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feature-extraction | transformers | # BioBERT-NLI
This is the model [BioBERT](https://github.com/dmis-lab/biobert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) t... | {} | gsarti/biobert-nli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| BioBERT-NLI
===========
This is the model BioBERT [1] fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [2].
The model uses the original BERT wordpiece vocabulary and was trained using the average pooling strategy and a softmax loss.
... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # CovidBERT-NLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.... | {} | gsarti/covidbert-nli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| CovidBERT-NLI
=============
This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to ... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# Italian T5 Base (Oscar) ๐ฎ๐น
*This repository contains the model formerly known as `gsarti/t5-base-it`*
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach a... | {"language": ["it"], "license": "apache-2.0", "tags": ["seq2seq", "lm-head"], "datasets": ["oscar"], "inference": false} | gsarti/it5-base-oscar | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
"it",
"dataset:oscar",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.03759"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-oscar #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| Italian T5 Base (Oscar) ๐ฎ๐น
==========================
*This repository contains the model formerly known as 'gsarti/t5-base-it'*
The IT5 model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the origi... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-oscar #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Italian T5 Base ๐ฎ๐น
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-tra... | {"language": ["it"], "license": "apache-2.0", "tags": ["seq2seq", "lm-head"], "datasets": ["gsarti/clean_mc4_it"], "inference": false, "thumbnail": "https://gsarti.com/publication/it5/featured.png"} | gsarti/it5-base | null | [
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"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
"it",
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"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.03759"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
| Italian T5 Base ๐ฎ๐น
==================
The IT5 model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original T5 model.
This model is released as part of the project "IT5: Large-Scale Text-to-Text ... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Italian T5 Large ๐ฎ๐น
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-tr... | {"language": ["it"], "license": "apache-2.0", "tags": ["seq2seq", "lm-head"], "datasets": ["gsarti/clean_mc4_it"], "inference": false, "thumbnail": "https://gsarti.com/publication/it5/featured.png"} | gsarti/it5-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
"it",
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"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.03759"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| Italian T5 Large ๐ฎ๐น
===================
The IT5 model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original T5 model.
This model is released as part of the project "IT5: Large-Scale Text-to-Tex... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Italian T5 Small ๐ฎ๐น
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-tr... | {"language": ["it"], "license": "apache-2.0", "tags": ["seq2seq", "lm-head"], "datasets": ["gsarti/clean_mc4_it"], "inference": false, "thumbnail": "https://gsarti.com/publication/it5/featured.png"} | gsarti/it5-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
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"dataset:gsarti/clean_mc4_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.03759"
] | [
"it"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| Italian T5 Small ๐ฎ๐น
===================
The IT5 model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original T5 model.
This model is released as part of the project "IT5: Large-Scale Text-to-Tex... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | # SciBERT-NLI
This is the model [SciBERT](https://github.com/allenai/scibert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to... | {} | gsarti/scibert-nli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"doi:10.57967/hf/0038",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #doi-10.57967/hf/0038 #endpoints_compatible #region-us
| SciBERT-NLI
===========
This is the model SciBERT [1] fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [2].
The model uses the original 'scivocab' wordpiece vocabulary and was trained using the average pooling strategy and a softmax ... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #doi-10.57967/hf/0038 #endpoints_compatible #region-us \n"
] |
text-to-image | generic | ERROR: type should be string, got "\nhttps://github.com/borisdayma/dalle-mini" | {"language": ["en"], "library_name": "generic", "pipeline_tag": "text-to-image"} | gsurma/ai_dreamer | null | [
"generic",
"jax",
"bart",
"text-to-image",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#generic #jax #bart #text-to-image #en #region-us
|
URL | [] | [
"TAGS\n#generic #jax #bart #text-to-image #en #region-us \n"
] |
fill-mask | transformers | # dummy model
This is a dummy model | {} | gulabpatel/new-dummy-model | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # dummy model
This is a dummy model | [
"# dummy model\n\nThis is a dummy model"
] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy model\n\nThis is a dummy model"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | gullenasatish/wav2vec2-base-timit-demo-colab | null | [
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4872
* Wer: 0.3417
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
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. -->
# gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
This model was trained from scratch on an conll2003 dataset.
... | {"language": ["en", "de", "nl", "es", "multilingual"], "datasets": ["conll2003"], "metrics": [{"precision": 0.936}, {"recall": 0.9458}, {"f1": 0.9409}, {"accuracy": 0.9902}], "model-index": [{"name": "gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner", "results": [{"task": {"type": "ner", "name": "Name... | gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"de",
"nl",
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] | TAGS
#transformers #pytorch #distilbert #token-classification #en #de #nl #es #multilingual #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us
| gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
==================================================================
This model was trained from scratch on an conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0388
* Precision: 0.9360
* Recall: 0.9458
* F1: 0.9409... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tra... |
translation | transformers |
This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-es-zh](https://huggingface.co/Helsinki-NLP/opus-tatoeba-es-zh) on a dataset of legal domain constructed by the author himself.
# Intended uses & limitations
This model is the result of the master graduation thesis for the Tradumatics: Translation Tech... | {"language": ["es", "zh"], "license": "apache-2.0", "tags": ["translation"]} | guocheng98/HelsinkiNLP-FineTuned-Legal-es-zh | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es",
"zh"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #es #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| This model is a fine-tuned version of Helsinki-NLP/opus-tatoeba-es-zh on a dataset of legal domain constructed by the author himself.
