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emilios/whisper-md-hu | emilios | whisper | 24 | 2 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['hu'] | ['mozilla-foundation/common_voice_11_0', 'google/fleurs'] | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,919 | 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 medium Hungarian El Greco
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/wh... | 048ba60184a4972f12b605ebadcb9810 |
fathyshalab/all-roberta-large-v1-small_talk-4-16-5 | 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 | 1,515 | 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. -->
# all-roberta-large-v1-small_talk-4-16-5
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https:... | c9475561f73f747809f916eb745a5f4b |
izumi-lab/electra-small-paper-japanese-discriminator | izumi-lab | electra | 7 | 2 | transformers | 1 | null | true | false | false | cc-by-sa-4.0 | ['ja'] | ['wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,883 | false |
# ELECTRA small Japanese discriminator
This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language.
The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0).
## Model architecture
... | d70713d25806f33491dc6f35afa6548d |
google/vit-large-patch32-224-in21k | google | vit | 7 | 178 | transformers | 0 | feature-extraction | true | true | true | apache-2.0 | null | ['imagenet-21k'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['vision'] | false | true | true | 4,911 | false |
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy e... | 84091ad5428754341e4553cacf13c19f |
SetFit/distilbert-base-uncased__sst2__train-8-8 | SetFit | distilbert | 10 | 6 | 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 | 1,888 | 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__sst2__train-8-8
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | 3d128a710b293768f077c9011f60cbef |
Helsinki-NLP/opus-mt-fr-gaa | Helsinki-NLP | marian | 10 | 8 | 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-fr-gaa
* source languages: fr
* target languages: gaa
* OPUS readme: [fr-gaa](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-gaa/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](... | afed9b58ae82a983b513301878ff26ad |
KarelDO/roberta-base.CEBaB_confounding.observational.sa.5-class.seed_43 | KarelDO | roberta | 15 | 2 | transformers | 0 | null | true | false | false | mit | ['en'] | ['OpenTable'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,108 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-base.CEBaB_confounding.observational.sa.5-class.seed_43
This model is a fine-tuned version of [roberta-base](https://hug... | 1bdb004791cf26de3d6a0111ecd62c03 |
JeremiahZ/bert-base-uncased-mrpc | JeremiahZ | bert | 17 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 2 | 0 | 2 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,712 | 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. -->
# bert-base-uncased-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | 015e27db50493ea3793fa508cf3d2723 |
k3nneth/finetuning-sentiment-model-3000-samples | k3nneth | distilbert | 16 | 11 | 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 | 1,053 | 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... | cae0bec1c7620c8a11b9a9291ffc0f43 |
anas-awadalla/bart-base-few-shot-k-128-finetuned-squad-seed-4 | anas-awadalla | bart | 16 | 3 | 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 | 991 | 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-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.c... | b40162d679d8964e5786eb649f403fd8 |
GItaf/bert-base-uncased-bert-base-uncased-mc-weight0.25-epoch2 | GItaf | bert | 17 | 2 | transformers | 0 | text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 924 | 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. -->
# bert-base-uncased-bert-base-uncased-mc-weight0.25-epoch2
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | 11f2e10f4179c3321644f7f61a745c3f |
InternalMegaT/Brazier_Diffusion | InternalMegaT | null | 3 | 0 | null | 2 | text-to-image | false | false | false | creativeml-openrail-m | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | true | true | 2,285 | false | #MODEL BY InternalMegaT
How to use: **_brazier_** "your prompt" **_, by Svetoslav Roerich, generative art, aspect ratio 16:9, fortnite art style, stylized layered shapes, warm color scheme art rendition, an ai generated image, by jake parker_**
Training on V1 - 3000 steps, 512x512, v1-5 Base, 13 images
Uploaded on 1... | 4670641c94bd4122173365bd91fa05d9 |
arijitx/wav2vec2-xls-r-300m-bengali | arijitx | wav2vec2 | 37 | 64 | transformers | 1 | automatic-speech-recognition | true | false | false | apache-2.0 | ['bn'] | ['openslr', 'SLR53', 'AI4Bharat/IndicCorp'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'bn', 'hf-asr-leaderboard', 'openslr_SLR53', 'robust-speech-event'] | true | true | true | 2,368 | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set.
