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text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_tiny_roberta_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | A french sequence to sequence pretrained model based on [BART](https://huggingface.co/facebook/bart-large). <br>
BARThez is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 66GB of french raw text is used to carry out the pretraining. <br>
Unlike already existing BERT-based French language ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["summarization"], "pipeline_tag": "fill-mask"} | moussaKam/mbarthez | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"fill-mask",
"fr",
"arxiv:2010.12321",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12321"
] | [
"fr"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #fill-mask #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| A french sequence to sequence pretrained model based on BART.
BARThez is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 66GB of french raw text is used to carry out the pretraining.
Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT, BARThez i... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #fill-mask #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# GPT-CSRC
This is a GPT2 774M model trained on the C/C++ code of the top 10,000 most popular packages in Debian, according to the [Debian Popularity Contest](https://popcon.debian.org/). The source files were deduplicated using a process similar to the OpenWebText preprocessing (basically a locality-sensitive hash t... | {"language": "code", "license": "cc0-1.0", "tags": ["programming", "gpt2", "causal-lm"], "thumbnail": "https://doesnotexist.codes/messlab.png"} | moyix/csrc_774m | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"programming",
"causal-lm",
"code",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #programming #causal-lm #code #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT-CSRC
This is a GPT2 774M model trained on the C/C++ code of the top 10,000 most popular packages in Debian, according to the Debian Popularity Contest. The source files were deduplicated using a process similar to the OpenWebText preprocessing (basically a locality-sensitive hash to detect near-duplicates). The... | [
"# GPT-CSRC\n\nThis is a GPT2 774M model trained on the C/C++ code of the top 10,000 most popular packages in Debian, according to the Debian Popularity Contest. The source files were deduplicated using a process similar to the OpenWebText preprocessing (basically a locality-sensitive hash to detect near-duplicates... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #programming #causal-lm #code #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-CSRC\n\nThis is a GPT2 774M model trained on the C/C++ code of the top 10,000 most popular packages in Debian, according... |
null | asteroid |
## Asteroid model
Imported from this Zenodo [model page](https://zenodo.org/record/3970768).
## Description:
This model was trained by Brij Mohan using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on the `enh_single` task of the Libri3Mix dataset.
## Training config:
```yaml
data:
n_src: 1
... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet"], "datasets": ["LibriMix", "enh_single"]} | mpariente/ConvTasNet_Libri1Mix_enhsingle_8k | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"dataset:LibriMix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #dataset-LibriMix #dataset-enh_single #license-cc-by-sa-4.0 #region-us
|
## Asteroid model
Imported from this Zenodo model page.
## Description:
This model was trained by Brij Mohan using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri3Mix dataset.
## Training config:
## Results:
## License notice:
This work "ConvTasNet_Libri1M... | [
"## Asteroid model\nImported from this Zenodo model page.",
"## Description:\nThis model was trained by Brij Mohan using the Librimix/ConvTasNet recipe in Asteroid. \nIt was trained on the 'enh_single' task of the Libri3Mix dataset.",
"## Training config:",
"## Results:",
"## License notice:\nThis work \"C... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #dataset-LibriMix #dataset-enh_single #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model\nImported from this Zenodo model page.",
"## Description:\nThis model was trained by Brij Mohan using the Librimix/ConvTasNet recipe in Asteroid. \nIt was trained on the '... |
audio-to-audio | asteroid |
## Asteroid model
Imported from this Zenodo [model page](https://zenodo.org/record/4020529).
## Description:
This model was trained by Takhir Mirzaev using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on the `sep_noisy` task of the Libri3Mix dataset.
## Training config:
```yaml
data:
n_src: 3
... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["LibriMix", "sep_noisy"]} | mpariente/ConvTasNet_Libri3Mix_sepnoisy | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:LibriMix",
"dataset:sep_noisy",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-LibriMix #dataset-sep_noisy #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model
Imported from this Zenodo model page.
## Description:
This model was trained by Takhir Mirzaev using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on the 'sep_noisy' task of the Libri3Mix dataset.
## Training config:
## Results:
## License notice:
This work "ConvTasNet_Libr... | [
"## Asteroid model\nImported from this Zenodo model page.",
"## Description:\nThis model was trained by Takhir Mirzaev using the Librimix/ConvTasNet recipe in Asteroid. \nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.",
"## Training config:",
"## Results:",
"## License notice:\nThis work ... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-LibriMix #dataset-sep_noisy #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model\nImported from this Zenodo model page.",
"## Description:\nThis model was trained by Takhir Mirzaev using the Librimix/ConvTasNet recipe in Aster... |
audio-to-audio | asteroid |
## Asteroid model `mpariente/ConvTasNet_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)
### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! da... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["wham", "sep_clean"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}]} | mpariente/ConvTasNet_WHAM_sepclean | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:wham",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-wham #dataset-sep_clean #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'mpariente/ConvTasNet_WHAM_sepclean'
Imported from Zenodo
### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasNet recipe in Asteroid.
It was trained on the 'sep_clean' task of the WHAM! dataset.
### Training config:
### Results:
### License notice:
This work "ConvT... | [
"## Asteroid model 'mpariente/ConvTasNet_WHAM_sepclean'\nImported from Zenodo",
"### Description:\nThis model was trained by Manuel Pariente \nusing the wham/ConvTasNet recipe in Asteroid.\nIt was trained on the 'sep_clean' task of the WHAM! dataset.",
"### Training config:",
"### Results:",
"### License no... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-wham #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'mpariente/ConvTasNet_WHAM_sepclean'\nImported from Zenodo",
"### Description:\nThis model was trained by Manuel Pariente \nusing the wham/ConvTasNet recipe in A... |
audio-to-audio | asteroid |
## Asteroid model `mpariente/DPRNNTasNet-ks2_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)
### Description:
This model was trained by Manuel Pariente
using the wham/DPRNN recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! da... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "DPRNNTasNet", "audio-to-audio"], "datasets": ["wham", "sep_clean"]} | mpariente/DPRNNTasNet-ks2_WHAM_sepclean | null | [
"asteroid",
"pytorch",
"audio",
"DPRNNTasNet",
"audio-to-audio",
"dataset:wham",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #DPRNNTasNet #audio-to-audio #dataset-wham #dataset-sep_clean #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'mpariente/DPRNNTasNet-ks2_WHAM_sepclean'
Imported from Zenodo
### Description:
This model was trained by Manuel Pariente
using the wham/DPRNN recipe in Asteroid.
It was trained on the 'sep_clean' task of the WHAM! dataset.
### Training config:
### Results:
### License notice:
This work "DPRNN... | [
"## Asteroid model 'mpariente/DPRNNTasNet-ks2_WHAM_sepclean'\nImported from Zenodo",
"### Description:\nThis model was trained by Manuel Pariente \nusing the wham/DPRNN recipe in Asteroid.\nIt was trained on the 'sep_clean' task of the WHAM! dataset.",
"### Training config:",
"### Results:",
"### License no... | [
"TAGS\n#asteroid #pytorch #audio #DPRNNTasNet #audio-to-audio #dataset-wham #dataset-sep_clean #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'mpariente/DPRNNTasNet-ks2_WHAM_sepclean'\nImported from Zenodo",
"### Description:\nThis model was trained by Manuel Pariente \nusing the wham/DPRNN... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-cv6-turkish
## Model description
This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 6.1 TR](... | {"language": "tr", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "hf-asr-leaderboard", "robust-speech-event", "tr"], "datasets": ["common_voice"], "model-index": [{"name": "mpoyraz/wav2vec2-xls-r-300m-cv6-turkish", "results": [{"task": {"type": "automatic-speech-recognition", "name":... | mpoyraz/wav2vec2-xls-r-300m-cv6-turkish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"hf-asr-leaderboard",
"robust-speech-event",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #hf-asr-leaderboard #robust-speech-event #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-cv6-turkish
===============================
Model description
-----------------
This ASR model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on Turkish language.
Training and evaluation data
----------------------------
The following datasets were used for finetuning:
* Common Vo... | [
"### Training hyperparameters\n\n\nThe following hypermaters were used for finetuning:\n\n\n* learning\\_rate 2e-4\n* num\\_train\\_epochs 10\n* warmup\\_steps 500\n* freeze\\_feature\\_extractor\n* mask\\_time\\_prob 0.1\n* mask\\_feature\\_prob 0.1\n* feat\\_proj\\_dropout 0.05\n* attention\\_dropout 0.05\n* fina... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #hf-asr-leaderboard #robust-speech-event #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hypermaters were used for finetuning:\n\n\n... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-cv7-turkish
## Model description
This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 7.0 TR](... | {"language": "tr", "license": "cc-by-4.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "tr"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "mpoyraz/wav2vec2-xls-r-300m-cv7-turkish", "results": [{"task": {"t... | mpoyraz/wav2vec2-xls-r-300m-cv7-turkish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"tr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:u... | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_7_0 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-300m-cv7-turkish
===============================
Model description
-----------------
This ASR model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on Turkish language.
Training and evaluation data
----------------------------
The following datasets were used for finetuning:
* Common Vo... | [
"### Training hyperparameters\n\n\nThe following hypermaters were used for finetuning:\n\n\n* learning\\_rate 2e-4\n* num\\_train\\_epochs 10\n* warmup\\_steps 500\n* freeze\\_feature\\_extractor\n* mask\\_time\\_prob 0.1\n* mask\\_feature\\_prob 0.05\n* feat\\_proj\\_dropout 0.05\n* attention\\_dropout 0.05\n* fin... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_7_0 #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nT... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-cv8-turkish
## Model description
This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 8.0 TR](... | {"language": "tr", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "tr"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "mpoyraz/wav2vec2-xls-r-300m-cv8-turkish", "result... | mpoyraz/wav2vec2-xls-r-300m-cv8-turkish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"tr",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"regi... | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-cv8-turkish
===============================
Model description
-----------------
This ASR model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on Turkish language.