Intended uses & limitations
===========================
This model is the result of the master graduation thesis for the Tradumatics: Translation Technologies program at the Autonomo... | [] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #es #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | # WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file [docs/docker/cuda10... | {} | guoqiang/WuDaoSailing | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102... | [
"# WudaoSailing\n\nWudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.",
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"## Get Started",
"### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images f... |
null | null | # WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file [docs/docker/cuda10... | {} | guoqiang/glm | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102... | [
"# WudaoSailing\n\nWudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.",
"## Get Started",
"### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/... | [
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"## Get Started",
"### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images f... |
text-classification | transformers | # Turkish News Text Classification
Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
# Dataset
Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classes.
Dataset can be ac... | {"language": "tr"} | gurkan08/bert-turkish-text-classification | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us
| # Turkish News Text Classification
Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
# Dataset
Dataset consists of 11 classes were obtained from URL The model was created using the most distinctive 6 classes.
Dataset can be accessed at URL
labe... | [
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"# Dataset\n\nDataset consists of 11 classes were obtained from URL The model was created using the most distinctive 6 classes.\n\nDataset can be accessed a... | [
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"# Dataset\n\nDataset consis... |
text-generation | transformers |
# Rick bot | {"tags": ["conversational"]} | gusintheshell/DialoGPT-small-rickbot | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick bot | [
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"# Rick bot"
] |
text2text-generation | transformers |
### Quantized BigScience's T0 3B with 8-bit weights
This is a version of [BigScience's T0](https://huggingface.co/bigscience/T0_3B) with 3 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**. Inspired by [GPT-J 8bit](https://hugg... | {"language": "fr", "license": "mit", "tags": ["en"], "datasets": ["bigscience/P3"]} | gustavecortal/T0_3B-8bit | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
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"fr",
"dataset:bigscience/P3",
"arxiv:2110.08207",
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"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08207"
] | [
"fr"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #fr #dataset-bigscience/P3 #arxiv-2110.08207 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
### Quantized BigScience's T0 3B with 8-bit weights
This is a version of BigScience's T0 with 3 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit.
Here's how to run it: ![colab](URL
This model can be easi... | [
"### Quantized BigScience's T0 3B with 8-bit weights\n\n\nThis is a version of BigScience's T0 with 3 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL\n\nThis model ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #fr #dataset-bigscience/P3 #arxiv-2110.08207 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Quantized BigScience's T0 3B with 8-bit weights\n\n\nThis is a version of BigScience's T0 with 3 billion... |
text-generation | transformers |
### Quantized Cedille/fr-boris with 8-bit weights
This is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**. Inspired by [GPT-J 8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit)... | {"language": "fr", "license": "mit", "tags": ["causal-lm", "fr"], "datasets": ["c4", "The Pile"]} | gustavecortal/fr-boris-8bit | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
### Quantized Cedille/fr-boris with 8-bit weights
This is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit.
Here's how to run it: ![colab](URL
This model ca... | [
"### Quantized Cedille/fr-boris with 8-bit weights\n\n\nThis is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL\n\nTh... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Quantized Cedille/fr-boris with 8-bit weights\n\n\nThis is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can gene... |
text-generation | transformers |
### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights
This is a version of [EleutherAI's GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-2.7B) with 2.7 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**. Inspired by [GPT-J... | {"language": "en", "license": "mit", "tags": ["causal-lm"], "datasets": ["The_Pile"]} | gustavecortal/gpt-neo-2.7B-8bit | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"en",
"dataset:The_Pile",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #dataset-The_Pile #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights
This is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit.
Here's how to run it: ![colab](URL
## Model D... | [
"### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights\n\n\nThis is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL",
... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #dataset-The_Pile #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights\n\n\nThis is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-ml
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on ml (Malayalam) using the [Indic TTS Malayalam Speech Corpus (via Kaggle)](https://www.kaggle.com/kavyamanohar/indic-tts-malayalam-speech-corpus), [Openslr Malayalam Speech Corpus](http:/... | {"language": "ml", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["Indic TTS Malayalam Speech Corpus", "Openslr Malayalam Speech Corpus", "SMC Malayalam Speech Corpus", "IIIT-H Indic Speech Databases"], "metrics": ["wer"], "model-index": [{"na... | gvs/wav2vec2-large-xlsr-malayalam | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ml",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ml"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ml #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-ml
Fine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The notebooks used to train model are available here. When using this model... | [
"# Wav2Vec2-Large-XLSR-53-ml\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The notebooks used to train model are available here. When using this... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ml #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-ml\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Ma... |
null | transformers | "5050_base_test"
| {} | gwkim22/5050_b_disc | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| "5050_base_test"
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | "test_5050"
| {} | gwkim22/5050_s_disc | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| "test_5050"
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | "domain_base_test"
| {} | gwkim22/domain_b_disc | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| "domain_base_test"
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | "domain_base2_disc_0719"
| {} | gwkim22/domain_base2_disc | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| "domain_base2_disc_0719"
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | "test_domain_only"
| {} | gwkim22/domain_s_disc | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| "test_domain_only"
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | "general_base_test"
| {} | gwkim22/general_b_disc | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| "general_base_test"
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | "general_test"
| {} | gwkim22/general_s_disc | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
| "general_test"
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}}]}]} | gwynethfae/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
null | null |
# MultiLingual CLIP
Multilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.
