuracy: 0.9302
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-0... |
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. -->
# punctuation-test-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "punctuation-test-4", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "ro-en"}... | mikeadimech/punctuation-test-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T13:31:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| punctuation-test-4
==================
This model is a fine-tuned version of facebook/bart-base on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3411
* Bleu: 39.1294
* Gen Len: 18.4812
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #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\\_r... |
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. -->
# xls-r-es-test-lm-finetuned-sentiment-mesd
This model is a fine-tuned version of [glob-asr/xls-r-es-test-lm](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "xls-r-es-test-lm-finetuned-sentiment-mesd", "results": []}]} | DrishtiSharma/xls-r-es-test-lm-finetuned-sentiment-mesd | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T13:54:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-es-test-lm-finetuned-sentiment-mesd
=========================================
This model is a fine-tuned version of glob-asr/xls-r-es-test-lm on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7851
* Accuracy: 0.2385
Training procedure
------------------
### Training h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.25e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 40\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and eps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #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: 1.25e-05\n* train\\_batch\\_size: 64\n* e... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "... | Chikashi/t5-small-finetuned-cnndm1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T13:55:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm1
=========================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6853
* Rouge1: 24.4246
* Rouge2: 11.6944
* Rougel: 20.1717
* Rougelsum: 23.0424
* Gen Len: 18.9996
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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
text2text-generation | transformers | This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
Paper describing the model: [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://www.aclweb.org/anthology/2020.findings-emnlp.63/... | {} | castorini/monot5-3b-msmarco-10k | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2206.02873",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T14:08:54+00:00 | [
"2206.02873"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2206.02873 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
For more details on how to use it, check URL
Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model
This model is also the state of the art on the BEIR Benchmark.
- Paper: No Param... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2206.02873 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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": []}]} | joniponi/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 | 2022-03-28T14:57:55+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: 0.8357
* Accuracy: 0.6309
* F1: 0.6469
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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... |
automatic-speech-recognition | transformers |
# Wav2vec2-xls-r-1b for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://... | {"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-1b-finnish-lm", "results":... | Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
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"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-28T15:05:02+00:00 | [
"2111.09296"
] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2vec2-xls-r-1b for Finnish ASR
=================================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-1b for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page... | [
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. Howe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the U... |
null | null |
# Fake Faces with DCGANs
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations... | {"license": "apache-2.0", "tags": ["huggan", "gan", "dcgans"], "datasets": ["huggan/CelebA-faces"], "task": "image-generation"} | kingabzpro/CELEB-GANs | null | [
"huggan",
"gan",
"dcgans",
"dataset:huggan/CelebA-faces",
"license:apache-2.0",
"region:us"
] | null | 2022-03-28T15:05:34+00:00 | [] | [] | TAGS
#huggan #gan #dcgans #dataset-huggan/CelebA-faces #license-apache-2.0 #region-us
|
# Fake Faces with DCGANs
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.... | [
"# Fake Faces with DCGANs",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data ... | [
"TAGS\n#huggan #gan #dcgans #dataset-huggan/CelebA-faces #license-apache-2.0 #region-us \n",
"# Fake Faces with DCGANs",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide ex... |
automatic-speech-recognition | transformers |
# Wav2vec2-xls-r-300m for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](htt... | {"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-300m-finnish-lm", "results... | Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"... | null | 2022-03-28T15:42:29+00:00 | [
"2111.09296"
] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2vec2-xls-r-300m for Finnish ASR
===================================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this... | [
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. Howe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\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-sentiment-mesd-v2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-sentiment-mesd-v2", "results": []}]} | DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T16:20:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-sentiment-mesd-v2
=========================================