Training and evaluation data
----------------------------
The following datasets were used for finetuning:
* Common Vo... | [
"### Training hyperparameters\n\n\nThe following hypermaters were used for finetuning:\n\n\n* learning\\_rate 2.5e-4\n* num\\_train\\_epochs 20\n* warmup\\_steps 500\n* freeze\\_feature\\_extractor\n* mask\\_time\\_prob 0.1\n* mask\\_feature\\_prob 0.1\n* feat\\_proj\\_dropout 0.05\n* attention\\_dropout 0.05\n* fi... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\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. -->
# run1
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co/Helsinki-NLP/opus-mt-es-es) on an... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model_index": [{"name": "run1", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Bleu", "type": "bleu", "value": 8.4217}}]}]} | mptrigo/run1 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| run1
====
This model is a fine-tuned version of Helsinki-NLP/opus-mt-es-es on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1740
* Bleu: 8.4217
* Gen Len: 15.9457
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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
text-generation | transformers |
#Scully from XFiles DialoGPT model | {"tags": ["conversational"]} | mra1ster/DialoGPT_scully_small | 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
|
#Scully from XFiles DialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmpacdj0jf1
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## M... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmpacdj0jf1", "results": []}]} | mradau/stress_classifier | null | [
"transformers",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# tmpacdj0jf1
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmp10l_qol1
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## M... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmp10l_qol1", "results": []}]} | mradau/stress_score | null | [
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"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# tmp10l_qol1
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# tmp10l_qol1\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
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fill-mask | transformers |
# CodeBERTaPy
CodeBERTaPy is a RoBERTa-like model trained on the [CodeSearchNet](https://github.blog/2019-09-26-introducing-the-codesearchnet-challenge/) dataset from GitHub for `python` by [Manuel Romero](https://twitter.com/mrm8488)
The **tokenizer** is a Byte-level BPE tokenizer trained on the corpus using Huggin... | {"language": "code"} | mrm8488/CodeBERTaPy | null | [
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"arxiv:1909.09436",
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] | null | 2022-03-02T23:29:05+00:00 | [
"1909.09436"
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#transformers #pytorch #jax #roberta #fill-mask #code #arxiv-1909.09436 #autotrain_compatible #endpoints_compatible #region-us
|
# CodeBERTaPy
CodeBERTaPy is a RoBERTa-like model trained on the CodeSearchNet dataset from GitHub for 'python' by Manuel Romero
The tokenizer is a Byte-level BPE tokenizer trained on the corpus using Hugging Face 'tokenizers'.
Because it is trained on a corpus of code (vs. natural language), it encodes the corpus ... | [
"# CodeBERTaPy\n\nCodeBERTaPy is a RoBERTa-like model trained on the CodeSearchNet dataset from GitHub for 'python' by Manuel Romero\n\nThe tokenizer is a Byte-level BPE tokenizer trained on the corpus using Hugging Face 'tokenizers'.\n\nBecause it is trained on a corpus of code (vs. natural language), it encodes t... | [
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text-generation | transformers |
# CodeGPT-small-py fine-tuned on CodeXGLUE for code-refinement task | {"language": "en", "widget": [{"text": "<s> def add_number ( a , b ) : <EOL> return a +"}]} | mrm8488/CodeGPT-small-finetuned-python-token-completion | null | [
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# CodeGPT-small-py fine-tuned on CodeXGLUE for code-refinement task | [
"# CodeGPT-small-py fine-tuned on CodeXGLUE for code-refinement task"
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] |
text-generation | transformers |
# GPT-2 + CORD19 dataset : 🦠 ✍ ⚕
**GPT-2** fine-tuned on **biorxiv_medrxiv**, **comm_use_subset** and **custom_license** files from [CORD-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) dataset.
## Datasets details
| Dataset | # Files |
| ---------------------- | -----... | {"language": "en"} | mrm8488/GPT-2-finetuned-CORD19 | null | [
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"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2 + CORD19 dataset :
========================
GPT-2 fine-tuned on biorxiv\_medrxiv, comm\_use\_subset and custom\_license files from CORD-19 dataset.
Datasets details
----------------
Model training
--------------
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
![tra... | [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# GPT-2 fine-tuned on CommonGen
[GPT-2](https://huggingface.co/gpt2) fine-tuned on [CommonGen](https://inklab.usc.edu/CommonGen/index.html) for *Generative Commonsense Reasoning*.
## Details of GPT-2
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
m... | {"language": "en", "datasets": ["common_gen"], "widget": [{"text": "<|endoftext|> apple, tree, pick:"}]} | mrm8488/GPT-2-finetuned-common_gen | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #dataset-common_gen #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2 fine-tuned on CommonGen
=============================
GPT-2 fine-tuned on CommonGen for *Generative Commonsense Reasoning*.
Details of GPT-2
----------------
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw ... | [
"# samples: 67389\nDataset: common\\_gen, Split: valid, # samples: 4018\nDataset: common\\_gen, Split: test, # samples: 1497\n\n\nModel fine-tuning ️\n--------------------\n\n\nYou can find the fine-tuning script here\n\n\nModel in Action\n---------------\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n... | [
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"# samples: 67389\nDataset: common\\_gen, Split: valid, # samples: 4018\nDataset: common\\_gen, Split: test, # samples: 1497\n\n\nModel fine-tuni... |
text-generation | transformers |
# GPT-2 + bio/medrxiv files from CORD19: 🦠 ✍ ⚕
**GPT-2** fine-tuned on **biorxiv_medrxiv** files from [CORD-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) dataset.
## Datasets details:
| Dataset | # Files |
| ---------------------- | ----- |
| biorxiv_medrxiv |... | {"language": "en", "widget": [{"text": "Old people with COVID-19 tends to suffer"}]} | mrm8488/GPT-2-finetuned-covid-bio-medrxiv | null | [
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"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2 + bio/medrxiv files from CORD19:
======================================
GPT-2 fine-tuned on biorxiv\_medrxiv files from CORD-19 dataset.
Datasets details:
-----------------
Model training:
---------------
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
Model in act... | [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # GuaPeTe-2-tiny fine-tuned on TED dataset for CLM | {"language": "es", "tags": ["spanish", "gpt-2", "spanish gpt2"], "widget": [{"text": "Ustedes tienen la oportunidad de"}]} | mrm8488/GuaPeTe-2-tiny-finetuned-TED | null | [
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#transformers #pytorch #jax #gpt2 #text-generation #spanish #gpt-2 #spanish gpt2 #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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"# GuaPeTe-2-tiny fine-tuned on TED dataset for CLM"
] |
text-generation | transformers | # GuaPeTe-2-tiny fine-tuned on eubookshop dataset for CLM | {"language": "es", "tags": ["spanish", "gpt-2"], "widget": [{"text": "El objetivo de la Uni\u00f3n Europea es"}]} | mrm8488/GuaPeTe-2-tiny-finetuned-eubookshop | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
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#transformers #pytorch #jax #gpt2 #text-generation #spanish #gpt-2 #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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"# GuaPeTe-2-tiny fine-tuned on eubookshop dataset for CLM"
] |
text-generation | transformers |
# GuaPeTe-2-tiny: A proof of concept tiny GPT-2 like model trained on Spanish Wikipedia corpus
| {"language": "es", "tags": ["spanish", "gpt-2", "spanish gpt2"], "widget": [{"text": "Murcia es la huerta de Europa porque"}]} | mrm8488/GuaPeTe-2-tiny | null | [
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"spanish",
"gpt-2",
"spanish gpt2",
"es",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #spanish #gpt-2 #spanish gpt2 #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GuaPeTe-2-tiny: A proof of concept tiny GPT-2 like model trained on Spanish Wikipedia corpus
| [
"# GuaPeTe-2-tiny: A proof of concept tiny GPT-2 like model trained on Spanish Wikipedia corpus"
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"# GuaPeTe-2-tiny: A proof of concept tiny GPT-2 like model trained on Spanish Wikipedia corpus"
] |
fill-mask | transformers |
# RoBERTinha: RoBERTa-like Language model trained on OSCAR Galician corpus
| {"language": "gl", "widget": [{"text": "Galicia \u00e9 unha <mask> aut\u00f3noma espa\u00f1ola."}, {"text": "A lingua oficial de Galicia \u00e9 o <mask>."}]} | mrm8488/RoBERTinha | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"gl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gl"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #gl #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTinha: RoBERTa-like Language model trained on OSCAR Galician corpus
| [
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] |
fill-mask | transformers |
# RoBasquERTa: RoBERTa-like Language model trained on OSCAR Basque corpus
| {"language": "eu", "widget": [{"text": "Euskara da Euskal Herriko <mask> ofiziala"}, {"text": "Gaur egun, Euskadik Espainia osoko ekonomia <mask> du"}]} | mrm8488/RoBasquERTa | null | [
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"jax",
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"fill-mask",
"eu",
"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #eu #autotrain_compatible #endpoints_compatible #region-us
|
# RoBasquERTa: RoBERTa-like Language model trained on OSCAR Basque corpus
| [
"# RoBasquERTa: RoBERTa-like Language model trained on OSCAR Basque corpus"
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] |
token-classification | transformers |
# RuPERTa-base (Spanish RoBERTa) + NER 🎃🏷
This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) version of [RuPERTa-base](https://huggingface.co/mrm8488/RuPERTa-base) for **NER** downstream task.
## Details of the downstream task (NER) - Dataset
- [Dataset: CONLL Corpora ES](https... | {"language": "es"} | mrm8488/RuPERTa-base-finetuned-ner | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
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#transformers #pytorch #jax #safetensors #roberta #token-classification #es #autotrain_compatible #endpoints_compatible #region-us
| RuPERTa-base (Spanish RoBERTa) + NER
====================================
This model is a fine-tuned on NER-C version of RuPERTa-base for NER downstream task.
Details of the downstream task (NER) - Dataset
----------------------------------------------
* Dataset: CONLL Corpora ES
* Fine-tune on NER script prov... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #token-classification #es #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# RuPERTa-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)
| {"language": "es", "tags": ["nli"], "datasets": ["xtreme"], "widget": [{"text": "En 2009 se mud\u00f3 a Filadelfia y en la actualidad vive en Nueva York. Se mud\u00f3 nuevamente a Filadelfia en 2009 y ahora vive en la ciudad de Nueva York."}]} | mrm8488/RuPERTa-base-finetuned-pawsx-es | null | [
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"jax",
"roberta",
"text-classification",
"nli",
"es",
"dataset:xtreme",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #nli #es #dataset-xtreme #autotrain_compatible #endpoints_compatible #region-us
|
# RuPERTa-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)
| [
"# RuPERTa-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)"
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"# RuPERTa-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)"
] |
token-classification | transformers |
# RuPERTa-base (Spanish RoBERTa) + POS 🎃🏷
This model is a fine-tuned on [CONLL CORPORA](https://www.kaggle.com/nltkdata/conll-corpora) version of [RuPERTa-base](https://huggingface.co/mrm8488/RuPERTa-base) for **POS** downstream task.
## Details of the downstream task (POS) - Dataset
- [Dataset: CONLL Corpora E... | {"language": "es"} | mrm8488/RuPERTa-base-finetuned-pos | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #roberta #token-classification #es #autotrain_compatible #endpoints_compatible #region-us
| RuPERTa-base (Spanish RoBERTa) + POS
====================================
This model is a fine-tuned on CONLL CORPORA version of RuPERTa-base for POS downstream task.