# Model Architecture
Multilingual CLIP was built using [OpenAI CLIP](https://github.com/openai/CLIP) model. I have used the same Vision encoder (ResNet... | {"language": "multilingual", "license": "mit", "tags": ["clip", "vision", "text"]} | gzomer/clip-multilingual | null | [
"clip",
"vision",
"text",
"multilingual",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#clip #vision #text #multilingual #license-mit #has_space #region-us
|
# MultiLingual CLIP
Multilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.
# Model Architecture
Multilingual CLIP was built using OpenAI CLIP model. I have used the same Vision encoder (ResNet 50x4), but instead I replaced the... | [
"# MultiLingual CLIP\n\nMultilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.",
"# Model Architecture\nMultilingual CLIP was built using OpenAI CLIP model. I have used the same Vision encoder (ResNet 50x4), but instead I re... | [
"TAGS\n#clip #vision #text #multilingual #license-mit #has_space #region-us \n",
"# MultiLingual CLIP\n\nMultilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.",
"# Model Architecture\nMultilingual CLIP was built using Ope... |
text-generation | transformers | hello
| {} | ha-mulan/moby-dick | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #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. -->
# egy-slang-model
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "egy-slang-model", "results": []}]} | habiba/egy-slang-model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| egy-slang-model
===============
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: 2.9273
* Wer: 1.0000
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch... |
fill-mask | transformers | This is a test! | {} | hackertec/dummy2 | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This is a test! | [] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi-taller
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https:/... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi-taller", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_... | hackertec/roberta-base-bne-finetuned-amazon_reviews_multi-taller | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-amazon\_reviews\_multi-taller
========================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2463
* Accuracy: 0.9113
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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
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. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggin... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews... | hackertec/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2557
* Accuracy: 0.9085
Model description
--... | [
"### 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 #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
text-classification | null |
# Test
| {"license": "afl-3.0", "tags": ["es", "bert"], "pipeline_tag": "text-classification", "widget": [{"text": "Mi nombre es Omar", "exdample_title": "Example 1"}, {"text": "Otra prueba", "example_title": "Test"}]} | hackertec9/test | null | [
"es",
"bert",
"text-classification",
"license:afl-3.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#es #bert #text-classification #license-afl-3.0 #region-us
|
# Test
| [
"# Test"
] | [
"TAGS\n#es #bert #text-classification #license-afl-3.0 #region-us \n",
"# Test"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | hady/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
feature-extraction | transformers | Github: https://github.com/haisongzhang/roberta-tiny-cased
| {} | haisongzhang/roberta-tiny-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us
| Github: URL
| [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertweet-base-SNS_BRANDS_100k
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-SNS_BRANDS_100k", "results": []}]} | haji2438/bertweet-base-SNS_BRANDS_100k | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-base-SNS\_BRANDS\_100k
===============================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0483
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_... |
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. -->
# bertweet-base-SNS_BRANDS_200k
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-SNS_BRANDS_200k", "results": []}]} | haji2438/bertweet-base-SNS_BRANDS_200k | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-base-SNS\_BRANDS\_200k
===============================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0243
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #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: 8\n* eval\\_batch\\_... |
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. -->
# bertweet-base-SNS_BRANDS_50k
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-b... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-SNS_BRANDS_50k", "results": []}]} | haji2438/bertweet-base-SNS_BRANDS_50k | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-base-SNS\_BRANDS\_50k
==============================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0490
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_... |
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. -->
# bertweet-base-finetuned-IGtext
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-finetuned-IGtext", "results": []}]} | haji2438/bertweet-base-finetuned-IGtext | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-base-finetuned-IGtext
==============================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0334
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: 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* num\\_epochs: 4",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #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: 32\n* eval\\_batch\\... |
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. -->
# bertweet-base-finetuned-SNS-brand-personality
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-finetuned-SNS-brand-personality", "results": []}]} | haji2438/bertweet-base-finetuned-SNS-brand-personality | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bertweet-base-finetuned-SNS-brand-personality
=============================================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0498
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #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: 8\n* eval\\_batch\\_... |
text-generation | transformers |
# XLNet-japanese
## Model description
This model require Mecab and senetencepiece with XLNetTokenizer.
See details https://qiita.com/mkt3/items/4d0ae36f3f212aee8002
This model uses NFKD as the normalization method for character encoding.
Japanese muddle marks and semi-muddle marks will be lost.
*ๆฅๆฌ่ชใฎๆฟ็นใปๅๆฟ็นใใชใใขใใซใงใ*... | {"language": ["ja"], "license": ["apache-2.0"], "tags": ["xlnet", "lm-head", "causal-lm"], "datasets": ["Japanese_Business_News"]} | hajime9652/xlnet-japanese | null | [
"transformers",
"pytorch",
"xlnet",
"text-generation",
"lm-head",
"causal-lm",
"ja",
"dataset:Japanese_Business_News",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #xlnet #text-generation #lm-head #causal-lm #ja #dataset-Japanese_Business_News #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# XLNet-japanese
## Model description
This model require Mecab and senetencepiece with XLNetTokenizer.
See details URL
This model uses NFKD as the normalization method for character encoding.
Japanese muddle marks and semi-muddle marks will be lost.
