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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | flair |
## HunFlair model for Transcription Factor Binding Site (TFBS)
[HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for TFBS entity.
Predicts 1 tag:
| **tag** | **meaning** |
|---------------------------------|-----------|
| Tfbs | D... | {"language": "en", "tags": ["flair", "hunflair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "It contains a functional GCGGCGGCG Egr-1-binding site"}]} | regel-corpus/hunflair-tfbs | null | [
"flair",
"pytorch",
"hunflair",
"token-classification",
"sequence-tagger-model",
"en",
"region:us"
] | null | 2022-03-29T10:26:41+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #hunflair #token-classification #sequence-tagger-model #en #region-us
| HunFlair model for Transcription Factor Binding Site (TFBS)
-----------------------------------------------------------
HunFlair (biomedical flair) for TFBS entity.
Predicts 1 tag:
---
### Cite
Please cite the following paper when using this model.
---
### Demo: How to use in Flair
Requires:
* Fl... | [
"### 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 \"*GCGGCGGCG Egr-1-binding site*\" is found in the sentence.\n\n\nAlternatively download ... | [
"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, ... |
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
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_masked_audio", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_data_masked_audio | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T10:30:40+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
=======================================================
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: 0.6445
* Wer: 0.4938
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... |
fill-mask | transformers | # 80% 1x4 Block Sparse BERT-Large (uncased) Prune OFA
This model is was created using Prune OFA method described in [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021.
For further details on the model and its result, see our paper and our... | {"language": "en", "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["wikipedia", "bookcorpus"]} | Intel/bert-large-uncased-sparse-80-1x4-block-pruneofa | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"fill-mask",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:2111.05754",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T10:42:51+00:00 | [
"2111.05754"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us
| # 80% 1x4 Block Sparse BERT-Large (uncased) Prune OFA
This model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.
For further details on the model and its result, see our paper and our implementation available here.
| [
"# 80% 1x4 Block Sparse BERT-Large (uncased) Prune OFA\nThis model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.\n\nFor further details on the model and its result, see our paper and our implementation available he... | [
"TAGS\n#transformers #pytorch #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# 80% 1x4 Block Sparse BERT-Large (uncased) Prune OFA\nThis model is was created using Prune OFA method described in Prune Once for Al... |
fill-mask | transformers | # 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021.
For further details on the model and its result, see our paper and our ... | {"language": "en", "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["wikipedia", "bookcorpus"]} | Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa | null | [
"transformers",
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"bert",
"pretraining",
"fill-mask",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:2111.05754",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T10:58:55+00:00 | [
"2111.05754"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us
| # 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.
For further details on the model and its result, see our paper and our implementation available here.
| [
"# 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA\nThis model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.\n\nFor further details on the model and its result, see our paper and our implementation available her... | [
"TAGS\n#transformers #pytorch #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA\nThis model is was created using Prune OFA method described in Prune Once for All... |
token-classification | flair |
# anglicisms-spanish-flair-cs
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*, *influe... | {"language": ["es"], "license": "cc-by-4.0", "tags": ["anglicisms", "loanwords", "borrowing", "codeswitching", "flair", "token-classification", "sequence-tagger-model", "arxiv:2203.16169"], "datasets": ["coalas"], "widget": [{"text": "Las fake news sobre la celebrity se reprodujeron por los 'mass media' en prime time."... | lirondos/anglicisms-spanish-flair-cs | null | [
"flair",
"pytorch",
"anglicisms",
"loanwords",
"borrowing",
"codeswitching",
"token-classification",
"sequence-tagger-model",
"arxiv:2203.16169",
"es",
"dataset:coalas",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-29T12:09:33+00:00 | [
"2203.16169"
] | [
"es"
] | TAGS
#flair #pytorch #anglicisms #loanwords #borrowing #codeswitching #token-classification #sequence-tagger-model #arxiv-2203.16169 #es #dataset-coalas #license-cc-by-4.0 #region-us
| anglicisms-spanish-flair-cs
===========================
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 learni... | [] | [
"TAGS\n#flair #pytorch #anglicisms #loanwords #borrowing #codeswitching #token-classification #sequence-tagger-model #arxiv-2203.16169 #es #dataset-coalas #license-cc-by-4.0 #region-us \n"
] |
text-generation | transformers |
# Simple model for Paraphrase Generation
## Model description
T5-based model for generating paraphrased sentences. It is trained on the labeled [MSRP](https://www.microsoft.com/en-us/download/details.aspx?id=52398) and [Google PAWS](https://github.com/google-research-datasets/paws) dataset.
## How to use
... | {"language": "en", "tags": ["paraphrase-generation", "text-generation", "Conditional Generation"], "inference": false} | shrishail/t5_paraphrase_msrp_paws | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"paraphrase-generation",
"text-generation",
"Conditional Generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T12:13:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #paraphrase-generation #text-generation #Conditional Generation #en #autotrain_compatible #text-generation-inference #region-us
|
# Simple model for Paraphrase Generation
## Model description
T5-based model for generating paraphrased sentences. It is trained on the labeled MSRP and Google PAWS dataset.
## How to use
| [
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"## Model description\r\n\r\nT5-based model for generating paraphrased se... |
text-generation | transformers |
EmailGenerator is a gpt-2 fine-tuned text-generation pre-trained model trained on [emailblog](https://www.kaggle.com/datasets/mikeschmidtavemac/emailblog) datasets for [EmailWriter](https://github.com/sagorbrur/EmailWriter) repositories.
For details about this model check [EmailWriter](https://github.com/sagorbrur/Em... | {"language": "en", "license": "mit", "tags": ["email-generation"]} | sagorsarker/emailgenerator | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"email-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T12:14:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #email-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
EmailGenerator is a gpt-2 fine-tuned text-generation pre-trained model trained on emailblog datasets for EmailWriter repositories.
For details about this model check EmailWriter repository.
