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nejox/distilbert-base-uncased-distilled-squad-coffee20230108 | nejox | distilbert | 12 | 3 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,969 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-distilled-squad-coffee20230108
This model is a fine-tuned version of [distilbert-base-uncased-distilled-... | ac50711e4c51d792b26d642b1aa8a847 |
gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb_128 | gokuls | mobilebert | 17 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,040 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mobilebert_sa_GLUE_Experiment_logit_kd_stsb_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggin... | d67261525a22e75eab30846a0dbc5531 |
microsoft/xclip-base-patch16-hmdb-2-shot | microsoft | xclip | 10 | 2 | transformers | 0 | feature-extraction | true | false | false | mit | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['vision', 'video-classification'] | true | true | true | 2,425 | false |
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=2) on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](http... | c78f56c7cbd357af76c7855b4177f332 |
facebook/wmt19-en-ru | facebook | fsmt | 9 | 3,395 | transformers | 4 | translation | true | false | false | apache-2.0 | ['en', 'ru'] | ['wmt19'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation', 'wmt19', 'facebook'] | false | true | true | 3,248 | false |
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for en-ru.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT sta... | 09fd5ca751e6c96921792d1b942ec023 |
PeterBanning71/t5-small-finetuned-xsum-finetuned-bioMedv3 | PeterBanning71 | t5 | 12 | 8 | transformers | 0 | summarization | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization', 'generated_from_trainer'] | true | true | true | 2,181 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# t5-small-finetuned-xsum-finetuned-bioMedv3
This model is a fine-tuned version of [PeterBanning71/t5-small-finetuned-xsum](https:... | fd41830000499dbb6d5db2af04fc04e4 |
yip-i/xls-r-53-copy | yip-i | wav2vec2 | 6 | 1 | transformers | 0 | null | true | false | true | apache-2.0 | ['multilingual'] | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['speech'] | false | true | true | 2,197 | false |
# Wav2Vec2-XLSR-53
[Facebook's XLSR-Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned o... | fb0df48764b64890ae5c043865e65d6e |
google/t5-11b-ssm-wq | google | t5 | 9 | 8 | transformers | 1 | text2text-generation | true | true | false | apache-2.0 | ['en'] | ['c4', 'wikipedia', 'web_questions'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,413 | false |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | 421f2b02195337d45d10a6dd9600d571 |
josetapia/hygpt2-clm | josetapia | gpt2 | 17 | 4 | transformers | 0 | text-generation | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 980 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hygpt2-clm
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
... | 4e288d13e1a2a45f6aa2104c6a908f1d |
terzimert/bert-finetuned-ner-v2.2 | terzimert | bert | 12 | 7 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['caner'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,545 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-finetuned-ner-v2.2
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-mu... | c1a5f866053a6d759a96278f6c27ab14 |
openclimatefix/nowcasting_cnn_v4 | openclimatefix | null | 4 | 0 | transformers | 1 | null | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['nowcasting', 'forecasting', 'timeseries', 'remote-sensing'] | false | true | true | 962 | false |
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | 409a984bb15368014d80cc8164fc5303 |
Thant123/distilbert-base-uncased-finetuned-emotion | Thant123 | distilbert | 12 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,343 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | ffacf1d2dcc9b780be66d5ad7b68e5e2 |
philschmid/roberta-base-squad2-optimized | philschmid | null | 15 | 3 | generic | 0 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['endpoints-template', 'optimum'] | false | true | true | 9,622 | false |
# Optimized and Quantized [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) with a custom handler.py
This repository implements a `custom` handler for `question-answering` for 🤗 Inference Endpoints for accelerated inference using [🤗 Optiumum](https://huggingface.co/docs/optimum/inde... | 8561f0d74d18810e336a6fc8caf0ae6d |
MaggieXM/distilbert-base-uncased-finetuned-squad | MaggieXM | distilbert | 20 | 5 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,109 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | b71ec1cf30fd6b9f371d478067525884 |
jonatasgrosman/exp_w2v2t_pt_vp-nl_s6 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['pt'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'pt'] | false | true | true | 467 | false | # exp_w2v2t_pt_vp-nl_s6
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your... | 7a000bd97bc0b74b5287e62948946ec7 |
hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch8-ep10 | hsohn3 | bert | 8 | 4 | transformers | 0 | fill-mask | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,340 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch8-ep10
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | d1d10ad0216333d9b17d1427aae2e8d4 |
shumail/wav2vec2-base-timit-demo-colab | shumail | wav2vec2 | 24 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,341 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | 21419281cc9c65d8413aab2df9d3ffbe |