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: 1.7213
* Accuracy: 0.3923
### Training hyperparameters
The following hyperparamet... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.25e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 40\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and eps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #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: 1.25e-05\n* train\\_batch\\_size: 64\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | avb/bert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T16:23:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-cola
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8297
* Matthews Correlation: 0.5642
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
## Model description
#### Finetuned on SQUAD2.0 Dataset
#### F1: 83.738696142672
Trained on single V100 GPU
Everyone is welcome ... | {"tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "--license": "mit"} | 21iridescent/RoBERTa-base-finetuned-squad2-lwt | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T16:24:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #endpoints_compatible #region-us
| Model description
-----------------
#### Finetuned on SQUAD2.0 Dataset
#### F1: 83.738696142672
Trained on single V100 GPU
Everyone is welcome to use~
Hope you have a nice day
Performance
-----------
* HasAns\_exact': 77.1255060728745, 'HasAns\_f1': 83.87812741260885, 'HasAns\_total': 5928,
* 'NoAns\_exac... | [
"#### Finetuned on SQUAD2.0 Dataset",
"#### F1: 83.738696142672\n\n\nTrained on single V100 GPU\n\n\nEveryone is welcome to use~\n\n\nHope you have a nice day\n\n\nPerformance\n-----------\n\n\n* HasAns\\_exact': 77.1255060728745, 'HasAns\\_f1': 83.87812741260885, 'HasAns\\_total': 5928,\n* 'NoAns\\_exact': 83.59... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #endpoints_compatible #region-us \n",
"#### Finetuned on SQUAD2.0 Dataset",
"#### F1: 83.738696142672\n\n\nTrained on single V100 GPU\n\n\nEveryone is welcome to use~\n\n\nHope you have a nice day\n... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# spanish-t5-small-disco-poetry
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/fla... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "spanish-t5-small-disco-poetry", "results": []}]} | jorge-henao/spanish-t5-small-disco-poetry | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T17:15:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| spanish-t5-small-disco-poetry
=============================
This model is a fine-tuned version of flax-community/spanish-t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0477
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-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... |
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. -->
# test-model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-3... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "test-model", "results": []}]} | Vkt/first_model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T17:49:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| test-model
==========
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: 0.0161
* Wer: 0.0141
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_data_masked_audio_10ms
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_data_masked_audio_10ms", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_data_masked_audio_10ms | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T17:54:42+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_data\_masked\_audio\_10ms
=============================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5945
* Wer: 0.4929
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
sentence-similarity | sentence-transformers |
# bertin-roberta-base-finetuning-esnli
This is a [sentence-transformers](https://www.SBERT.net) model trained on a
collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Based around the siamese network... | {"language": ["es"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["hackathon-pln-es/nli-es"], "pipeline_tag": "sentence-similarity", "widget": [{"text": "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos."}, {"text": "La huelga es el m\u00e9todo ... | hackathon-pln-es/bertin-roberta-base-finetuning-esnli | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"es",
"dataset:hackathon-pln-es/nli-es",
"arxiv:1908.10084",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-28T18:08:33+00:00 | [
"1908.10084"
] | [
"es"
] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #es #dataset-hackathon-pln-es/nli-es #arxiv-1908.10084 #endpoints_compatible #has_space #region-us
| bertin-roberta-base-finetuning-esnli
====================================
This is a sentence-transformers model trained on a
collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Based around the siam... | [] | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #es #dataset-hackathon-pln-es/nli-es #arxiv-1908.10084 #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# CzeGPT-2 headline generator
CzeGPT-2_headline_generator is a Czech summarizer built upon the <a href="https://huggingface.co/MU-NLPC/CzeGPT-2">CzeGPT-2</a> model. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimen... | {"language": "cs", "license": "cc-by-nc-sa-4.0", "datasets": ["csTenTen17"]} | MU-NLPC/CzeGPT-2_headline_generator | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"cs",
"dataset:csTenTen17",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T18:12:43+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #cs #dataset-csTenTen17 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| CzeGPT-2 headline generator
===========================
CzeGPT-2\_headline\_generator is a Czech summarizer built upon the <a href="URL model. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimensions) resulting in 1... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #cs #dataset-csTenTen17 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Symbermine/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T18:38:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Husky siberiano