| [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #email-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
token-classification | transformers | # 🔑 Keyphrase Extraction Model: KBIR-inspec
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done ... | {"language": "en", "license": "mit", "tags": ["keyphrase-extraction"], "datasets": ["midas/inspec"], "metrics": ["seqeval"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content ... | ml6team/keyphrase-extraction-kbir-inspec | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"keyphrase-extraction",
"en",
"dataset:midas/inspec",
"arxiv:2112.08547",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-29T12:14:21+00:00 | [
"2112.08547"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #keyphrase-extraction #en #dataset-midas/inspec #arxiv-2112.08547 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Keyphrase Extraction Model: KBIR-inspec
=======================================
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely.... | [
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.\n* Only works for English documents.",
"### How To Use\n\n\nTraining Dataset\n---... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #keyphrase-extraction #en #dataset-midas/inspec #arxiv-2112.08547 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Limitations\n\n\n* This keyphrase extraction model is very domain-specific and will p... |
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-russian-spell
---
модель для исправление текста из распознаного аудио. моя модлеь для распознования аудио https://hugging... | {"tags": ["generated_from_trainer"], "datasets": "UrukHan/wav2vec2-russian", "widget": [{"text": "\u044b\u0432\u0441\u0435\u043c \u043f\u0440\u0438\u0432\u0435\u0442 \u0432\u044b\u043d\u044b\u043a\u0430\u043d\u0430\u043b\u0435\u0442\u043e\u043f \u0430\u0440\u043c\u0438\u0438 \u0438 \u044d\u0442\u043e \u0434\u0432\u0430... | UrukHan/t5-russian-spell | null | [
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"pytorch",
"tensorboard",
"safetensors",
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"text2text-generation",
"generated_from_trainer",
"dataset:UrukHan/wav2vec2-russian",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T13:20:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-UrukHan/wav2vec2-russian #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
---
# t5-russian-spell
---
модель для исправление текста из распознаного аудио. моя модлеь для распознования аудио URL и его результаты можно закидывать в эту модель. тестил на видео случайном с ютюба
<table border="0">
<tr>
<td><b style="font-size:30px">Output wav2vec2</b></td>
<td><b style="font-size:30... | [
"# t5-russian-spell\n---\nмодель для исправление текста из распознаного аудио. моя модлеь для распознования аудио URL и его результаты можно закидывать в эту модель. тестил на видео случайном с ютюба\n\n<table border=\"0\">\n <tr>\n <td><b style=\"font-size:30px\">Output wav2vec2</b></td>\n <td><b style=\"f... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-UrukHan/wav2vec2-russian #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-russian-spell\n---\nмодель для исправление текста из распознаного аудио. моя мо... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 680820343
- CO2 Emissions (in grams): 115.48848403681228
## Validation Metrics
- Loss: 0.3041240870952606
- Accuracy: 0.9462770369425126
- Macro F1: 0.7836898686625933
- Micro F1: 0.9462770369425126
- Weighted F1: 0.9449148298990... | {"language": "es", "tags": "autotrain", "datasets": ["gabitoo1234/autotrain-data-mut_all_text"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 115.48848403681228} | gabitoo1234/autotrain-mut_all_text-680820343 | null | [
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"text-classification",
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"dataset:gabitoo1234/autotrain-data-mut_all_text",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T13:22:14+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #es #dataset-gabitoo1234/autotrain-data-mut_all_text #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 680820343
- CO2 Emissions (in grams): 115.48848403681228
## Validation Metrics
- Loss: 0.3041240870952606
- Accuracy: 0.9462770369425126
- Macro F1: 0.7836898686625933
- Micro F1: 0.9462770369425126
- Weighted F1: 0.9449148298990... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 680820343\n- CO2 Emissions (in grams): 115.48848403681228",
"## Validation Metrics\n\n- Loss: 0.3041240870952606\n- Accuracy: 0.9462770369425126\n- Macro F1: 0.7836898686625933\n- Micro F1: 0.9462770369425126\n- Weighted F... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 680820343\n- CO2 Emissi... |
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_v1
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/b... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v1", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v1 | null | [
"transformers",
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"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T13:22:30+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\_v1
=========================================
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: 0.1594
* Precision: 0.1875
* Recall: 0.0077
* F1: 0.0147
* Accuracy: 0.9508
Model... | [
"### 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:... |
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. -->
# augmented
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "augmented", "results": []}]} | krinal214/augmented | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T14:02:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| augmented
=========
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5104
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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 #bert #question-answering #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
token-classification | transformers |
## Model information:
distilibert-base-uncased model finetuned using the ncbi_disease dataset from the datasets library.
## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will predict lables based upon the NCBI-diseas... | {"language": "en", "license": "cc", "tags": ["token-classification", "named-entity-recognition", "multi_class_classification"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "task": ["token-classification", "named-entity-recognition", "multi_class_classification"], "widget": [{"tex... | sarahmiller137/distilbert-base-uncased-ft-ncbi-disease | null | [
"transformers",
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"safetensors",
"distilbert",
"token-classification",
"named-entity-recognition",
"multi_class_classification",
"en",
"dataset:ncbi_disease",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T14:21:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #named-entity-recognition #multi_class_classification #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
distilibert-base-uncased model finetuned using the ncbi_disease dataset from the datasets library.
## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will predict lables based upon the NCBI-diseas... | [
"## Model information:\ndistilibert-base-uncased model finetuned using the ncbi_disease dataset from the datasets library.",
"## Intended uses:\nThis model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will predict lables based upon the NC... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #token-classification #named-entity-recognition #multi_class_classification #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\ndistilibert-base-uncased model finetuned using the ncbi_disea... |
null | null | The ch-w2v-conformer model uses following datasets to pretrain:
ISML datasets (6 languages,70k hours): internal dataset contains 40k hours Chinese, Cantonese, Tibetan, Inner Mongolian, Inner Kazakh, Uighur.
Babel datasets (17 languages, 2k hours): Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh... | {} | emiyasstar/ch-w2v-conformer | null | [
"region:us"
] | null | 2022-03-29T14:44:56+00:00 | [] | [] | TAGS
#region-us
| The ch-w2v-conformer model uses following datasets to pretrain:
ISML datasets (6 languages,70k hours): internal dataset contains 40k hours Chinese, Cantonese, Tibetan, Inner Mongolian, Inner Kazakh, Uighur.
Babel datasets (17 languages, 2k hours): Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Ku... | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# LavenzaNumTwo DialoGPT Model | {"tags": ["conversational"]} | BeamBee/DialoGPT-small-LavenzaNumTwo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T14:56:17+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# LavenzaNumTwo DialoGPT Model | [
"# LavenzaNumTwo DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# LavenzaNumTwo DialoGPT Model"
] |
translation | transformers |
# t5-small-spanish-nahuatl
Nahuatl is the most widely spoken indigenous language in Mexico. However, training a neural network for the neural machine translation task is challenging due to the lack of structured data. The most popular datasets, such as the Axolot and bible-corpus, only consist of ~16,000 and ~7,000 sa... | {"language": ["es", "nah", "multilingual"], "license": "apache-2.0", "tags": ["translation"], "widget": [{"text": "translate Spanish to Nahuatl: Mi hermano es un ajolote"}]} | hackathon-pln-es/t5-small-spanish-nahuatl | null | [
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"safetensors",
"t5",
"text2text-generation",
"translation",
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"nah",
"multilingual",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T15:35:17+00:00 | [] | [
"es",
"nah",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #translation #es #nah #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| t5-small-spanish-nahuatl
========================
Nahuatl is the most widely spoken indigenous language in Mexico. However, training a neural network for the neural machine translation task is challenging due to the lack of structured data. The most popular datasets, such as the Axolot and bible-corpus, only consist ... | [
"### Dataset\n\n\nSince the Axolotl corpus contains misalignments, we select the best samples (12,207). We also use the bible-corpus (7,821).\n\n\n\nAlso, we collected 3,000 extra samples from the web to increase the data.",
"### Model and training\n\n\nWe employ two training stages using a multilingual T5-small.... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #translation #es #nah #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Dataset\n\n\nSince the Axolotl corpus contains misalignments, we select the best sampl... |
text-classification | transformers | ## Overview
The model is a `roberta-base` fine-tuned on [fake-and-real-news-dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset). It has a 100% accuracy on that dataset.
The model takes a news article and predicts if it is true or fake.
The format of the input should be:
```
<title> ... | {"language": ["en"], "license": "mit", "tags": ["classification"], "datasets": ["https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset"], "widget": [{"text": "Some ninja attacked the White House.", "example_title": "Fake example 1"}]} | hamzab/roberta-fake-news-classification | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-29T16:36:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Overview
The model is a 'roberta-base' fine-tuned on fake-and-real-news-dataset. It has a 100% accuracy on that dataset.
The model takes a news article and predicts if it is true or fake.