fveredas/xlm-roberta-base-finetuned-panx-de | fveredas | xlm-roberta | 16 | 5 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,320 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | 91b7a03e208a0ae34eca0e47fccabdb1 |
kurianbenoy/music_genre_classification_baseline | kurianbenoy | null | 4 | 0 | fastai | 1 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['fastai'] | false | true | true | 736 | false |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | f36361bbf3a4111abdeda44875b284bc |
mkhairil/distillbert-finetuned-indonlusmsa | mkhairil | distilbert | 12 | 8 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['indonlu'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 948 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distillbert-finetuned-indonlusmsa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | 42b35bdf153d04de3bdfd39be5fd4cfc |
Alred/bart-base-finetuned-summarization-cnn-ver2 | Alred | bart | 15 | 5 | transformers | 0 | summarization | true | false | false | apache-2.0 | null | ['cnn_dailymail'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization', 'generated_from_trainer'] | true | true | true | 1,176 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bart-base-finetuned-summarization-cnn-ver2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/fac... | 25269342aaf4d9a62e88d8b1b5ab5e8a |
manandey/wav2vec2-large-xlsr-_irish | manandey | wav2vec2 | 9 | 7 | transformers | 0 | automatic-speech-recognition | true | false | true | apache-2.0 | ['ga'] | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | true | true | true | 3,265 | false | # Wav2Vec2-Large-XLSR-53-Irish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Irish using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used ... | 5019d14cdddfee6804b9e3be5a44eb38 |
Helsinki-NLP/opus-mt-it-vi | Helsinki-NLP | marian | 11 | 38 | transformers | 0 | translation | true | true | false | apache-2.0 | ['it', 'vi'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 2,001 | false |
### ita-vie
* source group: Italian
* target group: Vietnamese
* OPUS readme: [ita-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-vie/README.md)
* model: transformer-align
* source language(s): ita
* target language(s): vie
* model: transformer-align
* pre-processing: normalization... | 449a6592d1e9c61ddf102c80ed93f5c6 |
coreml/coreml-stable-diffusion-v1-5 | coreml | null | 6 | 0 | null | 5 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['coreml', 'stable-diffusion', 'text-to-image'] | false | true | true | 13,867 | false |
# Core ML Converted Model
This model was converted to Core ML for use on Apple Silicon devices by following Apple's instructions [here](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).<br>
Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusio... | ff6dc79182f70d5525127385a73ba0ee |
jonatasgrosman/exp_w2v2t_pl_wavlm_s250 | jonatasgrosman | wavlm | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['pl'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'pl'] | false | true | true | 439 | false | # exp_w2v2t_pl_wavlm_s250
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at ... | 2ae287b7aad1344a15917389c6575372 |
Manishkalra/finetuning-movie-sentiment-model-9000-samples | Manishkalra | distilbert | 13 | 7 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['imdb'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,061 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuning-movie-sentiment-model-9000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | 7ef4c70555b92d4568f030df0ffc5331 |
gokuls/mobilebert_add_GLUE_Experiment_logit_kd_pretrain_rte | gokuls | mobilebert | 17 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,629 | false |
<!-- 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. -->
# mobilebert_add_GLUE_Experiment_logit_kd_pretrain_rte
This model is a fine-tuned version of [gokuls/mobilebert_add_pre-training-c... | d0cde83f8c26abb80591ae721ba50e2a |
Sahara/finetuning-sentiment-model-3000-samples | Sahara | distilbert | 13 | 12 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['imdb'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,055 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | 900f912994c36c1ea4886ea41a8f8ee4 |
Nadav/camembert-base-squad-fr | Nadav | camembert | 10 | 7 | transformers | 0 | question-answering | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,226 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# camembert-base-squad-fr
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the Non... | 27076f2df421437249dcc32fb253bc30 |
jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-10_female-0_s626 | jonatasgrosman | wav2vec2 | 10 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['fr'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'fr'] | false | true | true | 499 | false | # exp_w2v2r_fr_vp-100k_gender_male-10_female-0_s626
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using... | 64dffcb720500b36cba63de43180e27a |
karolill/nb-bert-finetuned-on-norec | karolill | bert | 8 | 4 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 630 | false | # NB-BERT fine-tuned on NoReC
## Description
This model is based on the pre-trained [NB-BERT-large model](https://huggingface.co/NbAiLab/nb-bert-large?text=P%C3%A5+biblioteket+kan+du+l%C3%A5ne+en+%5BMASK%5D.). It is a model for sentiment analysis.