*ๆฅๆฌ่ชใฎๆฟ็นใปๅๆฟ็นใใชใใขใใซใงใ*
#### How to use
#### Limitations and bias... | [
"# XLNet-japanese",
"## Model description\nThis model require Mecab and senetencepiece with XLNetTokenizer.\nSee details URL\n\nThis model uses NFKD as the normalization method for character encoding.\nJapanese muddle marks and semi-muddle marks will be lost.\n\n*ๆฅๆฌ่ชใฎๆฟ็นใปๅๆฟ็นใใชใใขใใซใงใ*",
"#### How to use",
"####... | [
"TAGS\n#transformers #pytorch #xlnet #text-generation #lm-head #causal-lm #ja #dataset-Japanese_Business_News #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLNet-japanese",
"## Model description\nThis model require Mecab and senetencepiece with XLNetTokenizer.\nSee details U... |
text-generation | transformers | This model has been initialized with random values. It is supposed to be used for the purpose of debugging. | {} | hakurei/gpt-j-random-tinier | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us
| This model has been initialized with random values. It is supposed to be used for the purpose of debugging. | [] | [
"TAGS\n#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling
Lit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is [GPT-Neo 125M](ht... | {"language": ["en"], "license": "mit", "tags": ["pytorch", "causal-lm"]} | hakurei/lit-125M | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling
Lit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is GPT-Neo 125M, whi... | [
"# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling\n\nLit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-Ne... | [
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"# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling\n\nLit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, ero... |
null | transformers |
# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is [GPT-J](https://github.co... | {"language": ["en"], "license": "mit", "tags": ["pytorch", "causal-lm"]} | hakurei/lit-6B-8bit | null | [
"transformers",
"pytorch",
"causal-lm",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #causal-lm #en #license-mit #endpoints_compatible #region-us
|
# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is GPT-J, which is a 6 billi... | [
"# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-J, which i... | [
"TAGS\n#transformers #pytorch #causal-lm #en #license-mit #endpoints_compatible #region-us \n",
"# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-... |
text-generation | transformers |
# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is [GPT-J](https://github.co... | {"language": ["en"], "license": "mit", "tags": ["pytorch", "causal-lm"]} | hakurei/lit-6B | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is GPT-J, which is a 6 billi... | [
"# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-J, which i... | [
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"# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and a... |
text-generation | transformers | # DOC DialoGPT Model | {"tags": ["conversational"]} | hama/Doctor_Bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # DOC DialoGPT Model | [
"# DOC DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DOC DialoGPT Model"
] |
text-generation | transformers | # Harry Potter DialoGPT Model | {"tags": ["conversational"]} | hama/Harry_Bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
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"# Harry Potter DialoGPT Model"
] |
text-generation | transformers | # BArney DialoGPT Model | {"tags": ["conversational"]} | hama/barney_bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # BArney DialoGPT Model | [
"# BArney DialoGPT Model"
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"# BArney DialoGPT Model"
] |
text-generation | transformers |
# me 101 | {"tags": ["conversational"]} | hama/me0.01 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# me 101 | [
"# me 101"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# me 101"
] |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | hama/rick_bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
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"# Rick and Morty DialoGPT Model"
] |
text2text-generation | transformers | # mBart50 for Zeroshot Azerbaijani-Turkish Translation
The mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performance. | {} | hamishs/mBART50-en-az-tr1 | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # mBart50 for Zeroshot Azerbaijani-Turkish Translation
The mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performance. | [
"# mBart50 for Zeroshot Azerbaijani-Turkish Translation\nThe mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performanc... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBart50 for Zeroshot Azerbaijani-Turkish Translation\nThe mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied repre... |
null | null | hello
| {} | hamxxxa/SBert | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| hello
| [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-small-discriminator-finetuned-squad
This model is a fine-tuned version of [google/electra-small-discriminator](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "electra-small-discriminator-finetuned-squad", "results": []}]} | hankzhong/electra-small-discriminator-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| electra-small-discriminator-finetuned-squad
===========================================
This model is a fine-tuned version of google/electra-small-discriminator on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2174
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #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... |
fill-mask | transformers | ## Not yet | {} | hansgun/model_test | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## Not yet | [
"## Not yet"
] | [
"TAGS\n#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## Not yet"
] |
text2text-generation | transformers | # Helsinki-NLP/opus-mt-en-vi
- This model is a fine-tune checkpoint of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi).
- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data.
# Fine-tuning hyper-parameters
- learning_rate = 1e-4
- batch_size = 4
- ... | {} | haotieu/en-vi-mt-model | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Helsinki-NLP/opus-mt-en-vi
- This model is a fine-tune checkpoint of Helsinki-NLP/opus-mt-en-vi.
- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data.