The format of the input should be:
## Using this model in your code
To use this model, first download it from the hugginfa... | [
"## Overview\nThe model is a 'roberta-base' fine-tuned on fake-and-real-news-dataset. It has a 100% accuracy on that dataset. \nThe model takes a news article and predicts if it is true or fake.\nThe format of the input should be:",
"## Using this model in your code \nTo use this model, first download it from the... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Overview\nThe model is a 'roberta-base' fine-tuned on fake-and-real-news-dataset. It has a 100% accuracy on that dataset. \nThe model takes a news... |
text-generation | transformers | # Michael Scott DialoGPT Model
| {"tags": ["conversational"]} | Meowren/MichaelScottBott | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T17:38:39+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Michael Scott DialoGPT Model
| [
"# Michael Scott DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott DialoGPT Model"
] |
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. -->
# poem-gen-spanish-t5-small-v5
This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-spanish-t5-small-v5", "results": []}]} | DrishtiSharma/poem-gen-spanish-t5-small-v5 | null | [
"transformers",
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"tensorboard",
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"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T17:54:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| poem-gen-spanish-t5-small-v5
============================
This model is a fine-tuned version of hackathon-pln-es/poem-gen-spanish-t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8881
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000125\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\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",
"### Train... | [
"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: 0.000... |
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. -->
# poem-gen-spanish-t5-small-v6
This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-spanish-t5-small-v6", "results": []}]} | DrishtiSharma/poem-gen-spanish-t5-small-v6 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T17:58:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| poem-gen-spanish-t5-small-v6
============================
This model is a fine-tuned version of hackathon-pln-es/poem-gen-spanish-t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8831
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\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 #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: 7.5e-... |
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. -->
# poem-gen-spanish-t5-small-v7
This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-spanish-t5-small-v7", "results": []}]} | DrishtiSharma/poem-gen-spanish-t5-small-v7 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T18:14:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| poem-gen-spanish-t5-small-v7
============================
This model is a fine-tuned version of hackathon-pln-es/poem-gen-spanish-t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9201
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000333\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\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",
"### Train... | [
"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: 0.000... |
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. -->
# electricidad-base-generator-fake-news
This model is a fine-tuned version of [mrm8488/electricidad-base-generator](https://huggin... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "electricidad-base-generator-fake-news", "results": []}]} | hackathon-pln-es/electricidad-base-generator-fake-news | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-29T18:52:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
| electricidad-base-generator-fake-news
=====================================
This model is a fine-tuned version of mrm8488/electricidad-base-generator on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0067
* Accuracy: 1.0
Model description
-----------------
More informatio... | [
"### 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... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #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:... |
sentence-similarity | sentence-transformers |
# sentence-IT5-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is a T5 ([IT5](https://huggingface.co/gsarti/it5-base)) base model. It is trained on a dataset mad... | {"language": ["it"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | efederici/sentence-it5-base | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"transformers",
"it",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T18:57:59+00:00 | [] | [
"it"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #transformers #it #endpoints_compatible #region-us
|
# sentence-IT5-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is a T5 (IT5) base model. It is trained on a dataset made from question/context pairs (squad-it), tags/news-article pairs, ... | [
"# sentence-IT5-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is a T5 (IT5) base model. It is trained on a dataset made from question/context pairs (squad-it), tags/news-article p... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #transformers #it #endpoints_compatible #region-us \n",
"# sentence-IT5-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clusterin... |
automatic-speech-recognition | speechbrain |
# German ASR
This model is trained on the Mozilla Common Voice 6.1, the Spoken Wikipedia Corpus and the m-ailabs corpus.
- https://nats.gitlab.io/swc/
- https://commonvoice.mozilla.org/de/datasets
- https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset/
We do not provide a language model.
You can find ... | {"language": "de", "license": "cc-by-sa-4.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "pytorch", "speechbrain"], "metrics": ["wer"]} | jfreiwa/asr-crdnn-german | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"de",
"arxiv:2106.04624",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-29T19:02:49+00:00 | [
"2106.04624"
] | [
"de"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #de #arxiv-2106.04624 #license-cc-by-sa-4.0 #region-us
|
# German ASR
This model is trained on the Mozilla Common Voice 6.1, the Spoken Wikipedia Corpus and the m-ailabs corpus.
- URL
- URL
- URL
We do not provide a language model.
You can find the training codes here.
# Performance
This model has a WER of 7.24%.
(You can find an updated version of this model her... | [
"# German ASR\n\nThis model is trained on the Mozilla Common Voice 6.1, the Spoken Wikipedia Corpus and the m-ailabs corpus.\n\n - URL\n - URL\n - URL\n\nWe do not provide a language model.\n\nYou can find the training codes here.",
"# Performance\n\nThis model has a WER of 7.24%.\n(You can find an updated ver... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #de #arxiv-2106.04624 #license-cc-by-sa-4.0 #region-us \n",
"# German ASR\n\nThis model is trained on the Mozilla Common Voice 6.1, the Spoken Wikipedia Corpus and the m-ailabs corpus.\n\n - URL\n - URL\n - URL\n\nWe do not provide a la... |
text-generation | transformers |
## dialogGPT-homer-simpson
This model has been fine tuned with the entire scripts of Homer Simpson from the T.V. show The Simpsons
It will give some nice answers seemingly from Homers brain in the Simpsons Universe during single turn conversation, letting you chat to Homer Simpson
## A State-of-the-Art Large-scale P... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | shalpin87/dialoGPT-homer-simpson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"arxiv:1911.00536",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T19:28:40+00:00 | [
"1911.00536"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| dialogGPT-homer-simpson
-----------------------
This model has been fine tuned with the entire scripts of Homer Simpson from the T.V. show The Simpsons
It will give some nice answers seemingly from Homers brain in the Simpsons Universe during single turn conversation, letting you chat to Homer Simpson
A State-of-th... | [
"### How to use Multi-Turn",
"#### NOTE: Multi-Turn seems to be broken, after a few exchanges the output will mostly be exclamation marks.\n\n\nNow we are ready to try out how the model works as a chatting partner!",
"### How to use Single Turn\n\n\nSample Output"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use Multi-Turn",
"#### NOTE: Multi-Turn seems to be broken, after a few exchanges the output will mostly be excla... |
null | null | Private sample code for running categorisation on the mT5X | {} | pere/eu-jav-categorisation | null | [
"region:us"
] | null | 2022-03-29T19:40:09+00:00 | [] | [] | TAGS
#region-us
| Private sample code for running categorisation on the mT5X | [] | [
"TAGS\n#region-us \n"
] |
null | null | test
| {} | ptorru/mymodel | null | [
"region:us"
] | null | 2022-03-29T20:01:08+00:00 | [] | [] | TAGS
#region-us
| test
| [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsOneSent")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsOneSent")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-lau... | {} | BigSalmon/PointsOneSent | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T20:19:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
It should also be able to do all that this can: URL | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
token-classification | transformers | # bert-base-spanish-wwm-uncased-finetuned-NER-medical
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an adaptation of [eHealth-KD Challenge 2020 dataset](https://knowledge-learning.github.io/ehealthkd-2020/), filtered o... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "El \u00fatero o matriz es el lugar donde se desarrolla el beb\u00e9 cuando una mujer est\u00e1 embarazada."}, {"text": "El s\u00edndrome de dolor regional complejo es un trastorno de dolor cr\u00f3nico."}], "... | fmmolina/bert-base-spanish-wwm-uncased-finetuned-NER-medical | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-29T20:37:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-spanish-wwm-uncased-finetuned-NER-medical
===================================================
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an adaptation of eHealth-KD Challenge 2020 dataset, filtered only for the task of NER. The dataset annotations for NER are ['Concept'... | [
"### 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: 12",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #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: 1... |
text-classification | transformers |
# DistilBERT (uncased) for FaceNews Classification
This model is a classification model built by fine-tuning
[DistilBERT base model](https://huggingface.co/distilbert-base-uncased).
This model was trained using
[fake-and-real-news-dataset](https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset)
for five ep... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | anwarvic/distilbert-base-uncased-for-fakenews | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:56:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #exbert #en #dataset-bookcorpus #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# DistilBERT (uncased) for FaceNews Classification
This model is a classification model built by fine-tuning
DistilBERT base model.
This model was trained using
fake-and-real-news-dataset
for five epochs.
> NOTE:
This model is just a POC (proof-of-concept) for a fellowship I was applying for.