## Data for fine-tuning
This model was fine-tuned on 1000 exemples ... | db8876a697ac2ee74b1e4f99bfbae95c |
lmvasque/readability-es-benchmark-mbert-es-sentences-3class | lmvasque | bert | 9 | 5 | transformers | 0 | text-classification | true | false | false | cc-by-4.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 6,041 | false |
## Readability benchmark (ES): mbert-es-sentences-3class
This project is part of a series of models from the paper "A Benchmark for Neural Readability Assessment of Texts in Spanish".
You can find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
## Models
Our mo... | febbe796a5f094ef6ad3bf1db2d17a6a |
AshishBalhara/distilbert-base-uncased-distilled-clinc | AshishBalhara | distilbert | 10 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['clinc_oos'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,730 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | 9073c9738e9938c44e81a35e81987bb6 |
kaejo98/bart-base-question-generation | kaejo98 | bart | 11 | 20 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,038 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-question-generation
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-ba... | 57de9dffeaa30d6dea822e0166a216b0 |
Gausstein26/wav2vec2-base-50k | Gausstein26 | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,845 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-50k
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-... | 39af85ca3a18ca97b7a4395f05670bd2 |
microsoft/resnet-34 | microsoft | resnet | 6 | 689 | transformers | 2 | image-classification | true | true | false | apache-2.0 | null | ['imagenet-1k'] | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['vision', 'image-classification'] | false | true | true | 2,572 | false |
# ResNet-34 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been wri... | 3a6fe139d3e20966c9c19b9645d70dca |
torchxrayvision/densenet121-res224-rsna | torchxrayvision | null | 4 | 2 | null | 0 | image-classification | false | false | false | apache-2.0 | null | ['nih-pc-chex-mimic_ch-google-openi-rsna'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['vision', 'image-classification'] | false | true | true | 3,755 | false |
# densenet121-res224-rsna
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional input... | 505e2b3064723731502eeedb68525169 |
luhua/chinese_pretrain_mrc_macbert_large | luhua | bert | 7 | 960 | transformers | 7 | question-answering | true | false | false | apache-2.0 | ['zh'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 848 | false |
## Chinese MRC macbert-large
* 使用大量中文MRC数据训练的macbert-large模型，详情可查看：https://github.com/basketballandlearn/MRC_Competition_Dureader
* 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高<br/>
（已有多位小伙伴在Dureader-2021等多个比赛中取得**top5**的成绩😁）
| 模型/数据集 | Dureader-2021 | tencentmedical |
| -----------------------... | 92981deba62ebeb06ea120c1d0cea854 |
sudheer997/distilbert-base-uncased-finetuned-emotion | sudheer997 | distilbert | 12 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,344 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | 09e96c41e3c29e83426fd262ed70d129 |
Yehor/wav2vec2-xls-r-300m-uk-with-lm | Yehor | wav2vec2 | 19 | 9 | transformers | 3 | automatic-speech-recognition | true | false | false | apache-2.0 | ['uk'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'uk'] | true | true | true | 2,482 | false |
# Ukrainian STT model (with Language Model)
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
- Have a look on an updated 300m model: https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-l... | ddc10d9e0cf5c2d5baf616b10f77be7e |
it5/it5-efficient-small-el32-repubblica-to-ilgiornale | it5 | t5 | 18 | 3 | transformers | 0 | text2text-generation | true | true | true | apache-2.0 | ['it'] | ['gsarti/change_it'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['italian', 'sequence-to-sequence', 'efficient', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | true | true | true | 4,264 | false | # IT5 Cased Small Efficient EL32 for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹
*Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!*
This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingf... | 70db3b7ae2d781068dff302cf9b67401 |
LysandreJik/testing | LysandreJik | distilbert | 24 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,061 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# testing
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the G... | e5aa2fe722ebd3f2e3beefe57cd8446a |
SfinOe/stable-diffusion-v2-1 | SfinOe | null | 18 | 15 | diffusers | 0 | text-to-image | false | false | false | openrail++ | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'text-to-image'] | false | true | true | 12,114 | false |
# Stable Diffusion v2-1 Model Card
This model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available [here](https://github.com/Stability-AI/stablediffusion).