# Fine-tuning hyper-parameters
- learning_rate = 1e-4
- batch_size = 4
- num_train_epochs = 3.0 | [
"# Helsinki-NLP/opus-mt-en-vi\n- This model is a fine-tune checkpoint of Helsinki-NLP/opus-mt-en-vi.\n- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data.",
"# Fine-tuning hyper-parameters\n- learning_rate = 1e-4\n- batch_size = 4\n- num_train_epochs = 3.0"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Helsinki-NLP/opus-mt-en-vi\n- This model is a fine-tune checkpoint of Helsinki-NLP/opus-mt-en-vi.\n- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Viet... |
feature-extraction | sentence-transformers |
# multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search,... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "feature-extraction"} | haqishen/test-mode-fe | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
| multi-qa-MiniLM-L6-cos-v1
=========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, hav... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, ... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"####... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | hark99/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1642
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ingredients
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ingredients_yes_no"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ingredients", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "datase... | harr/distilbert-base-uncased-finetuned-ingredients | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:ingredients_yes_no",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-ingredients_yes_no #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-uncased-finetuned-ingredients
=============================================
This model is a fine-tuned version of distilbert-base-uncased on the ingredients\_yes\_no dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0105
* Precision: 0.9899
* Recall: 0.9932
* F1: 0.9915
* A... | [
"### 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-ingredients_yes_no #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... |
null | null | Simple Sentiment Ananlysis | {} | harsh2040/sentiment_ananlysis | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Simple Sentiment Ananlysis | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-LV60-TIMIT
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)
on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (with... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["timit_asr"]} | harshit345/wav2vec2-large-lv60-timit | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"en",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-LV60-TIMIT
Fine-tuned facebook/wav2vec2-large-lv60
on the timit_asr dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
Here's the output:
## Fine-Tuning Script
You can find the s... | [
"# Wav2Vec2-Large-LV60-TIMIT\n\nFine-tuned facebook/wav2vec2-large-lv60\non the timit_asr dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:\n\n\n\nHere's the output:",
"## Fine-Tuning Script... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-LV60-TIMIT\n\nFine-tuned facebook/wav2vec2-large-lv60\non the timit_asr dataset.\nWhen using this model, make sure that your ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on greek using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10 Greek: Single Speaker Speech Dataset](https://www.kaggle.com/bryanpark/greek-single-speaker-sp... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "V XLSR Wav2Vec2 Large 53 - greek", "results": [{"task": {"type": "automatic-speech-recognition", "name": "S... | harshit345/xlsr-53-wav2vec-greek | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"el",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-Large-XLSR-53-greek
============================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on greek using the Common Voice and CSS10 Greek: Single Speaker Speech Dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
Usage
-----
The model can be used directly (without a la... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-hindi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html).
When using this mode... | {"language": "hi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["Interspeech 2021"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Hindi by Shyam Sunder Kumar", "results": [{"task": {"type": "automatic-speech-recognition", "nam... | harshit345/xlsr-53-wav2vec-hi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hi",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-hindi
Fine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) ... | [
"# Wav2Vec2-Large-XLSR-53-hindi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a lan... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-hindi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR c... |
audio-classification | transformers | ~~~
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
~~~
# prediction
~~~
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transforme... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "audio-classification", "speech"], "datasets": ["aesdd"]} | harshit345/xlsr-wav2vec-speech-emotion-recognition | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio",
"audio-classification",
"speech",
"en",
"dataset:aesdd",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #audio #audio-classification #speech #en #dataset-aesdd #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
```
# requirement packages
!pip install git+URL
!pip install git+URL
!pip install torchaudio
!pip install librosa
```
prediction
==========
```
import torch
import URL as nn
import URL.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
import librosa
import IPython.di... | [
"# requirement packages\n!pip install git+URL\n!pip install git+URL\n!pip install torchaudio\n!pip install librosa\n\n\n```\n\nprediction\n==========\n\n\n\n```\nimport torch\nimport URL as nn\nimport URL.functional as F\nimport torchaudio\nfrom transformers import AutoConfig, Wav2Vec2FeatureExtractor\nimport libro... | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio #audio-classification #speech #en #dataset-aesdd #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# requirement packages\n!pip install git+URL\n!pip install git+URL\n!pip install torchaudio\n!pip install librosa\n\n\n```\n\nprediction\n==========... |
automatic-speech-recognition | transformers |
# Wav2vec2-Large-English
Fine-tuned [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on English using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (wi... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "Wav2Vec2 English by Jonatas Grosman", "results": [{"task": {"type": "automatic-speech-recognition", "name":... | harshit345/xlsr_wav2vec_english | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"en",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #en #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2vec2-Large-English
======================
Fine-tuned facebook/wav2vec2-large on English using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
Usage
-----
The model can be used directly (without a language model) as follows...
Using the ASRecognition library:
... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #en #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
## EsperBERTo: RoBERTa-like Language model trained on Esperanto | {"language": "eo", "thumbnail": "https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png", "widget": [{"text": "\u0108u vi paloras la <mask> Esperanto?"}]} | hashk1/EsperBERTo-malgranda | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"eo",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #eo #autotrain_compatible #endpoints_compatible #region-us
|
## EsperBERTo: RoBERTa-like Language model trained on Esperanto | [
"## EsperBERTo: RoBERTa-like Language model trained on Esperanto"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #eo #autotrain_compatible #endpoints_compatible #region-us \n",
"## EsperBERTo: RoBERTa-like Language model trained on Esperanto"
] |
token-classification | transformers | # Arabic Named Entity Recognition Model
Pretrained BERT-based ([arabic-bert-base](https://huggingface.co/asafaya/bert-base-arabic)) Named Entity Recognition model for Arabic.
The pre-trained model can recognize the following entities:
1. **PERSON**
- ู ูุฐุง ู
ุง ููุงู ุงูู
ุนุงูู ุงูุณูุงุณู ููุฑุฆูุณ ***ูุจูู ุจุฑู*** ุ ุงููุงุฆุจ ***ุน... | {"language": "ar"} | hatmimoha/arabic-ner | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"token-classification",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #token-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Arabic Named Entity Recognition Model
Pretrained BERT-based (arabic-bert-base) Named Entity Recognition model for Arabic.