## Intended uses & lim... | [
"# DistilBERT (uncased) for FaceNews Classification\n\nThis model is a classification model built by fine-tuning\nDistilBERT base model.\nThis model was trained using\nfake-and-real-news-dataset\nfor five epochs.\n\n> NOTE:\nThis model is just a POC (proof-of-concept) for a fellowship I was applying for.",
"## In... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #exbert #en #dataset-bookcorpus #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT (uncased) for FaceNews Classification\n\nThis model is a classification model built by fine-tuning\nDistil... |
text-classification | transformers | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset MNLI. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the m... | {} | princeton-nlp/CoFi-MNLI-s95 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:57:29+00:00 | [
"2204.00408"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us
| This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from 'bert-base-uncased' to a 95% sparsity on dataset MNLI. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in our rep... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset MNLI. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the m... | {} | princeton-nlp/CoFi-MNLI-s60 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:58:04+00:00 | [
"2204.00408"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us
| This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from 'bert-base-uncased' to a 60% sparsity on dataset MNLI. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in our rep... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset QNLI. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the m... | {} | princeton-nlp/CoFi-QNLI-s95 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:58:10+00:00 | [
"2204.00408"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us
| This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from 'bert-base-uncased' to a 95% sparsity on dataset QNLI. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in our rep... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset QNLI. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the m... | {} | princeton-nlp/CoFi-QNLI-s60 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:58:20+00:00 | [
"2204.00408"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us
| This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from 'bert-base-uncased' to a 60% sparsity on dataset QNLI. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in our rep... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 93% sparsity on dataset SQuAD 1.1. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use ... | {} | princeton-nlp/CoFi-SQuAD-s93 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2204.00408",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:58:37+00:00 | [
"2204.00408"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2204.00408 #endpoints_compatible #region-us
| This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from 'bert-base-uncased' to a 93% sparsity on dataset SQuAD 1.1. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in ou... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2204.00408 #endpoints_compatible #region-us \n"
] |
question-answering | transformers | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset SQuAD 1.1. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use ... | {} | princeton-nlp/CoFi-SQuAD-s60 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2204.00408",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:58:46+00:00 | [
"2204.00408"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2204.00408 #endpoints_compatible #region-us
| This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from 'bert-base-uncased' to a 60% sparsity on dataset SQuAD 1.1. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in ou... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2204.00408 #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset SST-2. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the ... | {} | princeton-nlp/CoFi-SST2-s95 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:58:56+00:00 | [
"2204.00408"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us
| This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from 'bert-base-uncased' to a 95% sparsity on dataset SST-2. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in our re... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset SST-2. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the ... | {} | princeton-nlp/CoFi-SST2-s60 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T20:59:02+00:00 | [
"2204.00408"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us
| This is a model checkpoint for "Structured Pruning Learns Compact and Accurate Models". The model is pruned from 'bert-base-uncased' to a 60% sparsity on dataset SST-2. Please go to our repository for more details on how to use the model for inference. Note that you would have to use the model class specified in our re... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2204.00408 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsToSentence")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsToSentence")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the ... | {} | BigSalmon/PointsToSentence | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-29T21:58:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
It should also be able to do all that this can: URL
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #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 Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/classificationEsp2_Attraction
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/classificationEsp2_Attraction", "results": []}]} | javilonso/classificationEsp2_Attraction | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-29T22:17:31+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/classificationEsp2\_Attraction
========================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-large-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9927
* Validation Loss: 0.9926
* Epoch: 2
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 35916, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
image-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. -->
# vit-base-patch16-224-in21k-finetuned-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cifar10"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-in21k-finetuned-cifar10", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cifar10", "type": "cifar10"... | aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-29T23:18:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| vit-base-patch16-224-in21k-finetuned-cifar10
============================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2564
* Accuracy: 0.9788
Model description
-----------------
... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln33")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln33")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy... | {} | BigSalmon/InformalToFormalLincoln33 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T00:19:07+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
table-question-answering | transformers |
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraini... | {"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"], "datasets": ["wikitablequestions"]} | microsoft/tapex-base-finetuned-wtq | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2107.07653",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-30T00:25:28+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikitablequestions #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| TAPEX (base-sized model)
========================
TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.
Model description
-----------------
TAPEX (Table Pre-tr... | [
"### How to Use\n\n\nHere is how to use this model in transformers:",
"### How to Eval\n\n\nPlease find the eval script here.",
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikitablequestions #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to Use\n\n\nHere is how to use this model in transformers:",
"###... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | Fredvv/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T00:32:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4454
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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. -->
# finetuning-financial-news-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/disti... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-financial-news-sentiment", "results": []}]} | samayash/finetuning-financial-news-sentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T02:27:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-financial-news-sentiment
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3345
- Accuracy: 0.8751
- F1: 0.8751
## Model description
More information needed
## Intended uses & limitations
More inform... | [
"# finetuning-financial-news-sentiment\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3345\n- Accuracy: 0.8751\n- F1: 0.8751",
"## Model description\n\nMore information needed",
"## Intended uses & limita... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-financial-news-sentiment\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achie... |
text-generation | transformers |
# nlp-waseda/gpt2-small-japanese
This model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task.
Note that the texts should be segmented into words using Juman++ in advance.
### ... | {"language": ["ja"], "license": "cc-by-sa-4.0", "datasets": ["wikipedia", "cc100"], "widget": [{"text": "\u65e9\u7a32\u7530 \u5927\u5b66 \u3067 \u81ea\u7136 \u8a00\u8a9e \u51e6\u7406 \u3092"}]} | nlp-waseda/gpt2-small-japanese | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T02:34:11+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ja #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# nlp-waseda/gpt2-small-japanese
This model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task.
Note that the texts should be segmented into words using Juman++ in advance.
### ... | [
"# nlp-waseda/gpt2-small-japanese\r\n\r\nThis model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100.",
"## Intended uses & limitations\r\n\r\nYou can use the raw model for text generation or fine-tune it to a downstream task.\r\n\r\nNote that the texts should be segmented into words using Juman++ in... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ja #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# nlp-waseda/gpt2-small-japanese\r\n\r\nThis model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100.... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds-sample-2ep-29mar
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-sample-2ep-29mar", "results": []}]} | mimicheng/codeparrot-ds-sample-2ep-29mar | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T02:41:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| codeparrot-ds-sample-2ep-29mar
==============================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6283
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n... |
image-classification | transformers |
# roomidentifier
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/huggi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | lazyturtl/roomidentifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T03:10:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# roomidentifier
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
#### Bathroom
!Bathroom
#### Bedroom
!Bedroom
#### DinningRoom
!DinningRoom
#### Kitchen
!Kitchen
#### ... | [
"# roomidentifier\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",
"#### Bathroom\n\n!Bathroom",
"#### Bedroom\n\n!Bedroom",
"#### DinningRoom\n\n!DinningRoom... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# roomidentifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issue... |
automatic-speech-recognition | transformers |
Convert from model .pt to transformer
Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h
Bash:
```bash
pip install transformers[sentencepiece]
pip install fairseq -U
git clone https://github.com/huggingface/transformers.git
cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_c... | {"language": "vi", "license": "cc-by-nc-4.0", "tags": ["audio", "speech", "Transformer"], "datasets": ["vivos", "common_voice"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "Wav2vec2 NCKH Vietnamese 2022", "results": [{"task": {"type": "automatic-speech-recognition", "na... | hoangbinhmta99/wav2vec-NCKH-2022 | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"audio",
"speech",
"Transformer",
"automatic-speech-recognition",
"vi",
"dataset:vivos",
"dataset:common_voice",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T03:39:46+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #audio #speech #Transformer #automatic-speech-recognition #vi #dataset-vivos #dataset-common_voice #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us
|
Convert from model .pt to transformer
Link: URL
Bash:
# install and upload model
| [
"# install and upload model"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #audio #speech #Transformer #automatic-speech-recognition #vi #dataset-vivos #dataset-common_voice #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us \n",
"# install and upload model"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds-sample-gpt-small-neo
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/Eleuth... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-sample-gpt-small-neo", "results": []}]} | Pavithra/codeparrot-ds-sample-gpt-small-neo | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T04:57:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# codeparrot-ds-sample-gpt-small-neo
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trai... | [
"# codeparrot-ds-sample-gpt-small-neo\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeparrot-ds-sample-gpt-small-neo\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.",
"## Model d... |
text-generation | transformers |
# Overview
The CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. From Salesforce Research.
The abstract from the paper is the following:
Program synthesis strives to generate a computer program as a solution to a given proble... | {"license": "bsd-3-clause"} | shpotes/codegen-350M-mono | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T05:37:21+00:00 | [] | [] | TAGS
#transformers #pytorch #codegen #text-generation #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
|
# Overview
The CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. From Salesforce Research.