This `stable-diffusion-2-1` model is fine-tuned from [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffu... | fe9e1cdc45333400917d375824dbe07a |
hamidov02/wav2vec2-large-xls-hun-53h-colab | hamidov02 | wav2vec2 | 9 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 3,350 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-large-xls-hun-53h-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/... | af63d676d2f0b56d1f5c8d55d2bef6d9 |
Charalampos/whisper-new | Charalampos | whisper | 14 | 2 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['el'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,317 | false |
<!-- 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. -->
# Whisper Tiny Greek
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on th... | 81ff0cb7249db2f1c03fa77c00aba031 |
PaddyP/distilbert-base-uncased-finetuned-emotion | PaddyP | distilbert | 12 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,335 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | 273a7d6292504c7de9d52f0f6e59d80c |
sd-concepts-library/manga-style | sd-concepts-library | null | 13 | 0 | null | 6 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,424 | false | ### Manga style on Stable Diffusion
This is the `<manga>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also ... | b9cfe7378f4a2aebab4af1e914f23ef6 |
google/multiberts-seed_2-step_1000k | google | bert | 8 | 33 | transformers | 0 | null | true | true | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1000k'] | false | true | true | 3,527 | false |
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | eda5f85611a7e6697f8395dc9df69d3f |
milyiyo/multi-minilm-finetuned-amazon-review | milyiyo | bert | 35 | 3 | transformers | 0 | text-classification | true | false | false | mit | null | ['amazon_reviews_multi'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,826 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# multi-minilm-finetuned-amazon-review
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://hugg... | bbea158b23d3bd1f242a66420d2e24b5 |
thomas0104/whisper_large_v2_zh_tw | thomas0104 | whisper | 31 | 10 | transformers | 1 | automatic-speech-recognition | true | false | false | apache-2.0 | ['zh'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,769 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper large-v2 zh-tw
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-lar... | 98f37609420e3ef0611174e3c40e0038 |
Helsinki-NLP/opus-mt-tc-big-itc-eu | Helsinki-NLP | marian | 13 | 4 | transformers | 0 | translation | true | true | false | cc-by-4.0 | ['es', 'eu'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation', 'opus-mt-tc'] | true | true | true | 7,012 | false | # opus-mt-tc-big-itc-eu
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | 1ed70a370ef245ed1976a60c852add9a |
scasutt/wav2vec2-large-xlsr-53_toy_train_data_fast_10pct | scasutt | wav2vec2 | 7 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,418 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-large-xlsr-53_toy_train_data_fast_10pct
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https:/... | ad6cf658f624b957a20d1f76b1637d68 |
kyonimouto/hoyu-ai | kyonimouto | null | 9 | 0 | null | 0 | null | false | false | false | other | ['ja'] | ['hoyu256'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['PyTorch'] | false | true | true | 801 | false |
Diffusion GANというコードを使ってつくりました
https://github.com/Zhendong-Wang/Diffusion-GAN
つかいかた
試してないので動かなかったらごめんなさい
- 環境をととのえる
- 最近のNVIDIA製GPUがついたパソコンにLinuxを入れることをおすすめします
- PytorchをCUDAありでインストールしてください
- https://pytorch.org/get-started/locally/
- conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytor... | 5820d86ecd93456d7e8e645e11ad9c1b |
fathyshalab/all-roberta-large-v1-meta-1-16-5 | fathyshalab | roberta | 11 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,507 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# all-roberta-large-v1-meta-1-16-5