Details of the downstream task (POS) - Dataset
----------------------------------------------
* Dataset: CONLL Corpora ES
* Fine-tune on NER scr... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #token-classification #es #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# RuPERTa: the Spanish RoBERTa 🎃<img src="https://abs-0.twimg.com/emoji/v2/svg/1f1ea-1f1f8.svg" alt="spain flag" width="25"/>
RuPERTa-base (uncased) is a [RoBERTa model](https://github.com/pytorch/fairseq/tree/master/examples/roberta) trained on a *uncased* verison of [big Spanish corpus](https://github.com/josecann... | {"language": "es", "thumbnail": "https://i.imgur.com/DUlT077.jpg", "widget": [{"text": "Espa\u00f1a es un pa\u00eds muy <mask> en la UE"}]} | mrm8488/RuPERTa-base | null | [
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"es",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #es #autotrain_compatible #endpoints_compatible #region-us
| RuPERTa: the Spanish RoBERTa <img src="URL alt="spain flag" width="25"/>
========================================================================
RuPERTa-base (uncased) is a RoBERTa model trained on a *uncased* verison of big Spanish corpus.
RoBERTa iterates on BERT's pretraining procedure, including training the mod... | [
"### Usage for POS and NER\n\n\nFor POS just change the 'id2label' dictionary and the model path to mrm8488/RuPERTa-base-finetuned-pos",
"### Fast usage for LM with 'pipelines'\n\n\nAcknowledgments\n---------------\n\n\nI thank /transformers team for answering my doubts and Google for helping me with the TensorFl... | [
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"### Fast usage for LM with 'pipelines'\n\n... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# T5-base fine-tuned on CUAD for Legal Contract Review (via QA)
This model is a fine-tuned version of [t5-base](https://huggingfac... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["cuad"], "model-index": [{"name": "T5-base-cuad-512", "results": []}]} | mrm8488/T5-base-finetuned-cuad | 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 #safetensors #t5 #text2text-generation #generated_from_trainer #en #dataset-cuad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-base fine-tuned on CUAD for Legal Contract Review (via QA)
=============================================================
This model is a fine-tuned version of t5-base on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2209
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #generated_from_trainer #en #dataset-cuad #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lear... |
token-classification | transformers |
# Spanish TinyBERT + NER
This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) of a [Spanish Tiny Bert](https://huggingface.co/mrm8488/es-tinybert-v1-1) model I created using *distillation* for **NER** downstream task. The **size** of the model is **55MB**
## Details of the downstream ... | {"language": "es"} | mrm8488/TinyBERT-spanish-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #has_space #region-us
| Spanish TinyBERT + NER
======================
This model is a fine-tuned on NER-C of a Spanish Tiny Bert model I created using *distillation* for NER downstream task. The size of the model is 55MB
Details of the downstream task (NER) - Dataset
----------------------------------------------
* Dataset: CONLL Corpor... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | transformers |
# Spanish ViT to GPT-2
### WIP | {"language": ["es"], "tags": ["Vit2gpt", "captioning"]} | mrm8488/ViT2GPT-2-es | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"Vit2gpt",
"captioning",
"es",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #vision-encoder-decoder #Vit2gpt #captioning #es #endpoints_compatible #region-us
|
# Spanish ViT to GPT-2
### WIP | [
"# Spanish ViT to GPT-2",
"### WIP"
] | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #Vit2gpt #captioning #es #endpoints_compatible #region-us \n",
"# Spanish ViT to GPT-2",
"### WIP"
] |
null | null | #@title
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
- ATARI
- Breakout
---
# A2C Breakout (No frame skip) v4 🤖🎮
This is a pre-trained model of a A2C agent playing Breakout (NoFrameskip-v4) using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
<v... | {} | mrm8488/a2c-BreakoutNoFrameskip-v4 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| #@title
---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
- ATARI
- Breakout
---
# A2C Breakout (No frame skip) v4
This is a pre-trained model of a A2C agent playing Breakout (NoFrameskip-v4) using the stable-baselines3 library.
<video loop="" autoplay="" controls="" src="URL
### ... | [
"# A2C Breakout (No frame skip) v4 \n\nThis is a pre-trained model of a A2C agent playing Breakout (NoFrameskip-v4) using the stable-baselines3 library.\n\n<video loop=\"\" autoplay=\"\" controls=\"\" src=\"URL",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 an... | [
"TAGS\n#region-us \n",
"# A2C Breakout (No frame skip) v4 \n\nThis is a pre-trained model of a A2C agent playing Breakout (NoFrameskip-v4) using the stable-baselines3 library.\n\n<video loop=\"\" autoplay=\"\" controls=\"\" src=\"URL",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you ... |
reinforcement-learning | stable-baselines3 | # TODO: Fill this model card
| {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | mrm8488/a2c-Pong-v0 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
| # TODO: Fill this model card
| [
"# TODO: Fill this model card"
] | [
"TAGS\n#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us \n",
"# TODO: Fill this model card"
] |
reinforcement-learning | stable-baselines3 | # TODO: Fill this model card
| {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | mrm8488/a2c-PongNoFrameskip-v0 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
| # TODO: Fill this model card
| [
"# TODO: Fill this model card"
] | [
"TAGS\n#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us \n",
"# TODO: Fill this model card"
] |
text-classification | transformers |
# bert-base-german-dbmdz-cased fine-tuned on PAWS-X-de for Paraphrase Identification (NLI)
| {"language": "de", "tags": ["nli"], "datasets": ["xtreme"], "widget": [{"text": "Winarsky ist Mitglied des IEEE, Phi Beta Kappa, des ACM und des Sigma Xi. Winarsky ist Mitglied des ACM, des IEEE, der Phi Beta Kappa und der Sigma Xi."}]} | mrm8488/bert-base-german-dbmdz-cased-finetuned-pawsx-de | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"nli",
"de",
"dataset:xtreme",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #nli #de #dataset-xtreme #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-german-dbmdz-cased fine-tuned on PAWS-X-de for Paraphrase Identification (NLI)
| [
"# bert-base-german-dbmdz-cased fine-tuned on PAWS-X-de for Paraphrase Identification (NLI)"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #nli #de #dataset-xtreme #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-german-dbmdz-cased fine-tuned on PAWS-X-de for Paraphrase Identification (NLI)"
] |
token-classification | transformers |
# German BERT + LER (Legal Entity Recognition) ⚖️
German BERT ([BERT-base-german-cased](https://huggingface.co/bert-base-german-cased)) fine-tuned on [Legal-Entity-Recognition](https://github.com/elenanereiss/Legal-Entity-Recognition) dataset for **LER** (NER) downstream task.
## Details of the downstream task (NER)... | {"language": "de"} | mrm8488/bert-base-german-finetuned-ler | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us
| German BERT + LER (Legal Entity Recognition) ️
==============================================
German BERT (BERT-base-german-cased) fine-tuned on Legal-Entity-Recognition dataset for LER (NER) downstream task.
Details of the downstream task (NER) - Dataset
----------------------------------------------
Legal-Entit... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers |
# bert-base-portuguese-cased fine-tuned on SQuAD-v1-pt | {"language": "pt", "license": "apache-2.0", "datasets": ["squad_v1_pt"], "widget": [{"text": "Com que licen\u00e7a posso usar o conte\u00fado da wikipedia?", "context": "A Wikip\u00e9dia \u00e9 um projeto de enciclop\u00e9dia colaborativa, universal e multil\u00edngue estabelecido na internet sob o princ\u00edpio wiki.... | mrm8488/bert-base-portuguese-cased-finetuned-squad-v1-pt | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"pt",
"dataset:squad_v1_pt",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #pt #dataset-squad_v1_pt #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# bert-base-portuguese-cased fine-tuned on SQuAD-v1-pt | [
"# bert-base-portuguese-cased fine-tuned on SQuAD-v1-pt"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #pt #dataset-squad_v1_pt #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# bert-base-portuguese-cased fine-tuned on SQuAD-v1-pt"
] |
question-answering | transformers |
# BETO (Spanish BERT) + Spanish SQuAD2.0
This model is provided by [BETO team](https://github.com/dccuchile/beto) and fine-tuned on [SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve) for **Q&A** downstream task.
## Details of the language model('dccuchile/bert-base-spanish-wwm-cased')
Language m... | {"language": "es", "thumbnail": "https://i.imgur.com/jgBdimh.png"} | mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"es",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #es #endpoints_compatible #has_space #region-us
| BETO (Spanish BERT) + Spanish SQuAD2.0
======================================
This model is provided by BETO team and fine-tuned on SQuAD-es-v2.0 for Q&A downstream task.
Details of the language model('dccuchile/bert-base-spanish-wwm-cased')
----------------------------------------------------------------------
L... | [
"### Model in action (in a Colab Notebook)\n\n\n\n1. Set the context and ask some questions:\n\n\n!Set context and questions\n\n\n2. Run predictions:\n\n\n!Run the model\n\n\n\n\n> \n> Created by Manuel Romero/@mrm8488\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #es #endpoints_compatible #has_space #region-us \n",
"### Model in action (in a Colab Notebook)\n\n\n\n1. Set the context and ask some questions:\n\n\n!Set context and questions\n\n\n2. Run predictions:\n\n\n!Run the model\n\n\n\n\n> \n> Created by Manu... |
question-answering | transformers |
# Italian BERT fine-tuned on SQuAD_it v1
[Italian BERT base cased](https://huggingface.co/dbmdz/bert-base-italian-cased) fine-tuned on [italian SQuAD](https://github.com/crux82/squad-it) for **Q&A** downstream task.
## Details of Italian BERT
The source data for the Italian BERT model consists of a recent Wikipedia... | {"language": "it"} | mrm8488/bert-italian-finedtuned-squadv1-it-alfa | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"it",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #it #endpoints_compatible #has_space #region-us
| Italian BERT fine-tuned on SQuAD\_it v1
=======================================
Italian BERT base cased fine-tuned on italian SQuAD for Q&A downstream task.
Details of Italian BERT
-----------------------
The source data for the Italian BERT model consists of a recent Wikipedia dump and various texts from the OPU... | [
"### Raw metrics\n\n\nComparison ️\n------------\n\n\nModel: DrQA-it trained on SQuAD-it, EM: 56.1, F1 score: 65.9\nModel: This one, EM: 62.51, F1 score: 74.16\n\n\nModel in action\n---------------\n\n\nFast usage with pipelines\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #it #endpoints_compatible #has_space #region-us \n",
"### Raw metrics\n\n\nComparison ️\n------------\n\n\nModel: DrQA-it trained on SQuAD-it, EM: 56.1, F1 score: 65.9\nModel: This one, EM: 62.51, F1 score: 74.16\n\n\nModel in action\n---------------\n\... |
question-answering | transformers |
# BERT-Medium fine-tuned on SQuAD v2
[BERT-Medium](https://github.com/google-research/bert/) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
**Mode size** (after training): **157.46 MB**
## Detai... | {"language": "en"} | mrm8488/bert-medium-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1908.08962",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #en #arxiv-1908.08962 #endpoints_compatible #has_space #region-us
| BERT-Medium fine-tuned on SQuAD v2
==================================
BERT-Medium created by Google Research and fine-tuned on SQuAD 2.0 for Q&A downstream task.