The abstract from the paper is the following:
Program synthesis strives to generate a computer program as a solution to a given proble... | [
"# Overview \nThe CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. From Salesforce Research.\n\nThe abstract from the paper is the following:\nProgram synthesis strives to generate a computer program as a solution to a give... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us \n",
"# Overview \nThe CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. From Salesforce ... |
token-classification | transformers |
This model is the pretrained infoxlm 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 configuration files from the original... | {"language": ["es", "fr", "ru", "en", "it"], "license": "mit", "tags": ["token-classification", "fill-mask"], "datasets": ["iit-cdip"]} | manu/lilt-infoxlm-base | null | [
"transformers",
"pytorch",
"liltrobertalike",
"fill-mask",
"token-classification",
"es",
"fr",
"ru",
"en",
"it",
"dataset:iit-cdip",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T06:26:57+00:00 | [] | [
"es",
"fr",
"ru",
"en",
"it"
] | TAGS
#transformers #pytorch #liltrobertalike #fill-mask #token-classification #es #fr #ru #en #it #dataset-iit-cdip #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
This model is the pretrained infoxlm 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 the original repository, and load the p... | [] | [
"TAGS\n#transformers #pytorch #liltrobertalike #fill-mask #token-classification #es #fr #ru #en #it #dataset-iit-cdip #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/classificationPolEsp1
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggi... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/classificationPolEsp1", "results": []}]} | javilonso/classificationPolEsp1 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T06:49:20+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| javilonso/classificationPolEsp1
===============================
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3728
* Validation Loss: 0.6217
* Epoch: 2
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 17958, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {... |
text-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... | neibla/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 | 2022-03-30T07:22:55+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: 0.2187
* Accuracy: 0.9255
* F1: 0.9255
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-therapist-small
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-ne... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "gpt-neo-therapist-small", "results": []}]} | arampacha/gpt-neo-therapist-small | null | [
"transformers",
"pytorch",
"tensorboard",
"onnx",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-30T07:40:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #onnx #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| gpt-neo-therapist-small
=======================
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6731
* Rouge1: 39.5028
* Rouge2: 6.43
* Rougel: 24.0091
* Rougelsum: 35.4481
* Gen Len: 204.1329
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 24\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.98) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #onnx #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #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... |
fill-mask | transformers |
# DistilProtBert
A distilled version of [ProtBert-UniRef100](https://huggingface.co/Rostlab/prot_bert) model.
In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective and it only works with capital letter amino acids.
Check out our ... | {"license": "mit", "tags": ["protein language model"], "datasets": ["Uniref50"]} | yarongef/DistilProtBert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"protein language model",
"dataset:Uniref50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T09:07:23+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #protein language model #dataset-Uniref50 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| DistilProtBert
==============
A distilled version of ProtBert-UniRef100 model.
In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective and it only works with capital letter amino acids.
Check out our paper DistilProtBert: A distille... | [
"### How to use\n\n\nThe model can be used the same as ProtBert and with ProtBert's tokenizer.\n\n\nTraining data\n-------------\n\n\nDistilProtBert model was pretrained on Uniref50, a dataset consisting of ~43 million protein sequences (only sequences of length between 20 to 512 amino acids were used).\n\n\nPretra... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #protein language model #dataset-Uniref50 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nThe model can be used the same as ProtBert and with ProtBert's tokenizer.\n\n\nTraining data\n-------------\n\n\nDistilProtBert mod... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/classificationEsp3_Attraction
This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/Pla... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/classificationEsp3_Attraction", "results": []}]} | javilonso/classificationEsp3_Attraction | null | [
"transformers",
"tf",
"gpt2",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T10:07:40+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| javilonso/classificationEsp3\_Attraction
========================================
This model is a fine-tuned version of PlanTL-GOB-ES/gpt2-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0055
* Validation Loss: 0.0515
* Epoch: 2
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 17958, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #gpt2 #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
text-classification | transformers | Fake news classifier
This model trains a text classification model to detect fake news articles,
it uses distilbert-base-uncased-finetuned-sst-2-english pretrained model to work on
fake and real news dataset from kaggle (https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset) | {} | yinde/dummy-model | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T10:37:44+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Fake news classifier
This model trains a text classification model to detect fake news articles,
it uses distilbert-base-uncased-finetuned-sst-2-english pretrained model to work on
fake and real news dataset from kaggle (URL | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | fairseq |
# MTee translation model for legal domain
A legal 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 information a... | {"language": ["et", "en", "de", "ru"], "tags": ["translation", "modularNMT", "fairseq", "MTee", "legal"], "inference": false} | tartuNLP/mtee-legal | null | [
"fairseq",
"translation",
"modularNMT",
"MTee",
"legal",
"et",
"en",
"de",
"ru",
"region:us"
] | null | 2022-03-30T11:28:26+00:00 | [] | [
"et",
"en",
"de",
"ru"
] | TAGS
#fairseq #translation #modularNMT #MTee #legal #et #en #de #ru #region-us
| MTee translation model for legal domain
=======================================
A legal 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 about t... | [] | [
"TAGS\n#fairseq #translation #modularNMT #MTee #legal #et #en #de #ru #region-us \n"
] |
translation | fairseq |
# MTee translation model for crisis domain
A crisis (mostly healthcare-related) 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://... | {"language": ["et", "en", "de", "ru"], "tags": ["translation", "modularNMT", "fairseq", "MTee", "crisis"], "inference": false} | tartuNLP/mtee-crisis | null | [
"fairseq",
"translation",
"modularNMT",
"MTee",
"crisis",
"et",
"en",
"de",
"ru",
"region:us"
] | null | 2022-03-30T11:29:04+00:00 | [] | [
"et",
"en",
"de",
"ru"
] | TAGS
#fairseq #translation #modularNMT #MTee #crisis #et #en #de #ru #region-us
| MTee translation model for crisis domain
========================================
A crisis (mostly healthcare-related) 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 ... | [] | [
"TAGS\n#fairseq #translation #modularNMT #MTee #crisis #et #en #de #ru #region-us \n"
] |
translation | fairseq |
# MTee translation model for military domain
A military 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 informa... | {"language": ["et", "en", "de", "ru"], "tags": ["translation", "modularNMT", "fairseq", "MTee", "military"], "inference": false} | tartuNLP/mtee-military | null | [
"fairseq",
"translation",
"modularNMT",
"MTee",
"military",
"et",
"en",
"de",
"ru",
"region:us"
] | null | 2022-03-30T11:29:20+00:00 | [] | [
"et",
"en",
"de",
"ru"
] | TAGS
#fairseq #translation #modularNMT #MTee #military #et #en #de #ru #region-us
| MTee translation model for military domain
==========================================
A military 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 informatio... | [] | [
"TAGS\n#fairseq #translation #modularNMT #MTee #military #et #en #de #ru #region-us \n"
] |
text-classification | transformers |
# Readability ES Sentences for two classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using se... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "bertin"], "pipeline_tag": "text-classification", "widget": [{"text": "La ciencia nos ense\u00f1a, en efecto, a someter nuestra raz\u00f3n a la verdad y a conocer y juzgar las cosas como son, es decir, como ellas mismas eligen ser y no como quisi... | hackathon-pln-es/readability-es-sentences | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"spanish",
"bertin",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-30T11:30:08+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Readability ES Sentences for two classes
Model based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary cla... | [
"# Readability ES Sentences for two classes\n\nModel based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.",
"## Description and performance\n\nThis version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model perform... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Readability ES Sentences for two classes\n\nModel based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanis... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base_toy_train_data_random_high_pass
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_random_high_pass", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_random_high_pass | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T12:17:36+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_random\_high\_pass
===================================================
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: 1.2841
* Wer: 0.7222
Model description
-----------------
More... | [
"### 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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# javilonso/classificationPolEsp2
This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/PlanTL-GOB-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/classificationPolEsp2", "results": []}]} | javilonso/classificationPolEsp2 | null | [
"transformers",
"tf",
"gpt2",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T12:41:58+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| javilonso/classificationPolEsp2
===============================
This model is a fine-tuned version of PlanTL-GOB-ES/gpt2-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1229
* Validation Loss: 0.8172
* Epoch: 2
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 17958, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #gpt2 #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2hindiasr
This model is a fine-tuned version of [theainerd/Wav2Vec2-large-xlsr-hindi](https://huggingface.co/theainerd/Wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2hindiasr", "results": []}]} | SAGAR4REAL/wav2vec2hindiasr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T13:51:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2hindiasr
This model is a fine-tuned version of theainerd/Wav2Vec2-large-xlsr-hindi 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
### Trai... | [
"# wav2vec2hindiasr\n\nThis model is a fine-tuned version of theainerd/Wav2Vec2-large-xlsr-hindi 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",
"## Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2hindiasr\n\nThis model is a fine-tuned version of theainerd/Wav2Vec2-large-xlsr-hindi on the common_voice dataset.",... |