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://hugg... | 00bb1723ac003e09ff759f2718cdffd8 |
sd-dreambooth-library/quino | sd-dreambooth-library | null | 65 | 62 | diffusers | 7 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 1 | 1 | 0 | 0 | 2 | 2 | 0 | ['text-to-image'] | false | true | true | 6,144 | false | ### quino Dreambooth model trained by machinelearnear with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/note... | f7376fefdb0e0fbc960890f4996a147f |
T-Systems-onsite/cross-en-de-roberta-sentence-transformer | T-Systems-onsite | xlm-roberta | 10 | 106,465 | transformers | 14 | feature-extraction | true | true | false | mit | ['de', 'en', 'multilingual'] | ['stsb_multi_mt'] | null | 2 | 0 | 2 | 0 | 1 | 1 | 0 | ['sentence_embedding', 'search', 'pytorch', 'xlm-roberta', 'roberta', 'xlm-r-distilroberta-base-paraphrase-v1', 'paraphrase'] | false | true | true | 7,627 | false |
# Cross English & German RoBERTa for Sentence Embeddings
This model is intended to [compute sentence (text) embeddings](https://www.sbert.net/examples/applications/computing-embeddings/README.html) for English and German text. These embeddings can then be compared with [cosine-similarity](https://en.wikipedia.org/wiki... | 39507c8ae82169a34201c6131c064719 |
gokuls/distilbert_add_GLUE_Experiment_mrpc | gokuls | distilbert | 17 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,301 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert_add_GLUE_Experiment_mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/disti... | 9a7f7f884ae4664ffba19c3a471bd26f |
jonatasgrosman/exp_w2v2t_fr_xls-r_s250 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['fr'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'fr'] | false | true | true | 453 | false | # exp_w2v2t_fr_xls-r_s250
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input ... | fd90a5b962e7ad7a21c5907fb71b16bd |
tbosse/bert-base-german-cased-noisy-pretrain-fine-tuned_v1.2 | tbosse | bert | 13 | 5 | transformers | 0 | token-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,040 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-german-cased-noisy-pretrain-fine-tuned_v1.2
This model is a fine-tuned version of [tbosse/bert-base-german-cased-finet... | 12a2266c62d438271e4b1db92134a6a7 |
slplab/wav2vec2-large-xlsr-53-korean-nia13-asia-9634_001 | slplab | wav2vec2 | 11 | 7 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,215 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-large-xlsr-53-korean-samsung-60k
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggin... | aa6ec52905440e0969c9a8fafc6cf76e |
ZinebSN/whisper-small-swedish-Test-3000 | ZinebSN | whisper | 41 | 6 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['sv'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | true | true | true | 1,422 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Small Swedish -3000
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-s... | fa8176c8386386acd76dd2a3c9c6097c |
gokuls/mobilebert_add_GLUE_Experiment_logit_kd_qqp_256 | gokuls | mobilebert | 17 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,201 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mobilebert_add_GLUE_Experiment_logit_kd_qqp_256
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggin... | 8466e0635a43675a0b34bc6de87930da |
tkubotake/xlm-roberta-base-finetuned-panx-fr | tkubotake | xlm-roberta | 9 | 8 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,375 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://... | 0ceb5eeffb629c282138058fc91c160c |
andreduarte/distilbert-base-uncased-finetuned-cola | andreduarte | distilbert | 13 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,571 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | 816c3a137dbc59a8a3c0d2e72b4ccb58 |