Mode size (after training): 157.46 MB
Details of BERT-Small and its 'family' (from their documentation)
------------------------------------------------... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine tuning can be found here\n\n\nResults:\n--------",
"### Raw metrics from benchmark included in training script:\n\n\nCompariso... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #en #arxiv-1908.08962 #endpoints_compatible #has_space #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for... |
text-classification | transformers |
# BERT-Mini fine-tuned on age_news dataset for news classification
Test set accuray: 0.93 | {"language": "en", "tags": ["news", "classification", "mini"], "datasets": ["ag_news"], "widget": [{"text": "Israel withdraws from Gaza camp Israel withdraws from Khan Younis refugee camp in the Gaza Strip, after a four-day operation that left 11 dead."}], "model-index": [{"name": "mrm8488/bert-mini-finetuned-age_news-... | mrm8488/bert-mini-finetuned-age_news-classification | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"news",
"classification",
"mini",
"en",
"dataset:ag_news",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #news #classification #mini #en #dataset-ag_news #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-Mini fine-tuned on age_news dataset for news classification
Test set accuray: 0.93 | [
"# BERT-Mini fine-tuned on age_news dataset for news classification\n\nTest set accuray: 0.93"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #news #classification #mini #en #dataset-ag_news #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-Mini fine-tuned on age_news dataset for news classification\n\nTest set accuray: 0.93"
] |
question-answering | transformers |
# BERT-Mini fine-tuned on SQuAD v2
[BERT-Mini](https://github.com/google-research/bert/) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
**Mode size** (after training): **42.63 MB**
## Details of... | {"language": "en"} | mrm8488/bert-mini-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1908.08962",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #en #arxiv-1908.08962 #endpoints_compatible #region-us
| BERT-Mini fine-tuned on SQuAD v2
================================
BERT-Mini created by Google Research and fine-tuned on SQuAD 2.0 for Q&A downstream task.
Mode size (after training): 42.63 MB
Details of BERT-Mini and its 'family' (from their documentation)
--------------------------------------------------------... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine tuning can be found here\n\n\nResults:\n--------\n\n\n\nComparison:\n-----------\n\n\n\nModel in action\n---------------\n\n\nFa... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #en #arxiv-1908.08962 #endpoints_compatible #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine tunin... |
summarization | transformers |
# Bert-mini2Bert-mini Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *BERT2BERT* ([mini](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)) model fine-tuned on the *CNN/Dailymail* summarization dataset.
The model achieves a **16.51** ROUGE-2 score on *CNN/Dailymail*'s test dataset... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"]} | mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Bert-mini2Bert-mini Summarization with EncoderDecoder Framework
===============================================================
This model is a warm-started *BERT2BERT* (mini) model fine-tuned on the *CNN/Dailymail* summarization dataset.
The model achieves a 16.51 ROUGE-2 score on *CNN/Dailymail*'s test dataset.
... | [] | [
"TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# A fine-tuned model on GoldP task from Tydi QA dataset
This model uses [bert-multi-cased-finetuned-xquadv1](https://huggingface.co/mrm8488/bert-multi-cased-finetuned-xquadv1) and fine-tuned on [Tydi QA](https://github.com/google-research-datasets/tydiqa) dataset for Gold Passage task [(GoldP)](https://github.com/goo... | {"language": "multilingual"} | mrm8488/bert-multi-cased-finedtuned-xquad-tydiqa-goldp | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"multilingual",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #multilingual #endpoints_compatible #region-us
| A fine-tuned model on GoldP task from Tydi QA dataset
=====================================================
This model uses bert-multi-cased-finetuned-xquadv1 and fine-tuned on Tydi QA dataset for Gold Passage task (GoldP)
Details of the language model
-----------------------------
The base language model (bert-m... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #multilingual #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# BERT (base-multilingual-cased) fine-tuned for multilingual Q&A
This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) like data for multilingual (`11 different languages`) **Q&A** downstream task.
## Detai... | {"language": "multilingual"} | mrm8488/bert-multi-cased-finetuned-xquadv1 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"multilingual",
"arxiv:1910.11856",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.11856"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #multilingual #arxiv-1910.11856 #endpoints_compatible #region-us
| BERT (base-multilingual-cased) fine-tuned for multilingual Q&A
==============================================================
This model was created by Google and fine-tuned on XQuAD like data for multilingual ('11 different languages') Q&A downstream task.
Details of the language model('bert-base-multilingual-case... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #multilingual #arxiv-1910.11856 #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# BERT (base-multilingual-uncased) fine-tuned for multilingual Q&A
This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) like data for multilingual (`11 different languages`) **Q&A** downstream task.
## Det... | {"language": "multilingual"} | mrm8488/bert-multi-uncased-finetuned-xquadv1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"multilingual",
"arxiv:1910.11856",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.11856"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #multilingual #arxiv-1910.11856 #endpoints_compatible #region-us
| BERT (base-multilingual-uncased) fine-tuned for multilingual Q&A
================================================================
This model was created by Google and fine-tuned on XQuAD like data for multilingual ('11 different languages') Q&A downstream task.
Details of the language model('bert-base-multilingual-... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #multilingual #arxiv-1910.11856 #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# BERT-Small fine-tuned on SQuAD v2
[BERT-Small](https://github.com/google-research/bert/) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
**Mode size** (after training): **109.74 MB**
## Details... | {"language": "en"} | mrm8488/bert-small-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1908.08962",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #en #arxiv-1908.08962 #endpoints_compatible #has_space #region-us
| BERT-Small fine-tuned on SQuAD v2
=================================
BERT-Small created by Google Research and fine-tuned on SQuAD 2.0 for Q&A downstream task.
Mode size (after training): 109.74 MB
Details of BERT-Small and its 'family' (from their documentation)
---------------------------------------------------... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine tuning can be found here\n\n\nResults:\n--------\n\n\n\nComparison:\n-----------\n\n\n\nModel in action\n---------------\n\n\nFa... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #en #arxiv-1908.08962 #endpoints_compatible #has_space #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for... |
token-classification | transformers |
# BERT SMALL + Typo Detection ✍❌✍✔
[BERT SMALL](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) fine-tuned on [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) for **typo detection** (using *NER* style)
## Details of the downstream task (Typo detection as NER)
- Dataset: [GitHub Typo C... | {"language": "en", "widget": [{"text": "here there is an error in coment"}]} | mrm8488/bert-small-finetuned-typo-detection | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #en #autotrain_compatible #endpoints_compatible #region-us
| BERT SMALL + Typo Detection
===========================
BERT SMALL fine-tuned on GitHub Typo Corpus for typo detection (using *NER* style)
Details of the downstream task (Typo detection as NER)
------------------------------------------------------
* Dataset: GitHub Typo Corpus
* Fine-tune script on NER dataset p... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
# Bert-small2Bert-small Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *BERT2BERT* ([small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)) model fine-tuned on the *CNN/Dailymail* summarization dataset.
The model achieves a **17.37** ROUGE-2 score on *CNN/Dailymail*'s test data... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail"]} | mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Bert-small2Bert-small Summarization with EncoderDecoder Framework
=================================================================
This model is a warm-started *BERT2BERT* (small) model fine-tuned on the *CNN/Dailymail* summarization dataset.
The model achieves a 17.37 ROUGE-2 score on *CNN/Dailymail*'s test datas... | [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers |
# Spanish BERT (BETO) + NER
This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) version of the Spanish BERT cased [(BETO)](https://github.com/dccuchile/beto) for **NER** downstream task.
## Details of the downstream task (NER) - Dataset
- [Dataset: CONLL Corpora ES](https://www.kag... | {"language": "es", "thumbnail": "https://i.imgur.com/jgBdimh.png"} | mrm8488/bert-spanish-cased-finetuned-ner | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #has_space #region-us
| Spanish BERT (BETO) + NER
=========================
This model is a fine-tuned on NER-C version of the Spanish BERT cased (BETO) for NER downstream task.
Details of the downstream task (NER) - Dataset
----------------------------------------------
* Dataset: CONLL Corpora ES
I preprocessed the dataset and split... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers |
# Spanish BERT (BETO) + Syntax POS tagging ✍🏷
This model is a fine-tuned version of the Spanish BERT [(BETO)](https://github.com/dccuchile/beto) on Spanish **syntax** annotations in [CONLL CORPORA](https://www.kaggle.com/nltkdata/conll-corpora) dataset for **syntax POS** (Part of Speech tagging) downstream task.
##... | {"language": "es"} | mrm8488/bert-spanish-cased-finetuned-pos-syntax | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #region-us
| Spanish BERT (BETO) + Syntax POS tagging
========================================
This model is a fine-tuned version of the Spanish BERT (BETO) on Spanish syntax annotations in CONLL CORPORA dataset for syntax POS (Part of Speech tagging) downstream task.
Details of the downstream task (Syntax POS) - Dataset
------... | [
"#### Fine-tune script on NER dataset provided by Huggingface",
"#### 21 Syntax annotations (Labels) covered:\n\n\n* \\_\n* ATR\n* ATR.d\n* CAG\n* CC\n* CD\n* CD.Q\n* CI\n* CPRED\n* CPRED.CD\n* CPRED.SUJ\n* CREG\n* ET\n* IMPERS\n* MOD\n* NEG\n* PASS\n* PUNC\n* ROOT\n* SUJ\n* VOC\n\n\nMetrics on test set\n--------... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #es #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Fine-tune script on NER dataset provided by Huggingface",
"#### 21 Syntax annotations (Labels) covered:\n\n\n* \\_\n* ATR\n* ATR.d\n* CAG\n* CC\n* CD\n* CD.Q\n* CI\n* CPRED\n* CPR... |
token-classification | transformers |
# Spanish BERT (BETO) + POS
This model is a fine-tuned on Spanish [CONLL CORPORA](https://www.kaggle.com/nltkdata/conll-corpora) version of the Spanish BERT cased [(BETO)](https://github.com/dccuchile/beto) for **POS** (Part of Speech tagging) downstream task.
## Details of the downstream task (POS) - Dataset
- [D... | {"language": "es", "tags": ["POS", "Spanish"], "thumbnail": "https://i.imgur.com/jgBdimh.png", "widget": [{"text": "Mis amigos y yo estamos pensando en viajar a Londres este verano."}]} | mrm8488/bert-spanish-cased-finetuned-pos | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"POS",
"Spanish",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #POS #Spanish #es #autotrain_compatible #endpoints_compatible #region-us
| Spanish BERT (BETO) + POS
=========================
This model is a fine-tuned on Spanish CONLL CORPORA version of the Spanish BERT cased (BETO) for POS (Part of Speech tagging) downstream task.