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... | royam0820/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 | 2022-03-30T13:56:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2157
* Accuracy: 0.9265
* F1: 0.9267
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-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/1508824472924659725/267f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tojibaceo/1654229333065/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tojibaceo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T14:11:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tojiba CPU Corp (,)
@tojibaceo
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# distilgpt2-nepali
This model is pre-trained on [nepalitext](https://huggingface.co/datasets/Sakonii/nepalitext-language-model-dataset) dataset consisting of over 13 million Nepali text sequences using a Causal language modeling (CLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokenization ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": "Sakonii/nepalitext-language-model-dataset", "widget": [{"text": "\u0928\u0947\u092a\u093e\u0932 \u0930 \u092d\u093e\u0930\u0924\u092c\u0940\u091a", "example_title": "Example 1"}, {"text": "\u092a\u094d\u0930\u0927\u093e\u0928\u092e\u0928\u094d\u... | Sakonii/distilgpt2-nepali | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:Sakonii/nepalitext-language-model-dataset",
"arxiv:1911.02116",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T14:40:23+00:00 | [
"1911.02116"
] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #dataset-Sakonii/nepalitext-language-model-dataset #arxiv-1911.02116 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-nepali
=================
This model is pre-trained on nepalitext dataset consisting of over 13 million Nepali text sequences using a Causal language modeling (CLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokenization similar to XLM-ROBERTa and trains distilgpt2 for language mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #dataset-Sakonii/nepalitext-language-model-dataset #arxiv-1911.02116 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following... |
text-classification | transformers |
# Readability ES Sentences for three classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using ... | {"language": "es", "license": "cc-by-4.0", "tags": ["spanish", "roberta", "bertin"], "pipeline_tag": "text-classification", "widget": [{"text": "Las L\u00edneas de Nazca son una serie de marcas trazadas en el suelo, cuya anchura oscila entre los 40 y los 110 cent\u00edmetros."}, {"text": "Hace mucho tiempo, en el gran ... | hackathon-pln-es/readability-es-3class-sentences | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"spanish",
"bertin",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-30T16:35:24+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Readability ES Sentences for three classes
Model based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs classifi... | [
"# Readability ES Sentences for three classes\n\nModel based on the Roberta architecture finetuned on BERTIN for readability assessment of Spanish texts.",
"## Description and performance\n\nThis version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model perfo... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #spanish #bertin #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Readability ES Sentences for three classes\n\nModel based on the Roberta architecture finetuned on BERTIN for readability asses... |
sentence-similarity | sentence-transformers |
# paraphrase-spanish-distilroberta
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.
We follow a **teacher-student** transfer learning approach to train an `bertin-robert... | {"language": ["es"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["hackathon-pln-es/parallel-sentences"], "pipeline_tag": "sentence-similarity", "widget": [{"text": "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos."}, {"text": "... | hackathon-pln-es/paraphrase-spanish-distilroberta | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"es",
"dataset:hackathon-pln-es/parallel-sentences",
"arxiv:2004.09813",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-30T16:58:23+00:00 | [
"2004.09813"
] | [
"es"
] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #es #dataset-hackathon-pln-es/parallel-sentences #arxiv-2004.09813 #endpoints_compatible #has_space #region-us
| paraphrase-spanish-distilroberta
================================
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.
We follow a teacher-student transfer learning approach to train an 'bertin-rob... | [
"### ES-ES",
"### ES-EN\n\n\n\n\n\n---\n\n\nIntended uses\n-------------\n\n\nOur model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures\nthe semantic information. The sentence vector may be used for information retrieval, clustering or sent... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #es #dataset-hackathon-pln-es/parallel-sentences #arxiv-2004.09813 #endpoints_compatible #has_space #region-us \n",
"### ES-ES",
"### ES-EN\n\n\n\n\n\n---\n\n\nIntended uses\n-------------\n\n\nOur model is in... |
text-generation | transformers |
# Homer DialoGPT Model half data | {"tags": ["conversational"]} | darthrussel/DialoGPT-small-homerbot-halfdata | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T18:35:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Homer DialoGPT Model half data | [
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] |
null | null | Upside down detection model for Fatima Fellowship Coding Challenge 2022 | {"license": "apache-2.0"} | misterekole/upside_down_detector | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-03-30T18:47:20+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Upside down detection model for Fatima Fellowship Coding Challenge 2022 | [] | [
"TAGS\n#license-apache-2.0 #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. -->
# poem-gen-spanish-t5-small-test
This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggin... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "poem-gen-spanish-t5-small-test", "results": []}]} | DrishtiSharma/poem-gen-spanish-t5-small-test | null | [
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"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T18:55:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| poem-gen-spanish-t5-small-test
==============================
This model is a fine-tuned version of hackathon-pln-es/poem-gen-spanish-t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2170
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 12",
"### Traini... | [
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text-classification | transformers |
This is an off-the-shelf roberta-large model finetuned on WANLI, the Worker-AI Collaborative NLI dataset ([Liu et al., 2022](https://aclanthology.org/2022.findings-emnlp.508/)). It outperforms the `roberta-large-mnli` model on eight out-of-domain test sets, including by 11% on HANS and 9% on Adversarial NLI.
### How ... | {"language": ["en"], "tags": ["text-classification"], "datasets": ["alisawuffles/WANLI"], "widget": [{"text": "I almost forgot to eat lunch.</s></s>I didn't forget to eat lunch."}, {"text": "I almost forgot to eat lunch.</s></s>I forgot to eat lunch."}, {"text": "I ate lunch.</s></s>I almost forgot to eat lunch."}]} | alisawuffles/roberta-large-wanli | null | [
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"dataset:alisawuffles/WANLI",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-30T19:00:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-alisawuffles/WANLI #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is an off-the-shelf roberta-large model finetuned on WANLI, the Worker-AI Collaborative NLI dataset (Liu et al., 2022). It outperforms the 'roberta-large-mnli' model on eight out-of-domain test sets, including by 11% on HANS and 9% on Adversarial NLI.
### How to use
| [
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] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-mnli-rte-wnli-5
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/ber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-mnli-rte-wnli-5", "results": []}]} | yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5 | null | [
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"region:us"
] | null | 2022-03-30T19:09:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-mnli-rte-wnli-5
===========================================
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: 0.4400
* Accuracy: 0.9209
Model description
-----------------
More information nee... | [
"### 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\n* mixed\\_prec... | [
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text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 688020754
- CO2 Emissions (in grams): 3.1151249696839685
## Validation Metrics
- Loss: 0.2810373902320862
- Accuracy: 0.8928571428571429
- Precision: 0.9272727272727272
- Recall: 0.8869565217391304
- AUC: 0.9500805152979066
- F1: 0.90... | {"language": "unk", "tags": "autotrain", "datasets": ["vlsb/autotrain-data-security-texts-classification-roberta"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.1151249696839685} | vlsb/autotrain-security-texts-classification-roberta-688020754 | null | [
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"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T19:52:41+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 688020754
- CO2 Emissions (in grams): 3.1151249696839685
## Validation Metrics
- Loss: 0.2810373902320862
- Accuracy: 0.8928571428571429
- Precision: 0.9272727272727272
- Recall: 0.8869565217391304
- AUC: 0.9500805152979066
- F1: 0.90... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 688020... |
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. -->
# fatimah_fake_news_bert
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "fatimah_fake_news_bert", "results": []}]} | yinde/fatimah_fake_news_bert | null | [
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T19:54:21+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| fatimah\_fake\_news\_bert
=========================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on Fake and real dataset on kaggle )
It achieves the following results on the evaluation set:
* Loss: 0.0010
* Accuracy: 0.9998
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 20\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 688220764
- CO2 Emissions (in grams): 2.0817207656772445
## Validation Metrics
- Loss: 0.3055502772331238
- Accuracy: 0.9030612244897959
- Precision: 0.9528301886792453
- Recall: 0.8782608695652174
- AUC: 0.9439076757917337
- F1: 0.91... | {"language": "unk", "tags": "autotrain", "datasets": ["vlsb/autotrain-data-security-texts-classification-distilroberta"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.0817207656772445} | vlsb/autotrain-security-texts-classification-distilroberta-688220764 | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-30T19:54:56+00:00 | [] | [
"unk"
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|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 688220764
- CO2 Emissions (in grams): 2.0817207656772445
## Validation Metrics
- Loss: 0.3055502772331238
- Accuracy: 0.9030612244897959
- Precision: 0.9528301886792453
- Recall: 0.8782608695652174
- AUC: 0.9439076757917337
- F1: 0.91... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 688320769
- CO2 Emissions (in grams): 3.670416179055797
## Validation Metrics
- Loss: 0.3046899139881134
- Accuracy: 0.8826530612244898
- Precision: 0.9181818181818182
- Recall: 0.8782608695652174
- AUC: 0.9423510466988727
- F1: 0.897... | {"language": "unk", "tags": "autotrain", "datasets": ["vlsb/autotrain-data-security-text-classification-albert"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.670416179055797} | vlsb/autotrain-security-text-classification-albert-688320769 | null | [
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] | null | 2022-03-30T19:55:59+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 688320769
- CO2 Emissions (in grams): 3.670416179055797
## Validation Metrics
- Loss: 0.3046899139881134
- Accuracy: 0.8826530612244898
- Precision: 0.9181818181818182
- Recall: 0.8782608695652174
- AUC: 0.9423510466988727
- F1: 0.897... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 688320769... |
image-classification | transformers | Hello world,
This model have been created in the context of ` Fatima Fellowship Programme`. The model was trained on the Cifar10 dataset with a googd final accuracy of arround 98%.