The pre-trained model can recognize the following entities:
1. PERSON
- ู ูุฐุง ู
ุง ููุงู ุงูู
ุนุงูู ุงูุณูุงุณู ููุฑุฆูุณ *ูุจูู ุจุฑู* ุ ุงููุงุฆุจ *ุนูู ุญุณู ุฎููู*
- ููู ุฃูุณุงุท *ุงูุญุฑูุฑู* ุชุนุชุจุฑ ุฃูู ุถุญู ูุซูุฑุง ูู... | [
"# Arabic Named Entity Recognition Model\n\nPretrained BERT-based (arabic-bert-base) Named Entity Recognition model for Arabic.\n\nThe pre-trained model can recognize the following entities:\n1. PERSON\n\n- ู ูุฐุง ู
ุง ููุงู ุงูู
ุนุงูู ุงูุณูุงุณู ููุฑุฆูุณ *ูุจูู ุจุฑู* ุ ุงููุงุฆุจ *ุนูู ุญุณู ุฎููู* \n\n- ููู ุฃูุณุงุท *ุงูุญุฑูุฑู* ุชุนุชุจุฑ ุฃู... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Arabic Named Entity Recognition Model\n\nPretrained BERT-based (arabic-bert-base) Named Entity Recognition model for Arabic.\n\nThe pre-trained model can re... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | hchc/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8508
* Matthews Correlation: 0.5452
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | hcjang1987/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8657
* Matthews Correlation: 0.5472
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | hcy11/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2131
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
null | null |
# Fun with transformers | {"license": "mit"} | hcy11/transformer | null | [
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#license-mit #region-us
|
# Fun with transformers | [
"# Fun with transformers"
] | [
"TAGS\n#license-mit #region-us \n",
"# Fun with transformers"
] |
text-classification | transformers | Technique Classification for https://propaganda.qcri.org/ptc/index.html | {} | hd10/semeval2020_task11_tc | null | [
"transformers",
"pytorch",
"deberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Technique Classification for URL | [] | [
"TAGS\n#transformers #pytorch #deberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# diablo GPT random | {"tags": ["conversational"]} | heabeoun/DiabloGPT-small-nuon-conv | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# diablo GPT random | [
"# diablo GPT random"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# diablo GPT random"
] |
null | transformers | DPR context encoder for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details.
Load with:
```python
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
ctx_encoder = DPRContextEncoder.from_pretrained('healx/biomedical-dpr-ctx-encoder')
ctx_tokenizer = DPRContextEncoderTokeniz... | {} | healx/biomedical-dpr-ctx-encoder | null | [
"transformers",
"pytorch",
"dpr",
"arxiv:2109.08564",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.08564"
] | [] | TAGS
#transformers #pytorch #dpr #arxiv-2109.08564 #endpoints_compatible #region-us
| DPR context encoder for Biomedical slot filling see URL for details.
Load with:
| [] | [
"TAGS\n#transformers #pytorch #dpr #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | DPR query encoder for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details.
Load with:
```python
from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizerFast
qry_encoder = DPRQuestionEncoder.from_pretrained('healx/biomedical-dpr-qry-encoder')
qry_tokenizer = DPRQuestionEncoderToken... | {} | healx/biomedical-dpr-qry-encoder | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"arxiv:2109.08564",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.08564"
] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #arxiv-2109.08564 #endpoints_compatible #region-us
| DPR query encoder for Biomedical slot filling see URL for details.
Load with:
| [] | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
question-answering | transformers | Reader model for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details. The model is initialized with [biobert-base](https://huggingface.co/dmis-lab/biobert-v1.1). | {} | healx/biomedical-slot-filling-reader-base | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2109.08564",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.08564"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us
| Reader model for Biomedical slot filling see URL for details. The model is initialized with biobert-base. | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
question-answering | transformers | Reader model for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details. The model is initialized with [biobert-large](https://huggingface.co/dmis-lab/biobert-large-cased-v1.1). | {} | healx/biomedical-slot-filling-reader-large | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2109.08564",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.08564"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us
| Reader model for Biomedical slot filling see URL for details. The model is initialized with biobert-large. | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
null | transformers | GPT-2 (774M model) finetuned on 0.5m PubMed abstracts. Used in the [writemeanabstract.com](writemeanabstract.com) and the following preprint:
[Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).](https://arxiv.org/abs/2004.13845)
| {} | healx/gpt-2-pubmed-large | null | [
"transformers",
"pytorch",
"arxiv:2004.13845",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13845"
] | [] | TAGS
#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #region-us
| GPT-2 (774M model) finetuned on 0.5m PubMed abstracts. Used in the URL and the following preprint:
Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).
| [] | [
"TAGS\n#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #region-us \n"
] |
null | transformers | GPT-2 (355M model) finetuned on 0.5m PubMed abstracts. Used in the [writemeanabstract.com](writemeanabstract.com) and the following preprint:
[Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).](https://arxiv.org/abs/2004.13845)
| {} | healx/gpt-2-pubmed-medium | null | [
"transformers",
"pytorch",
"arxiv:2004.13845",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13845"
] | [] | TAGS
#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #has_space #region-us
| GPT-2 (355M model) finetuned on 0.5m PubMed abstracts. Used in the URL and the following preprint:
Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).