!Husky siberiano
#### cocker spaniel
!cocker spaniel
#### galgo
!galgo
#### labrador
... | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Husky siberiano\n\n!Husky siberiano",
"#### cocker spaniel\n\n!cocker spaniel",
"#### g... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
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... | okep/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 | 2022-03-28T19:03:24+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: 0.2269
* Accuracy: 0.9245
* F1: 0.9245
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... |
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. -->
# ita1
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](https://huggingface.... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "ita1", "results": []}]} | GioReg/ita1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T19:17:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# ita1
This model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5892
- Accuracy: 0.776
- F1: 0.5912
## Model description
More information needed
## Intended uses & limitations
More... | [
"# ita1\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5892\n- Accuracy: 0.776\n- F1: 0.5912",
"## Model description\n\nMore information needed",
"## Intended uses &... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# ita1\n\nThis model is a fine-tuned version of m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0 on the None dataset.\nIt achieves the following ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-finetuned-subj
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T19:51:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj
=====================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1424
* Precision: 0.6514
* Recall: 0.0186
* F1: 0.0363
* Accuracy: 0.9511
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
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-0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-med-term-conditional-masking-0", "results": []}]} | gayanin/t5-small-med-term-conditional-masking-0 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T21:04:47+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-0
=======================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6688
* Rouge2 Precision: 0.694
* Rouge2 Recall: 0.4781
* Rouge2 Fmeasure: 0.5479
Model descriptio... | [
"### 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... |
null | null | <!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Mar 21 22:59:35 UTC 2022`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `7ae4efd81778436a98b822483e8123adba6aa430`
... | {} | espnet/bur_openslr80_hubert | null | [
"region:us"
] | null | 2022-03-28T21:04:54+00:00 | [] | [] | TAGS
#region-us
| RESULTS
=======
Environments
------------
* date: 'Mon Mar 21 22:59:35 UTC 2022'
* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.10.1'
* Git hash: '7ae4efd81778436a98b822483e8123adba6aa430'
+ Commit date: 'Tue Mar 15 20:11:18 ... | [
"### WER",
"### CER",
"### TER"
] | [
"TAGS\n#region-us \n",
"### WER",
"### CER",
"### TER"
] |
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-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-med-term-conditional-masking-0", "results": []}]} | gayanin/bart-med-term-conditional-masking-0 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T21:12:30+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-0
===================================
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: 0.5041
* Rouge2 Precision: 0.7497
* Rouge2 Recall: 0.5246
* Rouge2 Fmeasure: 0.5986
Model descrip... | [
"### 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 #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\... |
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-sentiment-mesd-v9
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-sentiment-mesd-v9", "results": []}]} | DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v9 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-28T23:13:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-sentiment-mesd-v9
=========================================
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: 0.3500
* Accuracy: 0.9154
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 40\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #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: 64\n* eva... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm_3epoch
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dai... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm_3epoch", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "ty... | Chikashi/t5-small-finetuned-cnndm_3epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-28T23:14:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm\_3epoch
================================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6622
* Rouge1: 24.5435
* Rouge2: 11.7919
* Rougel: 20.2929
* Rougelsum: 23.1661
* Gen Len: 18.9996
Mode... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
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. -->
# ascend
This model is a fine-tuned version of [GleamEyeBeast/ascend](https://huggingface.co/GleamEyeBeast/ascend) on an unknown d... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ascend", "results": []}]} | GleamEyeBeast/ascend | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T00:37:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| ascend
======
This model is a fine-tuned version of GleamEyeBeast/ascend on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3718
* Wer: 0.6412
* Cer: 0.2428
Model description
-----------------
More information needed
Intended uses & limitations
--------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 20\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_s... |
feature-extraction | transformers |
# 🦮 LaPraDoR
Pretrained checkpoint for Findings of ACL 2022 paper [LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval](https://arxiv.org/abs/2203.06169).