google/multiberts-seed_3-step_20k | google | bert | 8 | 14 | transformers | 0 | null | true | true | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_20k'] | false | true | true | 3,515 | false |
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 20k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different... | 5054951bf7630834ee3b7576a7d32102 |
Zekunli/flan-t5-large-extraction-cnndm_fs0.1-all | Zekunli | t5 | 10 | 10 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,397 | false |
<!-- 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. -->
# flan-t5-large-extraction-cnndm_fs0.1-all
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/goo... | bbfcc5bbc997d13f85a7be0129ab2efa |
praf-choub/bart-CaPE-xsum | praf-choub | bart | 9 | 5 | transformers | 0 | summarization | true | false | false | bsd-3-clause | ['en'] | ['xsum'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization'] | false | true | true | 630 | false |
Citation
```
@misc{https://doi.org/10.48550/arxiv.2110.07166,
doi = {10.48550/ARXIV.2110.07166},
url = {https://arxiv.org/abs/2110.07166},
author = {Choubey, Prafulla Kumar and Fabbri, Alexander R. and Vig, Jesse and Wu, Chien-Sheng and Liu, Wenhao and Rajani, Nazneen Fatema},
keywords = {Computation and Langu... | 2468d26b38f2b16cbf690b197616995b |
DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1 | DrishtiSharma | wav2vec2 | 12 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['as'] | ['mozilla-foundation/common_voice_8_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | true | true | true | 3,861 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-large-xls-r-300m-as-g1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | 06836b810af39f2736d85422bdc5412c |
adiharush/tu-nlpweb-w22-g18-e6 | adiharush | distilbert | 8 | 16 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 920 | false |
<!-- 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. -->
# result
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unk... | c4d6bc071fd5b40401576730a87dbdee |
PaddlePaddle/uie-medium | PaddlePaddle | ernie | 7 | 0 | paddlenlp | 0 | null | false | false | false | apache-2.0 | ['zh'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 4,353 | false |
[](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/uie-medium
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified ... | 23016a311de271c35d1cf8e0e7c41f1a |
Salesforce/blip2-flan-t5-xl-coco | Salesforce | blip-2 | 11 | 7 | transformers | 1 | image-to-text | true | false | false | mit | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | true | true | 2,029 | false |
# BLIP-2, Flan T5-xl, fine-tuned on COCO
BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) b... | 16fd192d2c19228210efc52bbf85be93 |
TestZee/t5-small-finetuned-xum-test | TestZee | t5 | 7 | 3 | transformers | 0 | text2text-generation | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,169 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# TestZee/t5-small-finetuned-xum-test
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown da... | bc3616ed76b830d500dcfc4b1075ec34 |
sd-concepts-library/sherhook-painting-v2 | sd-concepts-library | null | 14 | 0 | null | 3 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,648 | false | ### Sherhook Painting v2 on Stable Diffusion
This is the `<sherhook>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. Y... | cf40b9acce2f8d693d45b053c3e2bc82 |
EIStakovskii/french_toxicity_classifier_plus | EIStakovskii | camembert | 8 | 6 | transformers | 0 | text-classification | true | false | false | other | ['fr'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 940 | false | This model was trained for toxicity labeling. Label_1 means TOXIC, Label_0 means NOT TOXIC
The model was fine-tuned based off [the CamemBERT language model](https://huggingface.co/camembert-base).