| [] | [
"TAGS\n#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 202661
## Validation Metrics
- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micr... | {"language": "es", "tags": "autonlp", "datasets": ["hectorcotelo/autonlp-data-spanish_songs"], "widget": [{"text": "Y si me tomo una cerveza Vuelves a mi cabeza Y empiezo a recordarte Es que me gusta c\u00f3mo besas Con tu delicadeza Puede ser que T\u00fa y yo, somos el uno para el otro Que no dejo de pensarte Quise ol... | hectorcotelo/autonlp-spanish_songs-202661 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"es",
"dataset:hectorcotelo/autonlp-data-spanish_songs",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #es #dataset-hectorcotelo/autonlp-data-spanish_songs #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 202661
## Validation Metrics
- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micr... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 202661",
"## Validation Metrics\n\n- Loss: 1.5369086265563965\n- Accuracy: 0.30762817840766987\n- Macro F1: 0.28034259092597485\n- Micro F1: 0.30762817840766987\n- Weighted F1: 0.28072818168048186\n- Macro Precision: 0.31138... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #es #dataset-hectorcotelo/autonlp-data-spanish_songs #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 202661",
"## Validation Metrics\n\n- Loss: ... |
null | null | Trying out Hugging Face | {} | hegdeashwin/test-model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Trying out Hugging Face | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
## Offensive Language Detection Model in Turkish
- uses Bert and pytorch
- fine tuned with Twitter data.
- UTF-8 configuration is done
### Training Data
Number of training sentences: 31,277
**Example Tweets**
- 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|
- 30525 @USER Bak biri kollarฤฑmda uy... | {"language": "tr", "widget": [{"text": "sevelim sevilelim bu dunya kimseye kalmaz"}]} | hemekci/off_detection_turkish | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us
| Offensive Language Detection Model in Turkish
---------------------------------------------
* uses Bert and pytorch
* fine tuned with Twitter data.
* UTF-8 configuration is done
### Training Data
Number of training sentences: 31,277
Example Tweets
* 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|
* 3... | [
"### Training Data\n\n\nNumber of training sentences: 31,277\n\n\nExample Tweets\n\n\n* 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|\n* 30525 @USER Bak biri kollarฤฑmda uyuyup gitmem diyor..|NOT|\n* 26468 Helal olsun be :) Norveรงten sabaha karลฤฑ geldi aq... | OFF|\n* 14105 @USER Sunu cekecek ve gรผzel old... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training Data\n\n\nNumber of training sentences: 31,277\n\n\nExample Tweets\n\n\n* 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|\n* 30525 @USER Bak biri kollarฤฑmda uyuyup g... |
question-answering | transformers |
# Multilingual + Dutch SQuAD2.0
This model is the multilingual model provided by the Google research team with a fine-tuned dutch Q&A downstream task.
## Details of the language model
Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multilingual.md)):
12... | {"language": "nl"} | henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"nl",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #nl #endpoints_compatible #region-us
| Multilingual + Dutch SQuAD2.0
=============================
This model is the multilingual model provided by the Google research team with a fine-tuned dutch Q&A downstream task.
Details of the language model
-----------------------------
Language model (bert-base-multilingual-cased):
12-layer, 768-hidden, 12-hea... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #nl #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# Multilingual + Polish SQuAD1.1
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
## Details of the language model
Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multilingual.md)):
12-layer... | {"language": "pl"} | henryk/bert-base-multilingual-cased-finetuned-polish-squad1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"pl",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #region-us
| Multilingual + Polish SQuAD1.1
==============================
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
Details of the language model
-----------------------------
Language model (bert-base-multilingual-cased):
12-layer, 768-hidden, 12-... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# Multilingual + Polish SQuAD2.0
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
## Details of the language model
Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multilingual.md)):
12-layer... | {"language": "pl"} | henryk/bert-base-multilingual-cased-finetuned-polish-squad2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"pl",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #has_space #region-us
| Multilingual + Polish SQuAD2.0
==============================
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
Details of the language model
-----------------------------
Language model (bert-base-multilingual-cased):
12-layer, 768-hidden, 12-... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | henryoce/DialoGPT-small-rick-and-morty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
summarization | transformers |
## `t5-3b-samsum-deepspeed`
This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-3b` checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, updates coming soon: 7/6/21~7/... | {"language": "en", "license": "apache-2.0", "tags": ["azureml", "t5", "summarization", "deepspeed"], "datasets": ["samsum"], "widget": [{"text": "Henry: Hey, is Nate coming over to watch the movie tonight?\nKevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out ... | henryu-lin/t5-3b-samsum-deepspeed | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 't5-3b-samsum-deepspeed'
------------------------
This model was trained using Microsoft's 'AzureML' and 'DeepSpeed''s ZeRO 2 optimization. It was fine-tuned on the 'SAMSum' corpus from 't5-3b' checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, updat... | [
"### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). \n\nCodeCarbon: URL\n\n\n\nHyperparameters\n---------------\n\n\n\\*Same 'per device batch size' for evaluations",
"### DeepSpeed\n\n\nOp... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is... |
summarization | transformers |
## `t5-large-samsum-deepspeed`
This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-large` checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, major updates coming soo... | {"language": "en", "license": "apache-2.0", "tags": ["azureml", "t5", "summarization", "deepspeed"], "datasets": ["samsum"], "widget": [{"text": "Kevin: Hey man, are you excited to watch Finding Nemo tonight?\nHenry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch it with us t... | henryu-lin/t5-large-samsum-deepspeed | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 't5-large-samsum-deepspeed'
---------------------------
This model was trained using Microsoft's 'AzureML' and 'DeepSpeed''s ZeRO 2 optimization. It was fine-tuned on the 'SAMSum' corpus from 't5-large' checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still W... | [
"### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). \n\nCodeCarbon: URL\n\n\n\nHyperparameters\n---------------\n\n\n\\*Same 'per device batch size' for evaluations",
"### DeepSpeed\n\n\nOp... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | hervetusse/DialogGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text2text-generation | transformers | # T5-base for paraphrase generation
Google's T5-base fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.