Details of the downstream task (POS) - Dataset
----------------------------------------------
* Dataset: CONLL Corpora ... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #POS #Spanish #es #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# BERT-Tiny ([5](https://huggingface.co/google/bert_uncased_L-12_H-128_A-2)) fine-tuned on SQuAD v2
[BERT-Tiny](https://huggingface.co/google/bert_uncased_L-12_H-128_A-2) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q... | {"language": "en", "tags": ["QA"]} | mrm8488/bert-tiny-5-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"QA",
"en",
"arxiv:1908.08962",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #QA #en #arxiv-1908.08962 #endpoints_compatible #region-us
| BERT-Tiny (5) fine-tuned on SQuAD v2
====================================
BERT-Tiny created by Google Research and fine-tuned on SQuAD 2.0 for Q&A downstream task.
Mode size (after training): 24.33 MB
Details of BERT-Tiny and its 'family' (from their documentation)
------------------------------------------------... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine tuning can be found here\n\n\nResults:\n--------\n\n\n\n\nModel in action\n---------------\n\n\nFast usage with pipelines:\n\n\n... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #QA #en #arxiv-1908.08962 #endpoints_compatible #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine t... |
text-classification | transformers |
# BERT Tiny fine-tuned for fake news detection | {"language": "en", "widget": [{"text": "It s official the inmates are running the asylum A police department in Northampton, Massachusetts is ending its High-Five Friday program at local elementary schools due to concerns that undocumented children and others may feel uncomfortable seeing an officer at school.The... | mrm8488/bert-tiny-finetuned-fake-news-detection | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
# BERT Tiny fine-tuned for fake news detection | [
"# BERT Tiny fine-tuned for fake news detection"
] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT Tiny fine-tuned for fake news detection"
] |
text-classification | transformers |
# BERT-Tiny fine-tuned on on sms_spam dataset for spam detection
Validation accuray: **0.98** | {"language": "en", "tags": ["sms", "spam", "detection"], "datasets": ["sms_spam"], "widget": [{"text": "Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days."}]} | mrm8488/bert-tiny-finetuned-sms-spam-detection | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"sms",
"spam",
"detection",
"en",
"dataset:sms_spam",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #sms #spam #detection #en #dataset-sms_spam #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT-Tiny fine-tuned on on sms_spam dataset for spam detection
Validation accuray: 0.98 | [
"# BERT-Tiny fine-tuned on on sms_spam dataset for spam detection\n\nValidation accuray: 0.98"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #sms #spam #detection #en #dataset-sms_spam #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT-Tiny fine-tuned on on sms_spam dataset for spam detection\n\nValidation accuray: 0.98"
] |
question-answering | transformers |
# BERT-Tiny fine-tuned on SQuAD v2
[BERT-Tiny](https://github.com/google-research/bert/) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
**Mode size** (after training): **16.74 MB**
## Details of... | {"language": "en", "tags": ["QA"]} | mrm8488/bert-tiny-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"QA",
"en",
"arxiv:1908.08962",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #QA #en #arxiv-1908.08962 #endpoints_compatible #has_space #region-us
| BERT-Tiny fine-tuned on SQuAD v2
================================
BERT-Tiny created by Google Research and fine-tuned on SQuAD 2.0 for Q&A downstream task.
Mode size (after training): 16.74 MB
Details of BERT-Tiny and its 'family' (from their documentation)
--------------------------------------------------------... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM.\nThe script for fine tuning can be found here\n\n\nResults:\n--------\n\n\n\n\nModel in action\n---------------\n\n\nFast usage with pipelines:",
"... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #QA #en #arxiv-1908.08962 #endpoints_compatible #has_space #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel training\n--------------\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM... |
text-classification | transformers |
# [BERT](https://huggingface.co/deepset/bert-base-cased-squad2) fine tuned on [QNLI](https://github.com/rhythmcao/QNLI)+ compression ([BERT-of-Theseus](https://github.com/JetRunner/BERT-of-Theseus))
I used a [Bert model fine tuned on **SQUAD v2**](https://huggingface.co/deepset/bert-base-cased-squad2) and then I fine... | {"language": "en"} | mrm8488/bert-uncased-finetuned-qnli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
| BERT fine tuned on QNLI+ compression (BERT-of-Theseus)
======================================================
I used a Bert model fine tuned on SQUAD v2 and then I fine tuned it on QNLI using compression (with a constant replacing rate) as proposed in BERT-of-Theseus
Details of the downstream task (QNLI):
---------... | [
"### Getting the dataset",
"### Model training\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:\n\n\nMetrics:\n--------\n\n\n\n\n> \n> See all my models\n> \n> \n> \n\n\n\n> \n> Created by Manuel Romero/@mrm8488\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n",
"### Getting the dataset",
"### Model training\n\n\nThe model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:\n\n\nMetrics:\n--------\n\n\n\n\n> \n> See all m... |
text2text-generation | transformers | # Spanish Bert2Bert fine-tuned on SQuaD (es) for question generation | {"language": "es", "tags": ["spanish", "question", "generation"], "widget": [{"text": "Manuel vive en Murcia, Espa\u00f1a"}]} | mrm8488/bert2bert-spanish-question-generation | null | [
"transformers",
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"spanish",
"question",
"generation",
"es",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #spanish #question #generation #es #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Spanish Bert2Bert fine-tuned on SQuaD (es) for question generation | [
"# Spanish Bert2Bert fine-tuned on SQuaD (es) for question generation"
] | [
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"# Spanish Bert2Bert fine-tuned on SQuaD (es) for question generation"
] |
summarization | transformers |
# German BERT2BERT fine-tuned on MLSUM DE for summarization
## Model
[bert-base-german-cased](https://huggingface.co/bert-base-german-cased) (BERT Checkpoint)
## Dataset
**MLSUM** is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in ... | {"language": "de", "tags": ["summarization", "news"], "datasets": ["mlsum"], "widget": [{"text": "Wie geht man nach schrecklichen Ereignissen ambesten auf die \u00c4ngste und Sorgen von Kindern ein?Therapeuten haben eine klare Botschaft. Die Weltist voller Gefahren, Verbrechen und Schrecken -Krieg, Terrorismus, Umweltz... | mrm8488/bert2bert_shared-german-finetuned-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"news",
"de",
"dataset:mlsum",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #news #de #dataset-mlsum #autotrain_compatible #endpoints_compatible #has_space #region-us
| German BERT2BERT fine-tuned on MLSUM DE for summarization
=========================================================
Model
-----
bert-base-german-cased (BERT Checkpoint)
Dataset
-------
MLSUM is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/s... | [
"# Score: 33.04\nSet: Test, Metric: Rouge2 - mid - recall, # Score: 33.83\nSet: Test, Metric: Rouge2 - mid - fmeasure, # Score: 33.15\n\n\nUsage\n-----\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 with the support of Narrativa\n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #news #de #dataset-mlsum #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Score: 33.04\nSet: Test, Metric: Rouge2 - mid - recall, # Score: 33.83\nSet: Test, Metric: Rouge2 - mid - fmeasure, # ... |
text2text-generation | transformers | # Spanish Bert2Bert (shared) fine-tuned on PAUS-X es for paraphrasing | {"language": "es", "tags": ["spanish", "paraphrasing", "paraphrase"], "datasets": ["pausx"], "widget": [{"text": "El pionero suizo John Sutter (1803-1880) lleg\u00f3 a Alta California con otros colonos euroamericanos en agosto de 1839."}]} | mrm8488/bert2bert_shared-spanish-finetuned-paus-x-paraphrasing | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"spanish",
"paraphrasing",
"paraphrase",
"es",
"dataset:pausx",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #spanish #paraphrasing #paraphrase #es #dataset-pausx #autotrain_compatible #endpoints_compatible #region-us
| # Spanish Bert2Bert (shared) fine-tuned on PAUS-X es for paraphrasing | [
"# Spanish Bert2Bert (shared) fine-tuned on PAUS-X es for paraphrasing"
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] |
summarization | transformers |
# Spanish BERT2BERT (BETO) fine-tuned on MLSUM ES for summarization
## Model
[dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) (BERT Checkpoint)
## Dataset
**MLSUM** is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it co... | {"language": "es", "tags": ["summarization", "news"], "datasets": ["mlsum"], "widget": [{"text": "Al filo de las 22.00 horas del jueves, la Asamblea de Madrid vive un momento sorprendente: Vox decide no apoyar una propuesta del PP en favor del blindaje fiscal de la Comunidad. Se ha roto la unidad de los tres partidos d... | mrm8488/bert2bert_shared-spanish-finetuned-summarization | null | [
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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| Spanish BERT2BERT (BETO) fine-tuned on MLSUM ES for summarization
=================================================================
Model
-----
dccuchile/bert-base-spanish-wwm-cased (BERT Checkpoint)
Dataset
-------
MLSUM is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspape... | [] | [
"TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #news #es #dataset-mlsum #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
summarization | transformers |
# Turkish BERT2BERT (shared) fine-tuned on MLSUM TR for summarization
## Model
[dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) (BERT Checkpoint)
## Dataset
**MLSUM** is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+... | {"language": "tr", "tags": ["summarization", "news"], "datasets": ["mlsum"], "widget": [{"text": "Ankara'da oto h\u0131rs\u0131zl\u0131k \u00e7etesine y\u00f6nelikd\u00fczenlenen \u2018Balta\u2019 operasyonunda, \u00e7ete lideri\u2018balta\u2019 lakapl\u0131 \u015fah\u0131s ile 7 ki\u015fi g\u00f6zalt\u0131na al\u0131n... | mrm8488/bert2bert_shared-turkish-summarization | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
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| Turkish BERT2BERT (shared) fine-tuned on MLSUM TR for summarization
===================================================================
Model
-----
dbmdz/bert-base-turkish-cased (BERT Checkpoint)
Dataset
-------
MLSUM is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #news #tr #dataset-mlsum #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | # BioclinicalBERT fine-tuned for MLM on COVID Papers | {"language": ["en"], "widget": [{"text": "Masks are [MASK] for preventing"}]} | mrm8488/bioclinicalBERT-finetuned-covid-papers | null | [
"transformers",
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"jax",
"bert",
"fill-mask",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us
| # BioclinicalBERT fine-tuned for MLM on COVID Papers | [
"# BioclinicalBERT fine-tuned for MLM on COVID Papers"
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] |
null | transformers |
## 🦠 BIOMEDtra 🏥
**BIOMEDtra** (small) is an Electra like model (discriminator in this case) trained on [Spanish Biomedical Crawled Corpus](https://zenodo.org/record/5510033#.Yhdk1ZHMLJx).