The accuracy is 93% on the test split during training and 79% on a manually picked (and thus harder) sample of 200 senten... | b6b0ef02d30aa35570f0dceb32f4b53d |
Najeen/bert-finetuned-ner | Najeen | bert | 16 | 13 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['conll2003'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,518 | false |
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# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | 8621ab6ec6418bf39fed49c8333196f3 |
yingqin/wav2vec2-base-timit-eng | yingqin | wav2vec2 | 11 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,984 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-base-timit-eng
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-... | eab774a0e012c13bad9929acc0880242 |
itzo/bert-base-uncased-fine-tuned-on-clinc_oos-dataset | itzo | bert | 14 | 0 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['clinc_oos'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,623 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-uncased-fine-tuned-on-clinc_oos-dataset
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.... | ac01da7b3425e866fb056ed1a1333feb |
jonatasgrosman/exp_w2v2r_de_xls-r_accent_germany-10_austria-0_s728 | jonatasgrosman | wav2vec2 | 10 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['de'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'de'] | false | true | true | 481 | false | # exp_w2v2r_de_xls-r_accent_germany-10_austria-0_s728
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make ... | aeaedfa8904f849d3fd51e5afd8c2ca9 |
Froddan/frost | Froddan | null | 12 | 0 | null | 3 | text-to-image | false | false | false | cc0-1.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'text-to-image'] | false | true | true | 1,425 | false |
# Stable Diffusion fine tuned on photographs of frozen nature
### Usage
Use by adding the keyword "frostography" to the prompt. The model was trained with the "nature" classname, which can also be added to the prompt.
## Samples
I hope it gives you an idea of what kind of styles can be created with this model.
<img... | b20d8a7549f6f3dcb45240890a11804d |
surfingdoggo/ddpm-butterflies-128 | surfingdoggo | null | 13 | 0 | diffusers | 0 | null | false | false | false | apache-2.0 | ['en'] | ['huggan/smithsonian_butterflies_subset'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,234 | false |
<!-- This model card has been generated automatically according to the information the training script had access to. You
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# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | 2fc542ff3b4b4e735376953a7950d023 |
MultiBertGunjanPatrick/multiberts-seed-4-400k | MultiBertGunjanPatrick | bert | 7 | 4 | transformers | 0 | null | true | false | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | true | true | 6,483 | false | # MultiBERTs Seed 4 Checkpoint 400k (uncased)
Seed 4 intermediate checkpoint 400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | f4fc4d69fb7b99c4f447db75fa1586f2 |
sd-concepts-library/isabell-schulte-pviii-12tiles-3000steps-style | sd-concepts-library | null | 17 | 0 | null | 0 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,723 | false | ### Isabell Schulte - PVIII - 12tiles - 3000steps - Style on Stable Diffusion
This is the `<isabell-schulte-p8-style-12tiles-3000s>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/ma... | e67a4e0608960c550db5dfa918350859 |
Celal11/resnet-50-finetuned-FER2013-0.003-CKPlus | Celal11 | resnet | 9 | 9 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['image_folder'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,424 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# resnet-50-finetuned-FER2013-0.003-CKPlus
This model is a fine-tuned version of [Celal11/resnet-50-finetuned-FER2013-0.003](https... | 258f8c517ed1cc09d06c87b9abe4f706 |
nvia/distilbert-base-uncased-finetuned-cola | nvia | distilbert | 13 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,571 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | bc8ac23fd5c6facf328aed17a638f8e1 |
cahya/gpt2-small-indonesian-522M | cahya | gpt2 | 10 | 195 | transformers | 3 | text-generation | true | true | true | mit | ['id'] | ['Indonesian Wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 3,000 | false |
# Indonesian GPT2 small model
## Model description
It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-tra... | be8f290cf1935e1ff95a2058d3a46791 |
rmihaylov/gpt2-small-theseus-bg | rmihaylov | gpt2 | 10 | 6 | transformers | 0 | text-generation | true | false | false | mit | ['bg'] | ['oscar', 'chitanka', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['torch'] | false | true | true | 2,748 | false |
# GPT-2
Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-langua... | d5d2d5d2d97953e6a68ef4d84c6f1ced |
askainet/bart_lfqa | askainet | bart | 8 | 259 | transformers | 1 | text2text-generation | true | false | false | mit | ['en'] | ['vblagoje/lfqa', 'vblagoje/lfqa_support_docs'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 3,175 | false |
## Introduction
See [blog post](https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb) for more details.