To use this model, please refer to our [GitHub repo](https://github.com/JetRunner/LaPraDoR).
| {"license": "apache-2.0"} | canwenxu/laprador | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2203.06169",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T01:33:50+00:00 | [
"2203.06169"
] | [] | TAGS
#transformers #pytorch #distilbert #feature-extraction #arxiv-2203.06169 #license-apache-2.0 #endpoints_compatible #region-us
|
# LaPraDoR
Pretrained checkpoint for Findings of ACL 2022 paper LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval.
To use this model, please refer to our GitHub repo.
| [
"# LaPraDoR\r\n\r\nPretrained checkpoint for Findings of ACL 2022 paper LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval.\r\n\r\nTo use this model, please refer to our GitHub repo."
] | [
"TAGS\n#transformers #pytorch #distilbert #feature-extraction #arxiv-2203.06169 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# LaPraDoR\r\n\r\nPretrained checkpoint for Findings of ACL 2022 paper LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval.\r\n\r\nTo use this mode... |
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-sentiment-mesd-v11
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-sentiment-mesd", "results": []}]} | hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-29T01:42:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| wav2vec2-base-finetuned-sentiment-mesd-v11
==========================================
This model is a fine-tuned version of facebook/wav2vec2-base on the MESD dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3071
* Accuracy: 0.9308
Model description
-----------------
This model was tr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 40\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\... |
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-xl_ft_logits_5k_experiment
This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on an unknown da... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-xl_ft_logits_5k_experiment", "results": []}]} | beston91/gpt2-xl_ft_logits_5k_experiment | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T02:13:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-xl\_ft\_logits\_5k\_experiment
===================================
This model is a fine-tuned version of gpt2-xl on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8601
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batc... |
text-classification | transformers |
# ProtBert-BFD finetuned on Rosetta 20,40,60AA dataset
This model is finetuned to predict Rosetta fold energy using a dataset of 300k protein sequences:
100k of 20AA, 100k of 40AA, and 100k of 60AA
Current model in this repo: `prot_bert_bfd-finetuned-032822_1323`
## Performance
- 20AA sequences (1k eval set):\
Met... | {"language": "protein", "tags": ["protein language model"], "datasets": ["BFD", "Custom Rosetta"]} | rampasek/prot_bert_bfd_rosetta204060aa | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"protein language model",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T03:02:40+00:00 | [] | [
"protein"
] | TAGS
#transformers #pytorch #bert #text-classification #protein language model #autotrain_compatible #endpoints_compatible #region-us
|
# ProtBert-BFD finetuned on Rosetta 20,40,60AA dataset
This model is finetuned to predict Rosetta fold energy using a dataset of 300k protein sequences:
100k of 20AA, 100k of 40AA, and 100k of 60AA
Current model in this repo: 'prot_bert_bfd-finetuned-032822_1323'
## Performance
- 20AA sequences (1k eval set):\
Met... | [
"# ProtBert-BFD finetuned on Rosetta 20,40,60AA dataset\n\nThis model is finetuned to predict Rosetta fold energy using a dataset of 300k protein sequences:\n100k of 20AA, 100k of 40AA, and 100k of 60AA\n\nCurrent model in this repo: 'prot_bert_bfd-finetuned-032822_1323'",
"## Performance\n\n- 20AA sequences (1k ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #protein language model #autotrain_compatible #endpoints_compatible #region-us \n",
"# ProtBert-BFD finetuned on Rosetta 20,40,60AA dataset\n\nThis model is finetuned to predict Rosetta fold energy using a dataset of 300k protein sequences:\n100k of 20AA, 1... |
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-small-spanish-disco-poetry-15
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-small-spanish-disco-poetry-15", "results": []}]} | jorge-henao/gpt2-small-spanish-disco-poetry-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T03:20:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-small-spanish-disco-poetry-15
This model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2465
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training... | [
"# gpt2-small-spanish-disco-poetry-15\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 4.2465",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-small-spanish-disco-poetry-15\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish on an un... |
text-classification | transformers |