This model determines wether an image is flipped of not. | {} | mustapha/flipped-image-ViT | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T20:57:42+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #autotrain_compatible #endpoints_compatible #region-us
| Hello world,
This model have been created in the context of ' Fatima Fellowship Programme'. The model was trained on the Cifar10 dataset with a googd final accuracy of arround 98%.
This model determines wether an image is flipped of not. | [] | [
"TAGS\n#transformers #pytorch #vit #image-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
#Bot Chat | {"tags": ["conversational"]} | TheGoldenToaster/DialoGPT-medium-Woody | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-30T21:01:35+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Bot Chat | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #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. -->
# ascend_with_english
This model is a fine-tuned version of [GleamEyeBeast/ascend](https://huggingface.co/GleamEyeBeast/ascend) on... | {"tags": ["generated_from_trainer"], "datasets": ["timit_asr"], "model-index": [{"name": "ascend_with_english", "results": []}]} | GleamEyeBeast/ascend_with_english | null | [
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"automatic-speech-recognition",
"generated_from_trainer",
"dataset:timit_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-30T21:09:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-timit_asr #endpoints_compatible #region-us
| ascend\_with\_english
=====================
This model is a fine-tuned version of GleamEyeBeast/ascend on the timit\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3049
* Wer: 0.2251
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: 5\n* mixed\\_prec... | [
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text2text-generation | transformers |
# Model Card for brio-cnndm-uncased
# Model Details
## Model Description
Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary. ... | {"tags": ["text-2-text-generation", "bart"]} | Yale-LILY/brio-cnndm-uncased | null | [
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"has_space",
"region:us"
] | null | 2022-03-30T22:35:54+00:00 | [
"2203.16804",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #text-2-text-generation #arxiv-2203.16804 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Card for brio-cnndm-uncased
=================================
Model Details
=============
Model Description
-----------------
Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will ... | [
"### Preprocessing\n\n\nThe model creators note in the associated paper:\n\n\n\n> \n> We follow Kedzie et al. (2018) for data preprocessing and splitting, and use the associated archival abstracts as the summaries\n> \n> \n>",
"### Speeds, Sizes, Times\n\n\nMore information needed\n\n\nEvaluation\n==========\n\n\... | [
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"### Preprocessing\n\n\nThe model creators note in the associated paper:\n\n\n\n> \n> We follow Kedzie et al. (2018) for data... |
image-classification | transformers |
# roomclassifier
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/huggi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | lazyturtl/roomclassifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T00:09:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# roomclassifier
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
#### Bathroom
!Bathroom
#### Bedroom
!Bedroom
#### DinningRoom
!DinningRoom
#### Kitchen
!Kitchen
#### ... | [
"# roomclassifier\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",
"#### Bathroom\n\n!Bathroom",
"#### Bedroom\n\n!Bedroom",
"#### DinningRoom\n\n!DinningRoom... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# roomclassifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issue... |
text-generation | transformers |
# YuyuanQA-GPT2-3.5B
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理医疗问答任务，医疗的领域模型，英文版的GPT2。
Good at handling medical question answering tasks, a medical domain model, GPT2 in English.
## 模型分类 Model Ta... | {"language": ["en"], "license": "apache-2.0", "tags": ["QA", "medical", "gpt2"], "inference": {"parameters": {"temperature": 0.7, "top_p": 0.6, "max_new_tokens": 64, "num_return_sequences": 3, "do_sample": true}}, "widget": [{"text": "Question:What should gout patients pay attention to in diet? Answer:", "example_title... | IDEA-CCNL/YuyuanQA-GPT2-3.5B | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"QA",
"medical",
"en",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-31T01:06:39+00:00 | [
"2209.02970"
] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #QA #medical #en #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| YuyuanQA-GPT2-3.5B
==================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理医疗问答任务，医疗的领域模型，英文版的GPT2。
Good at handling medical question answering tasks, a medical domain model, GPT2 in English.