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModel, AutoModelForSeq2SeqLM
model_name = "vblagoje/bart_lfqa"
device = torch.device('cu... | 5300d763d13a5da45266d46acf0e6fad |
Helsinki-NLP/opus-mt-tiv-fr | Helsinki-NLP | marian | 10 | 7 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 776 | false |
### opus-mt-tiv-fr
* source languages: tiv
* target languages: fr
* OPUS readme: [tiv-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tiv-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](... | 7b67510791791498937cfc16d663c61f |
chintagunta85/test_ner3 | chintagunta85 | distilbert | 12 | 5 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['pv_dataset'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 3,115 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# test_ner3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the... | 8a0cc7b56d4a0f40c904b187353855c0 |
MeshalAlamr/wav2vec2-xls-r-300m-ar-9 | MeshalAlamr | wav2vec2 | 11 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,848 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-xls-r-300m-ar-9
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wa... | a6f363e939775d062d96c1642c6d9774 |
kevinbror/bertbaseuncasedny | kevinbror | bert | 4 | 5 | transformers | 0 | question-answering | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 2,332 | false |
<!-- 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. -->
# bertbaseuncasedny
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown da... | c15bb7d6eec15ca4812bb1e404ab0af5 |
YSKartal/bert-base-turkish-cased-turkish_offensive_trained_model | YSKartal | bert | 10 | 3 | transformers | 1 | text-classification | false | true | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,633 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# YSKartal/bert-base-turkish-cased-turkish_offensive_trained_model
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased]... | 91d9eacdbd242cca4314a93c35532887 |
vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts | vijayv500 | gpt2 | 8 | 5 | transformers | 0 | conversational | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['conversational'] | false | true | true | 1,376 | false | ## I fine-tuned DialoGPT-small model on "The Big Bang Theory" TV Series dataset from Kaggle (https://www.kaggle.com/mitramir5/the-big-bang-theory-series-transcript)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("vijayv500/DialoGPT-small... | d240c7a766b3932490dcabd61a635eb1 |
gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_mrpc | gokuls | mobilebert | 17 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,362 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mobilebert_sa_GLUE_Experiment_logit_kd_mrpc
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingfac... | 57cffc13464954ffb0d98f2e3dca23b1 |
NhatPham/wav2vec2-base-finetuned-ks | NhatPham | wav2vec2 | 10 | 7 | transformers | 0 | audio-classification | true | false | false | apache-2.0 | null | ['superb'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,559 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | 6e1f259c065268bc3b6931280804e637 |
igorcadelima/distilbert-base-uncased-finetuned-emotion | igorcadelima | distilbert | 12 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,338 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | 852a2b2c1ba227bf1245d6203986ed9c |
hsohn3/ehr-bert-base-uncased-cchs-wordlevel | hsohn3 | bert | 8 | 2 | transformers | 1 | fill-mask | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,544 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# hsohn3/ehr-bert-base-uncased-cchs-wordlevel
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base... | 8a9301a30bbdf63cfb3e69f4b2fa51e9 |
emilios/whisper-medium-el-n3 | emilios | whisper | 101 | 25 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['el'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,983 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper medium Greek El Greco
This model is a fine-tuned version of [emilios/whisper-medium-el-n2](https://huggingface.co/emilio... | fdba85ecbbebd93a2ae4b94d1eeaa4f2 |
nlpie/bio-miniALBERT-128 | nlpie | bert | 8 | 3 | transformers | 0 | fill-mask | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,117 | false |
# Model
miniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency.
Since miniALBERT is a compact model, it is trained using a layer-to-layer distillation technique, using the BioBERT-v1.1 model as the teac... | 196599766a4fa28ee1ed67e75b376edc |
Lucapro/test-model | Lucapro | t5 | 13 | 8 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | ['en', 'ro'] | ['wmt16'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,017 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tst-translation
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 ro-en dataset.
It... | dd23cd43c9fcd35b06757c9be3491225 |
google/multiberts-seed_0-step_1700k | google | bert | 8 | 22 | transformers | 0 | null | true | true | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1700k'] | false | true | true | 3,527 | false |
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with differe... | 0f1f2445c07c2f221b49efc529d7efd5 |
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