# Detección de acoso en Twitter Español
This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-tweets-hate-speech](https://huggingface.co/mrm8488/distilroberta-finetuned-tweets-hate-speech) on [hackathon-pln-es/Dataset-Acoso-Twitter-Es](https://huggingface.co/datasets/hackathon-pln-es/Dataset-Acoso-Tw... | {"language": "es", "license": "apache-2.0", "tags": ["generated_from_trainer", "es", "text-classification", "acoso", "twitter", "cyberbullying"], "datasets": ["hackathon-pln-es/Dataset-Acoso-Twitter-Es"], "metrics": ["accuracy"], "widget": [{"text": "Que horrible como la far\u00e1ndula chilena siempre se encargaba de d... | hackathon-pln-es/Detect-Acoso-Twitter-Es | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"es",
"acoso",
"twitter",
"cyberbullying",
"dataset:hackathon-pln-es/Dataset-Acoso-Twitter-Es",
"base_model:mrm8488/distilroberta-finetuned-tweets-hate-speech",
"license:apache-2.0",
"auto... | null | 2022-03-29T03:52:41+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #es #acoso #twitter #cyberbullying #dataset-hackathon-pln-es/Dataset-Acoso-Twitter-Es #base_model-mrm8488/distilroberta-finetuned-tweets-hate-speech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #... | Detección de acoso en Twitter Español
=====================================
This model is a fine-tuned version of mrm8488/distilroberta-finetuned-tweets-hate-speech on hackathon-pln-es/Dataset-Acoso-Twitter-Es.
It achieves the following results on the evaluation set:
* Loss: 0.1628
* Accuracy: 0.9167
UNL: Unive... | [
"### 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #es #acoso #twitter #cyberbullying #dataset-hackathon-pln-es/Dataset-Acoso-Twitter-Es #base_model-mrm8488/distilroberta-finetuned-tweets-hate-speech #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_s... |
text-classification | transformers |
## Model Details: INT8 DistilBERT base uncased finetuned SST-2
This model is a fine-tuned DistilBERT model for the downstream task of sentiment classification, training on the [SST-2 dataset](https://huggingface.co/datasets/sst2) and quantized to INT8 (post-training static quantization) from the original FP32 model (... | {"language": "en", "license": "apache-2.0", "tags": ["text-classfication", "int8", "neural-compressor", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["sst2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst-2-english-int8-static", "results": [{"task": {"type": "sentiment-classific... | Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static | null | [
"transformers",
"pytorch",
"onnx",
"distilbert",
"text-classification",
"text-classfication",
"int8",
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"PostTrainingStatic",
"en",
"dataset:sst2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has... | null | 2022-03-29T04:04:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #distilbert #text-classification #text-classfication #int8 #neural-compressor #Intel® Neural Compressor #PostTrainingStatic #en #dataset-sst2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Details: INT8 DistilBERT base uncased finetuned SST-2
-----------------------------------------------------------
This model is a fine-tuned DistilBERT model for the downstream task of sentiment classification, training on the SST-2 dataset and quantized to INT8 (post-training static quantization) from the orig... | [
"#### Load the PyTorch model with Optimum Intel",
"#### Load the ONNX model with Optimum:\n\n\n\n\n\n\n\n\n\nBibTeX Entry and Citation Info\n=============================="
] | [
"TAGS\n#transformers #pytorch #onnx #distilbert #text-classification #text-classfication #int8 #neural-compressor #Intel® Neural Compressor #PostTrainingStatic #en #dataset-sst2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Load the PyTorch model wit... |
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-small-spanish-disco-poetry
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "datificate/gpt2-small-spanish", "model-index": [{"name": "gpt2-small-spanish-disco-poetry", "results": []}]} | hackathon-pln-es/gpt2-small-spanish-disco-poetry | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:datificate/gpt2-small-spanish",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T04:20:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #base_model-datificate/gpt2-small-spanish #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# gpt2-small-spanish-disco-poetry
This model is a fine-tuned version of datificate/gpt2-small-spanish on an DISCO dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2940
## Model description
More information needed
## Intended uses & limitations
More information needed
## Train... | [
"# gpt2-small-spanish-disco-poetry\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish on an DISCO dataset dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 4.2940",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informatio... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #base_model-datificate/gpt2-small-spanish #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# gpt2-small-spanish-disco-poetry\n\nThis model is a fine-tuned... |
text-generation | transformers |
# Model description
This model is a fine-tuned version of [flax-community/gpt-2-spanish](https://huggingface.co/flax-community/gpt-2-spanish) on a custom dataset (not publicly available). The dataset is made of crawled data from 3 Spanish cooking websites and it contains approximately ~50000 recipes.