As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
**ELECTRA** is a new method for self-supervise... | {"language": "es", "tags": ["Spanish", "Electra", "Bio", "Medical"], "datasets": ["cowese"]} | mrm8488/biomedtra-small-es | null | [
"transformers",
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"Spanish",
"Electra",
"Bio",
"Medical",
"es",
"dataset:cowese",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #electra #pretraining #Spanish #Electra #Bio #Medical #es #dataset-cowese #arxiv-1406.2661 #endpoints_compatible #region-us
| BIOMEDtra
---------
BIOMEDtra (small) is an Electra like model (discriminator in this case) trained on Spanish Biomedical Crawled Corpus.
As mentioned in the original paper:
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relativel... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #electra #pretraining #Spanish #Electra #Bio #Medical #es #dataset-cowese #arxiv-1406.2661 #endpoints_compatible #region-us \n"
] |
token-classification | transformers | # [BIOMEDtra](https://huggingface.co/mrm8488/biomedtra-small-es) (small) fine-tuned on clinical data for PII
| {"language": "es", "tags": ["clinical", "pii", "ner", "medical"], "widget": [{"text": " Nombre: Carolina . Apellidos: Ardoain Suarez. NASS: 12397565 54. Domicilio: C/ Viamonte, 166 - piso 1\u00ba. Localidad/ Provincia: Buenos Aires. CP: C1008. NHC: 794612. Datos asistenciales. Fecha de nacimiento: 28/02/1979. Pa\u00eds... | mrm8488/biomedtra-small-finenuned-clinical-ner | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #electra #token-classification #clinical #pii #ner #medical #es #autotrain_compatible #endpoints_compatible #region-us
| # BIOMEDtra (small) fine-tuned on clinical data for PII
| [
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] |
text-classification | transformers |
# Camembert-base fine-tuned on PAWS-X-fr for Paraphrase Identification (NLI)
| {"language": "fr", "tags": ["nli"], "datasets": ["xtreme"], "widget": [{"text": "La premi\u00e8re s\u00e9rie a \u00e9t\u00e9 mieux re\u00e7ue par la critique que la seconde. La seconde s\u00e9rie a \u00e9t\u00e9 bien accueillie par la critique, mieux que la premi\u00e8re."}]} | mrm8488/camembert-base-finetuned-pawsx-fr | null | [
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"camembert",
"text-classification",
"nli",
"fr",
"dataset:xtreme",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #dataset-xtreme #autotrain_compatible #endpoints_compatible #region-us
|
# Camembert-base fine-tuned on PAWS-X-fr for Paraphrase Identification (NLI)
| [
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"# Camembert-base fine-tuned on PAWS-X-fr for Paraphrase Identification (NLI)"
] |
summarization | transformers | # French RoBERTa2RoBERTa (shared) fine-tuned on MLSUM FR for summarization
## Model
[camembert-base](https://huggingface.co/camembert-base) (RoBERTa Checkpoint)
## Dataset
**MLSUM** is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in f... | {"language": "fr", "tags": ["summarization", "news"], "datasets": ["mlsum"], "widget": [{"text": "Un nuage de fum\u00e9e juste apr\u00e8s l\u2019explosion, le 1er juin 2019. Une d\u00e9flagration dans une importante usine d\u2019explosifs du centre de la Russie a fait au moins 79 bless\u00e9s samedi 1er juin. L\u2019ex... | mrm8488/camembert2camembert_shared-finetuned-french-summarization | null | [
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"fr",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #news #fr #dataset-mlsum #autotrain_compatible #endpoints_compatible #has_space #region-us
| French RoBERTa2RoBERTa (shared) fine-tuned on MLSUM FR for summarization
========================================================================
Model
-----
camembert-base (RoBERTa Checkpoint)
Dataset
-------
MLSUM is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it... | [
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fill-mask | transformers |
# *De Novo* Drug Design with MLM
## What is it?
An approximation to [Generative Recurrent Networks for De Novo Drug Design](https://onlinelibrary.wiley.com/doi/full/10.1002/minf.201700111) but training a MLM (RoBERTa like) from scratch.
## Why?
As mentioned in the paper:
Generative artificial intelligence models p... | {"language": "en", "tags": ["drugs", "chemist", "drug design"], "widget": [{"text": "CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)<mask>"}]} | mrm8488/chEMBL_smiles_v1 | null | [
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"drugs",
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"drug design",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
# *De Novo* Drug Design with MLM
## What is it?
An approximation to Generative Recurrent Networks for De Novo Drug Design but training a MLM (RoBERTa like) from scratch.
## Why?
As mentioned in the paper:
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, a... | [
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fill-mask | transformers |
# CodeBERTaJS
CodeBERTaJS is a RoBERTa-like model trained on the [CodeSearchNet](https://github.blog/2019-09-26-introducing-the-codesearchnet-challenge/) dataset from GitHub for `javaScript` by [Manuel Romero](https://twitter.com/mrm8488)
The **tokenizer** is a Byte-level BPE tokenizer trained on the corpus using Hu... | {"language": "code", "tags": ["javascript", "code"], "widget": [{"text": "async function createUser(req, <mask>) { if (!validUser(req.body.user)) { return res.status(400); } user = userService.createUser(req.body.user); return res.json(user); }"}]} | mrm8488/codeBERTaJS | null | [
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"fill-mask",
"javascript",
"code",
"arxiv:1909.09436",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.09436"
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"code"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #javascript #code #arxiv-1909.09436 #autotrain_compatible #endpoints_compatible #region-us
|
# CodeBERTaJS
CodeBERTaJS is a RoBERTa-like model trained on the CodeSearchNet dataset from GitHub for 'javaScript' by Manuel Romero
The tokenizer is a Byte-level BPE tokenizer trained on the corpus using Hugging Face 'tokenizers'.
Because it is trained on a corpus of code (vs. natural language), it encodes the cor... | [
"# CodeBERTaJS\n\nCodeBERTaJS is a RoBERTa-like model trained on the CodeSearchNet dataset from GitHub for 'javaScript' by Manuel Romero\n\nThe tokenizer is a Byte-level BPE tokenizer trained on the corpus using Hugging Face 'tokenizers'.\n\nBecause it is trained on a corpus of code (vs. natural language), it encod... | [
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text-classification | transformers |
# CodeBERT fine-tuned for Insecure Code Detection 💾⛔
[codebert-base](https://huggingface.co/microsoft/codebert-base) fine-tuned on [CodeXGLUE -- Defect Detection](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection) dataset for **Insecure Code Detection** downstream task.
## Details of [Cod... | {"language": "en", "datasets": ["codexglue"]} | mrm8488/codebert-base-finetuned-detect-insecure-code | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
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#transformers #pytorch #jax #roberta #text-classification #en #dataset-codexglue #arxiv-2002.08155 #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us
| CodeBERT fine-tuned for Insecure Code Detection
===============================================
codebert-base fine-tuned on CodeXGLUE -- Defect Detection dataset for Insecure Code Detection downstream task.
Details of CodeBERT
-------------------
We present CodeBERT, a bimodal pre-trained model for programming la... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #en #dataset-codexglue #arxiv-2002.08155 #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers |
# Codebert (base) fine-tuned this [dataset](https://aclanthology.org/2020.acl-main.443/) for NER
## Eval metrics
eval_accuracy_score = 0.9430622955139325
eval_precision = 0.6047440699126092
eval_recall = 0.6100755667506297
eval_f1 = 0.607398119122257
| {"language": "en", "license": "mit", "datasets": ["https://aclanthology.org/2020.acl-main.443/"], "widget": [{"text": "I want to create a table and ListView or ArrayList for Android or javascript in Windows 10"}]} | mrm8488/codebert-base-finetuned-stackoverflow-ner | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
# Codebert (base) fine-tuned this dataset for NER
## Eval metrics
eval_accuracy_score = 0.9430622955139325
eval_precision = 0.6047440699126092
eval_recall = 0.6100755667506297
eval_f1 = 0.607398119122257
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feature-extraction | transformers |
# ConvBERT base pre-trained on large_spanish_corpus
The ConvBERT architecture is presented in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper.
## Metrics on evaluation set
```
disc_accuracy = 0.9488542
disc_auc = 0.8833056
disc_loss = 0.15933733
disc_prec... | {"language": "es", "license": "mit", "datasets": ["large_spanish_corpus"]} | mrm8488/convbert-base-spanish | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.02496"
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"es"
] | TAGS
#transformers #pytorch #tf #convbert #feature-extraction #es #dataset-large_spanish_corpus #arxiv-2008.02496 #license-mit #endpoints_compatible #region-us
|
# ConvBERT base pre-trained on large_spanish_corpus
The ConvBERT architecture is presented in the "ConvBERT: Improving BERT with Span-based Dynamic Convolution" paper.
## Metrics on evaluation set
## Usage
> Created by Manuel Romero/@mrm8488 with the support of Narrativa
> Made with <span style="color: #e2555... | [
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feature-extraction | transformers |
# ConvBERT small pre-trained on large_spanish_corpus
The ConvBERT architecture is presented in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper.
## Metrics on evaluation set
```
disc_accuracy = 0.95163906
disc_auc = 0.9405496
disc_loss = 0.13658184
disc_pr... | {"language": "es", "license": "mit", "datasets": ["large_spanish_corpus"]} | mrm8488/convbert-small-spanish | null | [
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"region:us"
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#transformers #pytorch #tf #safetensors #convbert #feature-extraction #es #dataset-large_spanish_corpus #arxiv-2008.02496 #license-mit #endpoints_compatible #region-us
|
# ConvBERT small pre-trained on large_spanish_corpus
The ConvBERT architecture is presented in the "ConvBERT: Improving BERT with Span-based Dynamic Convolution" paper.