模型分类 Model Taxonomy
-------------------
模型信息 Model In... | [
"### 下游任务 Performance\n\n\n我们测试了该模型在未见过的100条QA对上的表现：\n\n\nWe tested the model on 100 unseen QA pairs:\n\n\n\n使用 Usage\n--------",
"### 加载模型 Loading Models",
"### 使用示例 Usage Examples",
"### 演示 Demo\n\n\n我们用该模型做了一个医疗问答演示。\n\n\nWe made a demo of medical QA system with this model.\n\n\n!avatar\n\n\n引用 Citation\n-... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #QA #medical #en #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### 下游任务 Performance\n\n\n我们测试了该模型在未见过的100条QA对上的表现：\n\n\nWe tested the model on 100 unseen QA pairs:\n\n\n\n使用 Usage\n... |
text-generation | transformers |
# George Costanza DialoGPT model | {"tags": ["conversational"]} | bemich/DialoGPT-small-GeorgeCostanza | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-31T02:02:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# George Costanza DialoGPT model | [
"# George Costanza DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# George Costanza DialoGPT model"
] |
text2text-generation | transformers | # Model Card for brio-xsum-cased
# Model Details
## Model Description
BRIO: Bringing Order to Abstractive Summarization
- **Developed by:** Yale LILY Lab
- **Shared by [Optional]:** Hugging Face
- **Model type:** PEGASUS
- **Language(s) (NLP):** Text2Text Generation
- **License:** More information needed
- **... | {"tags": ["pegasus"]} | Yale-LILY/brio-xsum-cased | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"arxiv:2203.16804",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-31T02:09:01+00:00 | [
"2203.16804",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #arxiv-2203.16804 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Card for brio-xsum-cased
==============================
Model Details
=============
Model Description
-----------------
BRIO: Bringing Order to Abstractive Summarization
* Developed by: Yale LILY Lab
* Shared by [Optional]: Hugging Face
* Model type: PEGASUS
* Language(s) (NLP): Text2Text Generation
* Lic... | [
"### Preprocessing\n\n\nThe model creators note in the associated paper\n\n\n\n> \n> We follow Kedzie et al. (2018) for data preprocessing and splitting, and use the associated archival abstracts as the summaries\n> \n> \n>",
"### Speeds, Sizes, Times\n\n\nMore information needed\n\n\nEvaluation\n==========\n\n\n... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #arxiv-2203.16804 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Preprocessing\n\n\nThe model creators note in the associated paper\n\n\n\n> \n> We follow Kedzie et al. (2018) for data preprocessing and spl... |
image-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. -->
# vit-base-patch16-224-in21k-finetuned-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cifar10"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-in21k-finetuned-cifar10", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cifar10", "type": "cifar10"... | tanlq/vit-base-patch16-224-in21k-finetuned-cifar10 | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-31T02:09:09+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| vit-base-patch16-224-in21k-finetuned-cifar10
============================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0503
* Accuracy: 0.9875
Model description
-----------------
... | [
"### 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: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
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. -->
# librispeech-100h-supervised-aug
This model is a fine-tuned version of [Kuray107/librispeech-5h-supervised](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "librispeech-100h-supervised-aug", "results": []}]} | Kuray107/librispeech-100h-supervised-aug | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T02:24:38+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| librispeech-100h-supervised-aug
===============================
This model is a fine-tuned version of Kuray107/librispeech-5h-supervised on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0776
* Wer: 0.0327
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #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: 32\n* eval\\_b... |
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. -->
# chinese-bert-wwm-finetuned-product
This model is a fine-tuned version of [hfl/chinese-bert-wwm](https://huggingface.co/hfl/chine... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "chinese-bert-wwm-finetuned-product", "results": []}]} | agdsga/chinese-bert-wwm-finetuned-product | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T02:29:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-bert-wwm-finetuned-product
==================================
This model is a fine-tuned version of hfl/chinese-bert-wwm on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... |
null | null | # Dummy model | {} | imanueldrexel/fake_news_detect | null | [
"region:us"
] | null | 2022-03-31T02:34:14+00:00 | [] | [] | TAGS
#region-us
| # Dummy model | [
"# Dummy model"
] | [
"TAGS\n#region-us \n",
"# Dummy model"
] |
text-classification | transformers | fake-news-classifier | {} | imanueldrexel/fake-news-classifier | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T02:40:50+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| fake-news-classifier | [] | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-commonvoice-tamil
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-tamil-tam-250](https://hugging... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-commonvoice-tamil", "results": []}]} | nikhil6041/wav2vec2-commonvoice-tamil | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T03:00:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-mit #endpoints_compatible #region-us
| wav2vec2-commonvoice-tamil
==========================
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-tamil-tam-250 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3415
* Wer: 1.0
Model description
-----------------
More information needed
... | [
"### 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-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_... |
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-commonvoice-hindi
This model is a fine-tuned version of [theainerd/Wav2Vec2-large-xlsr-hindi](https://huggingface.co/th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-commonvoice-hindi", "results": []}]} | nikhil6041/wav2vec2-commonvoice-hindi | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T03:27:46+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-commonvoice-hindi
==========================
This model is a fine-tuned version of theainerd/Wav2Vec2-large-xlsr-hindi on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9825
* Wer: 0.6763
Model description
-----------------
More information needed
Intend... | [
"### 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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 689220804
- CO2 Emissions (in grams): 39.40549299946679
## Validation Metrics
- Loss: 2.0426149368286133
- Rouge1: 54.9813
- Rouge2: 44.923
- RougeL: 54.0399
- RougeLsum: 54.2553
- Gen Len: 16.6211
## Usage
You can use cURL to access this m... | {"language": "unk", "tags": "autotrain", "datasets": ["unjustify/autotrain-data-IWant"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 39.40549299946679} | unjustify/autotrain-IWant-689220804 | null | [
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"unk",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-31T05:09:55+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-unjustify/autotrain-data-IWant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 689220804
- CO2 Emissions (in grams): 39.40549299946679
## Validation Metrics
- Loss: 2.0426149368286133
- Rouge1: 54.9813
- Rouge2: 44.923
- RougeL: 54.0399
- RougeLsum: 54.2553
- Gen Len: 16.6211
## Usage
You can use cURL to access this m... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 689220804\n- CO2 Emissions (in grams): 39.40549299946679",
"## Validation Metrics\n\n- Loss: 2.0426149368286133\n- Rouge1: 54.9813\n- Rouge2: 44.923\n- RougeL: 54.0399\n- RougeLsum: 54.2553\n- Gen Len: 16.6211",
"## Usage\n\nYou can ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-unjustify/autotrain-data-IWant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 689220804\n- CO2 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. -->
# distilbert-base-uncased_fakenews_identification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased_fakenews_identification", "results": []}]} | yaswanth/distilbert-base-uncased_fakenews_identification | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T05:10:15+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\_fakenews\_identification
=================================================
This model is a fine-tuned version of distilbert-base-uncased on the below dataset.
URL
It achieves the following results on the evaluation set:
* Loss: 0.0059
* Accuracy: 0.999
* F1: 0.9990
Label Description
-----... | [
"### 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 689620825
- CO2 Emissions (in grams): 20.656741915705204
## Validation Metrics
- Loss: 0.7315372824668884
- Accuracy: 0.6354949675117849
- Precision: 0.63792194092827
- Recall: 0.6191451241361658
- AUC: 0.6912165223485615
- F1: 0.6283... | {"language": "en", "tags": "autotrain", "datasets": ["unjustify/autotrain-data-commonsence"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 20.656741915705204} | unjustify/autotrain-commonsence-689620825 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:unjustify/autotrain-data-commonsence",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T05:18:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-unjustify/autotrain-data-commonsence #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 689620825
- CO2 Emissions (in grams): 20.656741915705204
## Validation Metrics
- Loss: 0.7315372824668884
- Accuracy: 0.6354949675117849
- Precision: 0.63792194092827
- Recall: 0.6191451241361658
- AUC: 0.6912165223485615
- F1: 0.6283... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 689620825\n- CO2 Emissions (in grams): 20.656741915705204",
"## Validation Metrics\n\n- Loss: 0.7315372824668884\n- Accuracy: 0.6354949675117849\n- Precision: 0.63792194092827\n- Recall: 0.6191451241361658\n- AUC: 0.69121652234... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-unjustify/autotrain-data-commonsence #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 689620825\n- CO2 Emission... |
feature-extraction | transformers |
# Motivation
This model is based on anferico/bert-for-patents - a BERT<sub>LARGE</sub> model (See next section for details below). By default, the pre-trained model's output embeddings with size 768 (base-models) or with size 1024 (large-models). However, when you store Millions of embeddings, this can require quite... | {"language": ["en"], "license": "apache-2.0", "tags": ["masked-lm", "pytorch"], "metrics": ["perplexity"], "pipeline-tag": "fill-mask", "mask-token": "[MASK]", "widget": [{"text": "The present [MASK] provides a torque sensor that is small and highly rigid and for which high production efficiency is possible."}, {"text"... | prithivida/bert-for-patents-64d | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"masked-lm",
"en",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-31T05:40:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #masked-lm #en #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Motivation
This model is based on anferico/bert-for-patents - a BERT<sub>LARGE</sub> model (See next section for details below). By default, the pre-trained model's output embeddings with size 768 (base-models) or with size 1024 (large-models). However, when you store Millions of embeddings, this can require quite... | [
"# Motivation\n\n\nThis model is based on anferico/bert-for-patents - a BERT<sub>LARGE</sub> model (See next section for details below). By default, the pre-trained model's output embeddings with size 768 (base-models) or with size 1024 (large-models). However, when you store Millions of embeddings, this can requir... | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #masked-lm #en #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Motivation\n\n\nThis model is based on anferico/bert-for-patents - a BERT<sub>LARGE</sub> model (See next section for details below). By default, the pre-trained model... |
feature-extraction | transformers | # KpfBERT
https://github.com/jinmang2/kpfbert | {} | jinmang2/kpfbert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T05:40:37+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #endpoints_compatible #region-us
| # KpfBERT
URL | [
"# KpfBERT\n\nURL"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #endpoints_compatible #region-us \n",
"# KpfBERT\n\nURL"
] |
token-classification | transformers | This is model fine tune from layoutlmv2 model for japanese and english language
| {} | thaind/layoutlmv2-jaen-gemai | null | [
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-31T06:38:07+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This is model fine tune from layoutlmv2 model for japanese and english language
| [] | [
"TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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