It achieves the... | {"language": ["es"], "tags": ["generated_from_trainer", "recipe-generation"], "widget": [{"text": "<RECIPE_START> <INPUT_START> salm\u00f3n <NEXT_INPUT> zumo de naranja <NEXT_INPUT> aceite de oliva <NEXT_INPUT> sal <NEXT_INPUT> pimienta <INPUT_END> <INGR_START>"}, {"text": "<RECIPE_START> <INPUT_START> harina <NEXT_INP... | gastronomia-para-to2/gastronomia_para_to2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"recipe-generation",
"es",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T05:26:01+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #recipe-generation #es #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Model description
=================
This model is a fine-tuned version of flax-community/gpt-2-spanish on a custom dataset (not publicly available). The dataset is made of crawled data from 3 Spanish cooking websites and it contains approximately ~50000 recipes.
It achieves the following results on the evaluation set... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #recipe-generation #es #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: ... |
text2text-generation | transformers |
# Livonian NMT
This model translates English, Estonian and Latvian into Livonian. It is based on [m2m100_418M](https://huggingface.co/facebook/m2m100_418M), fine-tuned to all-to-Livonian data from the [liv4ever](https://huggingface.co/datasets/tartuNLP/liv4ever-data) dataset. | {"language": ["en", "lv", "et", "multilingual"], "widget": [{"text": "Let us translate some text to Livonian!"}]} | tartuNLP/nmt-all-to-liv-base | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"lv",
"et",
"multilingual",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T05:44:46+00:00 | [] | [
"en",
"lv",
"et",
"multilingual"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #en #lv #et #multilingual #autotrain_compatible #endpoints_compatible #region-us
|
# Livonian NMT
This model translates English, Estonian and Latvian into Livonian. It is based on m2m100_418M, fine-tuned to all-to-Livonian data from the liv4ever dataset. | [
"# Livonian NMT\n\n\n\nThis model translates English, Estonian and Latvian into Livonian. It is based on m2m100_418M, fine-tuned to all-to-Livonian data from the liv4ever dataset."
] | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #en #lv #et #multilingual #autotrain_compatible #endpoints_compatible #region-us \n",
"# Livonian NMT\n\n\n\nThis model translates English, Estonian and Latvian into Livonian. It is based on m2m100_418M, fine-tuned to all-to-Livonian data from the liv4e... |
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. -->
# roberta-retrained_ru_covid_papers
This model is a fine-tuned version of [Daryaflp/roberta-retrained_ru_covid](https://huggingfac... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-retrained_ru_covid_papers", "results": []}]} | Daryaflp/roberta-retrained_ru_covid_papers | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T06:12:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-retrained_ru_covid_papers
This model is a fine-tuned version of Daryaflp/roberta-retrained_ru_covid on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9998
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tra... | [
"# roberta-retrained_ru_covid_papers\n\nThis model is a fine-tuned version of Daryaflp/roberta-retrained_ru_covid on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.9998",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informat... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-retrained_ru_covid_papers\n\nThis model is a fine-tuned version of Daryaflp/roberta-retrained_ru_covid on an unknown dataset.\nIt achieves the following res... |
null | null | Metamodel Card (MMC) builds on MC and DC schemas by adding system level abstraction to the data. MMC instantiations follow | {"license": "mit"} | STARBORN/MMC | null | [
"license:mit",
"region:us"
] | null | 2022-03-29T06:12:26+00:00 | [] | [] | TAGS
#license-mit #region-us
| Metamodel Card (MMC) builds on MC and DC schemas by adding system level abstraction to the data. MMC instantiations follow | [] | [
"TAGS\n#license-mit #region-us \n"
] |
null | null |
This is the exported model for a small project I' working on, to test integration with spaces.
It is a fastai model and needs some custom code to work.
For now please ignore :) | {"license": "cc-by-4.0"} | johnowhitaker/sketchy_unet_rn34 | null | [
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-29T06:57:40+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #has_space #region-us
|
This is the exported model for a small project I' working on, to test integration with spaces.
It is a fastai model and needs some custom code to work.
For now please ignore :) | [] | [
"TAGS\n#license-cc-by-4.0 #has_space #region-us \n"
] |
automatic-speech-recognition | transformers | # XLS-R-1B-ITALIAN-DOC4LM-5GRAM
## Fine-tuned XLS-R 1B model for speech recognition in Italian
Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Italian using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voi... | {"language": ["it"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard", "it", "mozilla-foundation/common_voice_8_0", "speech", "wav2vec2"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R Wav2Vec2 Italian by ra... | radiogroup-crits/wav2vec2-xls-r-1b-italian-doc4lm-5gram | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"it",
"mozilla-foundation/common_voice_8_0",
"speech",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T07:31:46+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #it #mozilla-foundation/common_voice_8_0 #speech #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # XLS-R-1B-ITALIAN-DOC4LM-5GRAM
## Fine-tuned XLS-R 1B model for speech recognition in Italian
Fine-tuned facebook/wav2vec2-xls-r-1b on Italian using the train and validation splits of Common Voice 8.0, Multilingual TEDx, Multilingual LibriSpeech, and Voxpopuli.
When using this model, make sure that your speech inpu... | [
"# XLS-R-1B-ITALIAN-DOC4LM-5GRAM",
"## Fine-tuned XLS-R 1B model for speech recognition in Italian\n\nFine-tuned facebook/wav2vec2-xls-r-1b on Italian using the train and validation splits of Common Voice 8.0, Multilingual TEDx, Multilingual LibriSpeech, and Voxpopuli.\n\nWhen using this model, make sure that you... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #it #mozilla-foundation/common_voice_8_0 #speech #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# XLS-R-1B-ITALIAN-DOC4LM-5GRAM",
"## Fine-tuned ... |
token-classification | flair |
## HunFlair model for ENHANCER
[HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for enhancer entity.
Predicts 1 tag:
| **tag** | **meaning** |
|---------------------------------|-----------|
| Enhancer | DNA enhancer region |
... | {"language": "en", "tags": ["flair", "hunflair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Isolate an enhancer element located between -89 and -50 bp in PAI-1"}]} | regel-corpus/hunflair-enhancer | null | [
"flair",
"pytorch",
"hunflair",
"token-classification",
"sequence-tagger-model",
"en",
"region:us"
] | null | 2022-03-29T08:09:18+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #hunflair #token-classification #sequence-tagger-model #en #region-us
| HunFlair model for ENHANCER
---------------------------
HunFlair (biomedical flair) for enhancer entity.
Predicts 1 tag:
---
### Cite
Please cite the following paper when using this model.