## Metrics on evaluation set
## Usage
> Created by Manuel Romero/@mrm8488 with the support of Narrativa
> Made with <span style="color: #e255... | [
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"# ConvBERT small pre-trained on large_spanish_corpus\n\nThe ConvBERT architecture is presented in the \"ConvBERT: Improving BERT with... |
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. -->
# deberta-v3-base-goemotions
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/de... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "deberta-v3-base-goemotions", "results": []}]} | mrm8488/deberta-v3-base-goemotions | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| deberta-v3-base-goemotions
==========================
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7610
* F1: 0.4468
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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. -->
# DeBERTa-v3-large fine-tuned on MNLI
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/mi... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "widget": [{"text": "She was badly wounded already. Another spear would take her down."}], "model-index": [{"name": "deberta-v3-large-mnli-2", "results": [{"task": {"type": "text-classification", "n... | mrm8488/deberta-v3-large-finetuned-mnli | null | [
"transformers",
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"deberta-v2",
"text-classification",
"generated_from_trainer",
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"arxiv:2006.03654",
"arxiv:2111.09543",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03654",
"2111.09543"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa-v3-large fine-tuned on MNLI
===================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6763
* Accuracy: 0.8949
Model description
-----------------
DeBERTa improves the B... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DeBERTa-v3-small fine-tuned on CoLA
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/mi... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "widget": [{"text": "They represented seriously to the dean Mary as a genuine linguist."}], "model-index": [{"name": "deberta-v3-small", "results": [{"task": {"type": "text-classificatio... | mrm8488/deberta-v3-small-finetuned-cola | null | [
"transformers",
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"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
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"arxiv:2006.03654",
"arxiv:2111.09543",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03654",
"2111.09543"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa-v3-small fine-tuned on CoLA
===================================
This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4051
* Matthews Correlation: 0.6333
Model description
-----------------
DeBERTa im... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparamet... |
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. -->
# DeBERTa v3 (small) fine-tuned on MNLI
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer", "deberta-v3"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "ds_results", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mn... | mrm8488/deberta-v3-small-finetuned-mnli | null | [
"transformers",
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"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"deberta-v3",
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"arxiv:2111.09543",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
... | null | 2022-03-02T23:29:05+00:00 | [
"2006.03654",
"2111.09543"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| DeBERTa v3 (small) fine-tuned on MNLI
=====================================
This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4985
* Accuracy: 0.8746
Model description
-----------------
DeBERTa improves t... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #arxiv-2006.03654 #arxiv-2111.09543 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hy... |
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. -->
# DeBERTa v3 (small) fine-tuned on MRPC
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer", "deberta-v3"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-v3-small", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue",... | mrm8488/deberta-v3-small-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"deberta-v3",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa v3 (small) fine-tuned on MRPC
=====================================
This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2787
* Accuracy: 0.8922
* F1: 0.9233
* Combined Score: 0.9078
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
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. -->
# DeBERTa-v3-small fine-tuned on QNLI
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/mi... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-small", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QNLI", "type": "glue", "args": "qnli"}, "m... | mrm8488/deberta-v3-small-finetuned-qnli | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa-v3-small fine-tuned on QNLI
===================================
This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2143
* Accuracy: 0.9151
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# DeBERTa v3 (small) fine-tuned on SST2
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer", "deberta-v3"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-small", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args... | mrm8488/deberta-v3-small-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"deberta-v3",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeBERTa v3 (small) fine-tuned on SST2
=====================================
This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2134
* Accuracy: 0.9404
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #deberta-v2 #text-classification #generated_from_trainer #deberta-v3 #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
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. -->
# deberta-v3-snall-goemotions
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "deberta-v3-snall-goemotions", "results": []}]} | mrm8488/deberta-v3-small-goemotions | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-snall-goemotions
===========================
This model is a fine-tuned version of microsoft/deberta-v3-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5638
* F1: 0.4241
Model description
-----------------
More information needed
Intended uses & limit... | [
"### 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 #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_... |
token-classification | transformers |
# DISTILBERT 🌎 + Typo Detection ✍❌✍✔
[distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) fine-tuned on [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) for **typo detection** (using *NER* style)
## Details of the downstream task (Typo detection as NER... | {"language": ["multilingual", "en", "zh", "ja", "ru", "fr", "de", "pt", "es", "ko", "hi"]} | mrm8488/distilbert-base-multi-cased-finetuned-typo-detection | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"multilingual",
"en",
"zh",
"ja",
"ru",
"fr",
"de",
"pt",
"es",
"ko",
"hi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"en",
"zh",
"ja",
"ru",
"fr",
"de",
"pt",
"es",
"ko",
"hi"
] | TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #multilingual #en #zh #ja #ru #fr #de #pt #es #ko #hi #autotrain_compatible #endpoints_compatible #region-us
| DISTILBERT + Typo Detection
===========================
distilbert-base-multilingual-cased fine-tuned on GitHub Typo Corpus for typo detection (using *NER* style)
Details of the downstream task (Typo detection as NER)
------------------------------------------------------
* Dataset: GitHub Typo Corpus for 15 lang... | [] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #token-classification #multilingual #en #zh #ja #ru #fr #de #pt #es #ko #hi #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# DistilBERT multilingual fine-tuned on TydiQA (GoldP task) dataset for multilingual Q&A 😛🌍❓
## Details of the language model
[distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased)
## Details of the Tydi QA dataset
TyDi QA contains 200k human-annotated question-answer p... | {"language": "multilingual"} | mrm8488/distilbert-multi-finetuned-for-xqua-on-tydiqa | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"multilingual",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #distilbert #question-answering #multilingual #endpoints_compatible #region-us
| DistilBERT multilingual fine-tuned on TydiQA (GoldP task) dataset for multilingual Q&A
======================================================================================
Details of the language model
-----------------------------
distilbert-base-multilingual-cased
Details of the Tydi QA dataset
--------------... | [] | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #multilingual #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# distilGPT-2 fine-tuned on Kaggle WSB Reddit posts dataset | {"language": "en", "tags": ["wsb", "tweets"], "widget": [{"text": "Come on guys this is"}]} | mrm8488/distilgpt2-finetuned-wsb-tweets | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"wsb",
"tweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #wsb #tweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilGPT-2 fine-tuned on Kaggle WSB Reddit posts dataset | [
"# distilGPT-2 fine-tuned on Kaggle WSB Reddit posts dataset"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #wsb #tweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilGPT-2 fine-tuned on Kaggle WSB Reddit posts dataset"
] |
question-answering | transformers |
# BETO (Spanish BERT) + Spanish SQuAD2.0 + distillation using 'bert-base-multilingual-cased' as teacher
This model is a fine-tuned on [SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve) and **distilled** version of [BETO](https://github.com/dccuchile/beto) for **Q&A**.
Distillation makes the model... | {"language": "es", "license": "apache-2.0", "thumbnail": "https://i.imgur.com/jgBdimh.png"} | mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es | null | [
"transformers",
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"jax",
"bert",
"question-answering",
"es",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #bert #question-answering #es #license-apache-2.0 #endpoints_compatible #has_space #region-us
| BETO (Spanish BERT) + Spanish SQuAD2.0 + distillation using 'bert-base-multilingual-cased' as teacher
=====================================================================================================
This model is a fine-tuned on SQuAD-es-v2.0 and distilled version of BETO for Q&A.
Distillation makes the model ... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\nPlay with this model and in a Colab:\n\n\n<a href=\"URL target=\"\\_parent\"><img src=\"URL alt=\"Open In Colab\" data-canonical-src=\"URL\n\n\n\n1. Set the context and ask some questions:\n\n\n!Set context and questions\n\n\n2. Run predictions:\n\n\n!Run th... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #es #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\nPlay with this model and in a Colab:\n\n\n<a href=\"URL target=\"\\_parent\"><img src=\"URL alt=\"Open In Colab\" data-can... |
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. -->
# distilroberta-base-finetuned-suicide-depression
This model is a fine-tuned version of [distilroberta-base](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "It's in the back of my mind. I'm not sure I'll be ok. Not sure I can deal with this. I'll try...I will try. Even though it's hard to see the point. But...this still isn't off the table."}], "model-index": [{"name... | mrm8488/distilroberta-base-finetuned-suicide-depression | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-suicide-depression
===============================================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6622
* Accuracy: 0.7158
Model description
-----------------
Just a PO... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text-classification | transformers |
# distilroberta-base fine-tuned on age_news dataset for news classification
Test set accuray: 0.94 | {"language": "en", "tags": ["news", "classification"], "datasets": ["ag_news"], "widget": [{"text": "Venezuela Prepares for Chavez Recall Vote Supporters and rivals warn of possible fraud; government says Chavez's defeat could produce turmoil in world oil market."}]} | mrm8488/distilroberta-finetuned-age_news-classification | null | [
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"roberta",
"text-classification",
"news",
"classification",
"en",
"dataset:ag_news",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #text-classification #news #classification #en #dataset-ag_news #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base fine-tuned on age_news dataset for news classification
Test set accuray: 0.94 | [
"# distilroberta-base fine-tuned on age_news dataset for news classification\n\nTest set accuray: 0.94"
] | [
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"# distilroberta-base fine-tuned on age_news dataset for news classification\n\nTest set accuray: 0.94"
] |
text-classification | transformers | # distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896
## How to use
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
ckpt = 'mrm8488/distilroberta-finetuned-banking77'
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = Au... | {"language": "en", "tags": ["banking", "intent", "multiclass"], "datasets": ["banking77"], "widget": [{"text": "How long until my transfer goes through?"}]} | mrm8488/distilroberta-finetuned-banking77 | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
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"banking",
"intent",
"multiclass",
"en",
"dataset:banking77",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #banking #intent #multiclass #en #dataset-banking77 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896
## How to use
> Created by Manuel Romero/@mrm8488 | LinkedIn
> Made with <span style="color: #e25555;">♥</span> in Spain | [
"# distilroberta-base fine-tuned on banking77 dataset for intent classification\nTest set accuray: 0.896",
"## How to use\n\n\n\n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with <span style=\"color: #e25555;\">♥</span> in Spain"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #banking #intent #multiclass #en #dataset-banking77 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# distilroberta-base fine-tuned on banking77 dataset for intent classification\nTest set accuray: 0.896",