<!-- ## Model fine-tuning -->
<!-- The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_... | {"language": "en", "datasets": ["tapaco"]} | hetpandya/t5-base-tapaco | null | [
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"text2text-generation",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5-base for paraphrase generation
Google's T5-base fine-tuned on TaPaCo dataset for paraphrasing.
## Model in Action
## Output
Created by Het Pandya/@hetpandya | LinkedIn
Made with <span style="color: red;">♥</span> in India | [
"# T5-base for paraphrase generation\n\nGoogle's T5-base fine-tuned on TaPaCo dataset for paraphrasing.",
"## Model in Action",
"## Output\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-base for paraphrase generation\n\nGoogle's T5-base fine-tuned on TaPaCo dataset for paraphrasing.",
"## Model in Action",
"## Ou... |
text2text-generation | transformers | # T5-small for paraphrase generation
Google's T5-small fine-tuned on [Quora Question Pairs](https://huggingface.co/datasets/quora) dataset for paraphrasing.
## Model in Action ๐
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-qu... | {"language": "en", "datasets": ["quora"]} | hetpandya/t5-small-quora | null | [
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"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:quora",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-quora #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5-small for paraphrase generation
Google's T5-small fine-tuned on Quora Question Pairs dataset for paraphrasing.
## Model in Action
## Output
Created by Het Pandya/@hetpandya | LinkedIn
Made with <span style="color: red;">♥</span> in India | [
"# T5-small for paraphrase generation\n\nGoogle's T5-small fine-tuned on Quora Question Pairs dataset for paraphrasing.",
"## Model in Action",
"## Output\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-quora #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-small for paraphrase generation\n\nGoogle's T5-small fine-tuned on Quora Question Pairs dataset for paraphrasing.",
"## Model in Action",
"## ... |
text2text-generation | transformers | # T5-small for paraphrase generation
Google's T5 small fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.
## Model in Action ๐
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-tapaco")
model ... | {"language": "en", "datasets": ["tapaco"]} | hetpandya/t5-small-tapaco | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:tapaco",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5-small for paraphrase generation
Google's T5 small fine-tuned on TaPaCo dataset for paraphrasing.
## Model in Action
## Output
## Model fine-tuning
Please find my guide on fine-tuning the model here:
URL
Created by Het Pandya/@hetpandya | LinkedIn
Made with <span style="color: red;">♥</span> in I... | [
"# T5-small for paraphrase generation\n\nGoogle's T5 small fine-tuned on TaPaCo dataset for paraphrasing.",
"## Model in Action",
"## Output",
"## Model fine-tuning\nPlease find my guide on fine-tuning the model here:\n\nURL\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: re... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-small for paraphrase generation\n\nGoogle's T5 small fine-tuned on TaPaCo dataset for paraphrasing.",
"## Model in Action",
"## Output",
"#... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Swedish - CV8", "results": ... | hf-test/xls-r-300m-sv-cv8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_c... | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
Without LM:
* Wer: 0.2465
* Cer: 0.0717
With LM:
* Wer: 0.1710
* Cer: 0.0569
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training... |
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. -->
# XLS-R-300m-SV
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-... | {"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "hello", "model_for_talk", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "sv"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-3... | hf-test/xls-r-300m-sv | null | [
"transformers",
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hello",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"sv",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apach... | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hello #model_for_talk #mozilla-foundation/common_voice_7_0 #robust-speech-event #sv #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us... | XLS-R-300m-SV
=============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3171
* Wer: 0.2468
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hello #model_for_talk #mozilla-foundation/common_voice_7_0 #robust-speech-event #sv #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #reg... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATI... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | hf-test/xls-r-ab-test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8787
- Wer: 1.3460
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8787\n- Wer: 1.3460",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_... |
token-classification | transformers | # BERT base model (uncased) fine-tuned on CoNLL-2003
This model was trained following the PyTorch token-classification example from Hugging Face: https://github.com/huggingface/transformers/tree/master/examples/pytorch/token-classification.
There were no tweaks to the model or dataset.
| {} | hfeng/bert_base_uncased_conll2003 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # BERT base model (uncased) fine-tuned on CoNLL-2003
This model was trained following the PyTorch token-classification example from Hugging Face: URL
There were no tweaks to the model or dataset.
| [
"# BERT base model (uncased) fine-tuned on CoNLL-2003\n\nThis model was trained following the PyTorch token-classification example from Hugging Face: URL\n\nThere were no tweaks to the model or dataset."
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base model (uncased) fine-tuned on CoNLL-2003\n\nThis model was trained following the PyTorch token-classification example from Hugging Face: URL\n\nThere were no tweaks to the model or da... |
fill-mask | transformers | ## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Ya... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-bert-wwm-ext | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08101",
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository... | [
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-... |
fill-mask | transformers | ## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Ya... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-bert-wwm | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08101",
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository... | [
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-... |
null | transformers |
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-electra-180g-base-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | [
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor fu... | [
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called E... |
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