---
### Demo: How to use in Flair
Requires:
* Flair ('pip install flair')
This yields the following output... | [
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Demo: How to use in Flair\n\n\nRequires:\n\n\n* Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entity \"*enhancer element located between - 89 and - 50 bp in PAI-1*\" (labeled as a enhancer) ... | [
"TAGS\n#flair #pytorch #hunflair #token-classification #sequence-tagger-model #en #region-us \n",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Demo: How to use in Flair\n\n\nRequires:\n\n\n* Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, ... |
question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | peterhsu/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T08:35:05+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
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. -->
# jwt300_mt-Italian-to-Spanish_transformers
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["new_dataset"], "metrics": ["sacrebleu"], "model-index": [{"name": "jwt300_mt-Italian-to-Spanish_transformers", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "new_dat... | frtna/jwt300_mt-Italian-to-Spanish_transformers | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:new_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T08:49:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-new_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| jwt300\_mt-Italian-to-Spanish\_transformers
===========================================
This model is a fine-tuned version of t5-small on the new\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4425
* Sacrebleu: 0.9057
* Gen Len: 18.1276
Model description
-----------------
M... | [
"### 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: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-new_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 678720226
- CO2 Emissions (in grams): 133.19491276284793
## Validation Metrics
- Loss: 0.4864234924316406
- Accuracy: 0.865424430641822
- Macro F1: 0.7665472174344069
- Micro F1: 0.8654244306418221
- Weighted F1: 0.85863754451150... | {"language": "unk", "tags": "autotrain", "datasets": ["KeithHorgan98/autotrain-data-TweetClimateAnalysis"], "widget": [{"text": "Climate Change is a hoax"}, {"text": "It is freezing, where is global warming"}], "co2_eq_emissions": 133.19491276284793} | KeithHorgan/TweetClimateAnalysis | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:KeithHorgan98/autotrain-data-TweetClimateAnalysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T09:16:42+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-KeithHorgan98/autotrain-data-TweetClimateAnalysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 678720226
- CO2 Emissions (in grams): 133.19491276284793
## Validation Metrics
- Loss: 0.4864234924316406
- Accuracy: 0.865424430641822
- Macro F1: 0.7665472174344069
- Micro F1: 0.8654244306418221
- Weighted F1: 0.85863754451150... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 678720226\n- CO2 Emissions (in grams): 133.19491276284793",
"## Validation Metrics\n\n- Loss: 0.4864234924316406\n- Accuracy: 0.865424430641822\n- Macro F1: 0.7665472174344069\n- Micro F1: 0.8654244306418221\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-KeithHorgan98/autotrain-data-TweetClimateAnalysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 678720226... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Rishav-hub/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T09:26:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1352
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | flair |
## HunFlair model for PROMOTER
[HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for promoter entity.
Predicts 1 tag:
| **tag** | **meaning** |
|---------------------------------|-----------|
| Promoter | DNA promoter region |
... | {"language": "en", "tags": ["flair", "hunflair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Two putative extended promoters consensus sequences (p1 and p2)."}]} | regel-corpus/hunflair-promoter | null | [
"flair",
"pytorch",
"hunflair",
"token-classification",
"sequence-tagger-model",
"en",
"region:us"
] | null | 2022-03-29T10:22:27+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #hunflair #token-classification #sequence-tagger-model #en #region-us
| HunFlair model for PROMOTER
---------------------------
HunFlair (biomedical flair) for promoter entity.
Predicts 1 tag:
---
### Cite
Please cite the following paper when using this model.
---
### Demo: How to use in Flair
Requires:
* Flair ('pip install flair')
This yields the following output... | [
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Demo: How to use in Flair\n\n\nRequires:\n\n\n* Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*p1*\" and \"*p2*\" (labeled as a promoter) are found in the sentence.\n\n\nAlternati... | [
"TAGS\n#flair #pytorch #hunflair #token-classification #sequence-tagger-model #en #region-us \n",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Demo: How to use in Flair\n\n\nRequires:\n\n\n* Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, ... |
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