"## How t... |
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. -->
<div style="text-align:center;width:250px;height:250px;">
<img src="https://huggingface.co/mrm8488/distilroberta-finetuned-fin... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "financial", "stocks", "sentiment"], "datasets": ["financial_phrasebank"], "metrics": ["accuracy"], "thumbnail": "https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis/resolve/main/logo_no_bg.png", "widget": [{"text": "Opera... | mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis | null | [
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #financial #stocks #sentiment #dataset-financial_phrasebank #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
DistilRoberta-financial-sentiment
=================================
This model is a fine-tuned version of distilroberta-base on the financial\_phrasebank dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1116
* Accuracy: 0.9823
Base Model description
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #financial #stocks #sentiment #dataset-financial_phrasebank #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe f... |
text-classification | transformers |
# distilroberta-base fine-tuned on tweets_hate_speech_detection dataset for hate speech detection
Validation accuray: 0.98 | {"language": "en", "tags": ["twitter", "hate", "speech"], "datasets": ["tweets_hate_speech_detection"], "widget": [{"text": "the fuck done with #mansplaining and other bullshit."}]} | mrm8488/distilroberta-finetuned-tweets-hate-speech | null | [
"transformers",
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"jax",
"roberta",
"text-classification",
"twitter",
"hate",
"speech",
"en",
"dataset:tweets_hate_speech_detection",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #twitter #hate #speech #en #dataset-tweets_hate_speech_detection #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# distilroberta-base fine-tuned on tweets_hate_speech_detection dataset for hate speech detection
Validation accuray: 0.98 | [
"# distilroberta-base fine-tuned on tweets_hate_speech_detection dataset for hate speech detection\n\nValidation accuray: 0.98"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #twitter #hate #speech #en #dataset-tweets_hate_speech_detection #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# distilroberta-base fine-tuned on tweets_hate_speech_detection dataset for hate speech detection\n\nValidation ... |
sentence-similarity | sentence-transformers |
# Distiluse-m-v2 fine-tuned on stsb_multi_mt for Spanish Semantic Textual Similarity
This is a [sentence-transformers](https://www.SBERT.net) model (distiluse-base-multilingual-cased-v2): It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic searc... | {"language": "es", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["stsb_multi_mt"], "thumbnail": "https://imgur.com/a/G77ZqQN", "pipeline_tag": "sentence-similarity"} | mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
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"dataset:stsb_multi_mt",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #es #dataset-stsb_multi_mt #endpoints_compatible #region-us
|
# Distiluse-m-v2 fine-tuned on stsb_multi_mt for Spanish Semantic Textual Similarity
This is a sentence-transformers model (distiluse-base-multilingual-cased-v2): It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-... | [
"# Distiluse-m-v2 fine-tuned on stsb_multi_mt for Spanish Semantic Textual Similarity\n\nThis is a sentence-transformers model (distiluse-base-multilingual-cased-v2): It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (S... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #es #dataset-stsb_multi_mt #endpoints_compatible #region-us \n",
"# Distiluse-m-v2 fine-tuned on stsb_multi_mt for Spanish Semantic Textual Similarity\n\nThis is a sentence-transformers model (distiluse-base-... |
question-answering | transformers |
# Electra base ⚡ + SQuAD v1 ❓
[Electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
**ELECTRA** is a new me... | {"language": "en"} | mrm8488/electra-base-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"en",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"en"
] | TAGS
#transformers #pytorch #electra #question-answering #en #arxiv-1406.2661 #endpoints_compatible #region-us
| Electra base + SQuAD v1
=======================
Electra-base-discriminator fine-tuned on SQUAD v1.1 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model
--------------------------------------------
ELECTRA is a new method for self-supervised language representation learning. It can be use... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #electra #question-answering #en #arxiv-1406.2661 #endpoints_compatible #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> Made with ♥ in Spain\n> \n> \n>"
] |
question-answering | transformers |
# Electra small ⚡ + SQuAD v1 ❓
[Electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
**ELECTRA** is a new... | {"language": "en"} | mrm8488/electra-small-finetuned-squadv1 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"en",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"en"
] | TAGS
#transformers #pytorch #electra #question-answering #en #arxiv-1406.2661 #endpoints_compatible #region-us
| Electra small + SQuAD v1
========================
Electra-small-discriminator fine-tuned on SQUAD v1.1 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model
--------------------------------------------
ELECTRA is a new method for self-supervised language representation learning. It can be ... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] | [
"TAGS\n#transformers #pytorch #electra #question-answering #en #arxiv-1406.2661 #endpoints_compatible #region-us \n",
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] |
question-answering | transformers |
# Electra small ⚡ + SQuAD v2 ❓
[Electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
**ELECTRA** is a ne... | {"language": "en", "license": "apache-2.0"} | mrm8488/electra-small-finetuned-squadv2 | null | [
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"pytorch",
"electra",
"question-answering",
"en",
"arxiv:1406.2661",
"license:apache-2.0",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"en"
] | TAGS
#transformers #pytorch #electra #question-answering #en #arxiv-1406.2661 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Electra small + SQuAD v2
========================
Electra-small-discriminator fine-tuned on SQUAD v2.0 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model
--------------------------------------------
ELECTRA is a new method for self-supervised language representation learning. It can be ... | [
"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \n>"
] | [
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"### Model in action\n\n\nFast usage with pipelines:\n\n\n\n> \n> Created by Manuel Romero/@mrm8488 | LinkedIn\n> \n> \n> \n\n\n\n> \n> Made with ♥ in Spain\n> \n> \... |
null | transformers |
## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
**Electricidad-base-discriminator** (uncased) is a ```base``` Electra like model (discriminator in this case) trained on a [Large Spanish Corpus](https://github.com/josecannete/spanish-corpora) (aka BETO's corpus)
As mentioned in the original [p... | {"language": "es", "tags": ["Spanish", "Electra"], "datasets": "-large_spanish_corpus", "thumbnail": "https://i.imgur.com/uxAvBfh.png"} | mrm8488/electricidad-base-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"Spanish",
"Electra",
"es",
"dataset:-large_spanish_corpus",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"es"
] | TAGS
#transformers #pytorch #electra #pretraining #Spanish #Electra #es #dataset--large_spanish_corpus #arxiv-1406.2661 #endpoints_compatible #region-us
| ELECTRICIDAD: The Spanish Electra Imgur
---------------------------------------
Electricidad-base-discriminator (uncased) is a Electra like model (discriminator in this case) trained on a Large Spanish Corpus (aka BETO's corpus)
As mentioned in the original paper:
ELECTRA is a new method for self-supervised languag... | [
"### Some models fine-tuned on a downstream task ️\n\n\nQuestion Answering\n\n\nPOS\n\n\nNER",
"### Spanish LM model comparison\n\n\n\nAcknowledgments\n---------------\n\n\nI thank /transformers team for allowing me to train the model (specially to Julien Chaumond).\n\n\nIf you want to cite this model you can use... | [
"TAGS\n#transformers #pytorch #electra #pretraining #Spanish #Electra #es #dataset--large_spanish_corpus #arxiv-1406.2661 #endpoints_compatible #region-us \n",
"### Some models fine-tuned on a downstream task ️\n\n\nQuestion Answering\n\n\nPOS\n\n\nNER",
"### Spanish LM model comparison\n\n\n\nAcknowledgments\n... |
text-classification | transformers |
# Electricidad (base) fine-tuned medical diagnostics | {"lang": "es", "widget": [{"text": "TUMOR DE COMPORTAMIENTO INCIERTO O DESCONOCIDO DEL HNGADO, DE LA VESNCULA BILIAR Y DEL CONDUCTO BILIAR - DiagnNstico Principal - Z01.8 OTROS EXNMENES ESPECIALES ESPECIFICADOS"}]} | mrm8488/electricidad-base-finetuned-medical-diagnostics | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# Electricidad (base) fine-tuned medical diagnostics | [
"# Electricidad (base) fine-tuned medical diagnostics"
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"# Electricidad (base) fine-tuned medical diagnostics"
] |
text-classification | transformers | # Electricidad-base fine-tuned for (Spanish) Sentiment Anlalysis 🎞️👍👎
[Electricidad](https://huggingface.co/mrm8488/electricidad-base-discriminator) base fine-tuned on [muchocine](https://huggingface.co/datasets/muchocine) dataset for Spanish **Sentiment Analysis** downstream task.
## Fast usage with `pipelines` ... | {"language": "es", "tags": ["sentiment", "analysis", "spanish"], "datasets": ["muchocine"], "widget": [{"text": "Una buena pel\u00edcula, sin m\u00e1s."}]} | mrm8488/electricidad-base-finetuned-muchocine | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"sentiment",
"analysis",
"spanish",
"es",
"dataset:muchocine",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #electra #text-classification #sentiment #analysis #spanish #es #dataset-muchocine #autotrain_compatible #endpoints_compatible #region-us
| # Electricidad-base fine-tuned for (Spanish) Sentiment Anlalysis ️
Electricidad base fine-tuned on muchocine dataset for Spanish Sentiment Analysis downstream task.
## Fast usage with 'pipelines'
| [
"# Electricidad-base fine-tuned for (Spanish) Sentiment Anlalysis ️\n\n\nElectricidad base fine-tuned on muchocine dataset for Spanish Sentiment Analysis downstream task.",
"## Fast usage with 'pipelines'"
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text-classification | transformers |
# Electricidad-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)
| {"language": "es", "tags": ["nli"], "datasets": ["xtreme"], "widget": [{"text": "El r\u00edo Tabaci es una vertiente del r\u00edo Leurda en Rumania. El r\u00edo Leurda es un afluente del r\u00edo Tabaci en Rumania."}]} | mrm8488/electricidad-base-finetuned-pawsx-es | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"nli",
"es",
"dataset:xtreme",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #electra #text-classification #nli #es #dataset-xtreme #autotrain_compatible #endpoints_compatible #region-us
|
# Electricidad-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)
| [
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"# Electricidad-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)"
] |
fill-mask | transformers |
## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
**Electricidad-base-generator** (uncased) is a ```base``` Electra like model (generator in this case) trained on a + 20 GB of the [OSCAR](https://oscar-corpus.com/) Spanish corpus.
As mentioned in the original [paper](https://openreview.net/pdf... | {"language": "es", "thumbnail": "https://i.imgur.com/uxAvBfh.png", "widget": [{"text": "Madrid es una ciudad muy [MASK] en Espa\u00f1a."}]} | mrm8488/electricidad-base-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"es",
"arxiv:1406.2661",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"es"
] | TAGS
#transformers #pytorch #electra #fill-mask #es #arxiv-1406.2661 #autotrain_compatible #endpoints_compatible #region-us
|
## ELECTRICIDAD: The Spanish Electra Imgur
Electricidad-base-generator (uncased) is a Electra like model (generator in this case) trained on a + 20 GB of the OSCAR Spanish corpus.
As mentioned in the original paper:
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre... | [
"## ELECTRICIDAD: The Spanish Electra Imgur\n\nElectricidad-base-generator (uncased) is a Electra like model (generator in this case) trained on a + 20 GB of the OSCAR Spanish corpus.\n\nAs mentioned in the original paper:\nELECTRA is a new method for self-supervised language representation learning. It can be us... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #es #arxiv-1406.2661 #autotrain_compatible #endpoints_compatible #region-us \n",
"## ELECTRICIDAD: The Spanish Electra Imgur\n\nElectricidad-base-generator (uncased) is a Electra like model (generator in this case) trained on a + 20 GB of the OSCAR Spanish corpu... |
null | transformers |
## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
**ELECTRICIDAD** is a small Electra like model (discriminator in this case) trained on a [Large Spanish Corpus](https://github.com/josecannete/spanish-corpora) (aka BETO's corpus).
As mentioned in the original [paper](https://openreview.net/pdf?... | {"language": "es", "tags": ["Spanish", "Electra"], "datasets": ["large_spanish_corpus"], "thumbnail": "https://i.imgur.com/uxAvBfh.png"} | mrm8488/electricidad-small-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"Spanish",
"Electra",
"es",
"dataset:large_spanish_corpus",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [
"es"
] | TAGS
#transformers #pytorch #electra #pretraining #Spanish #Electra #es #dataset-large_spanish_corpus #arxiv-1406.2661 #endpoints_compatible #region-us
| ELECTRICIDAD: The Spanish Electra Imgur
---------------------------------------
ELECTRICIDAD is a small Electra like model (discriminator in this case) trained on a Large Spanish Corpus (aka BETO's corpus).
As mentioned in the original paper:
ELECTRA is a new method for self-supervised language representation learn... | [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #Spanish #Electra #es #dataset-large_spanish_corpus #arxiv-1406.2661 #endpoints_compatible #region-us \n"
] |
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