Without language model :
- WER: 0.21726385291857586
- CER: 0.04725010353701041
With 5 gram langua... | 460637fc234bcbb0796671ebcd5886cd |
tensorspeech/tts-mb_melgan-kss-ko | tensorspeech | null | 4 | 0 | tensorflowtts | 1 | text-to-speech | false | false | false | apache-2.0 | ['ko'] | ['KSS'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | true | true | 2,193 | false |
# Multi-band MelGAN trained on KSS (Korean)
This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on KSS dataset (ko). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS... | 6c4035ee6c1382614de9a1402229653b |
tomXBE/bert-finetuned-squad_2 | tomXBE | distilbert | 12 | 5 | 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 | 980 | 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. -->
# bert-finetuned-squad_2
This model is a fine-tuned version of [tomXBE/distilbert-base-uncased-finetuned-squad](https://huggingfac... | b88491c289b4e5f95b4c4581222bc0ad |
gcmsrc/distilbert-base-uncased-finetuned-emotion | gcmsrc | 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 | 1,345 | 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | 35c8a0b594224be94b670854b7b356d4 |
SebastianS/distilbert-base-uncased-finetuned-imdb | SebastianS | distilbert | 8 | 4 | transformers | 0 | fill-mask | true | false | false | apache-2.0 | null | ['imdb'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,159 | 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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | 3d58d1d71998e3c696f1888733f26f0c |
venetis/distilbert-base-uncased_finetuned_disaster_tweets | venetis | distilbert | 14 | 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 | 1,422 | 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_disaster_tweets
This model is a fine-tuned version of [distilbert-base-uncased](https://huggin... | 17c967052e73d9b0df89f4a2fa871c7e |
vumichien/mobilebert-uncased-squad-v2 | vumichien | mobilebert | 7 | 165 | transformers | 0 | question-answering | false | true | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 865 | 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. -->
# tf-mobilebert-uncased-squad-v2
This model is a fine-tuned version of [csarron/mobilebert-uncased-squad-v2](https://huggingface.co/csar... | 29ca6f1566af31915c4c0cec1a7e478c |
Chikashi/t5-small-finetuned-cnndm1 | Chikashi | t5 | 11 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['cnn_dailymail'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,055 | 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. -->
# t5-small-finetuned-cnndm1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail... | 13b7bba082c5507b11d0b67975323d15 |
pcuenq/coreml-stable-diffusion-2-1-base | pcuenq | null | 104 | 0 | null | 1 | text-to-image | false | false | false | other | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | true | true | 8,867 | false |
# Stable Diffusion v2 Model Card
This model was generated by Hugging Face using [Apple’s repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md).
This model card focuses on the model associated with the Stable Diffusion v2.1 model,... | 7b768279bed0250608e9410cd9d91eb3 |
Drazcat/whisper-small-es | Drazcat | whisper | 19 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['es'] | ['Drazcat/Cencosud'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | true | true | true | 1,462 | 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 Small Es - GoCloud
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-sm... | f705a626d461edc70ce27b7f7afc31d7 |
sentence-transformers/distiluse-base-multilingual-cased-v1 | sentence-transformers | distilbert | 15 | 174,180 | sentence-transformers | 14 | sentence-similarity | true | true | false | apache-2.0 | ['multilingual'] | null | null | 1 | 1 | 0 | 0 | 1 | 1 | 0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | true | true | 2,205 | false |
# sentence-transformers/distiluse-base-multilingual-cased-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model become... | 6e4503b762b84a2a4e2692ddbcebbdc1 |
yuhuizhang/finetuned_gpt2-large_sst2_negation0.2 | yuhuizhang | gpt2 | 11 | 5 | transformers | 0 | text-generation | true | false | false | mit | null | ['sst2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,248 | 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. -->
# finetuned_gpt2-large_sst2_negation0.2
This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on t... | 9603b4d930836579e429523e1f82eda2 |
Helsinki-NLP/opus-mt-fi-no | Helsinki-NLP | marian | 11 | 38 | transformers | 0 | translation | true | true | false | apache-2.0 | ['fi', False] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 2,099 | false |
### fin-nor
* source group: Finnish
* target group: Norwegian
* OPUS readme: [fin-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-nor/README.md)
* model: transformer-align
* source language(s): fin
* target language(s): nno nob
* model: transformer-align
* pre-processing: normalizat... | f5982d0dd4f5d39b7382e88c4f849f4a |
pig4431/IMDB_DistilBERT_5E | pig4431 | distilbert | 10 | 7 | 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 | 10,815 | 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. -->
# IMDB_DistilBERT_5EE
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncas... | 4b33fabf17949e00311f38ce43b256b2 |
nishantyadav/cls_crossencoder_zeshel | nishantyadav | null | 3 | 0 | null | 0 | null | false | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 441 | false | This repo contains the cross-encoder model which uses \[cls\]-token based pooling to score a query-item pair.
This model is used in the experiments for our EMNLP 2022 paper titled "[Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization](https://arxiv.org/pdf/2210.12579.pdf)".
See [pap... | 08bb62b7d34ed7537d6fa044d37f534d |
espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char | espnet | null | 34 | 1 | espnet | 0 | null | false | false | false | cc-by-4.0 | ['en'] | ['chime4'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'speech-enhancement-recognition'] | false | true | true | 13,323 | false |
## ESPnet2 EnhS2T model
### `espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char`
This model was trained by simpleoier using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 2b663318cd1773fb8685b1... | 7c63a6b381aa05b947ba012c6ae9621a |
jbreunig/xlm-roberta-base-finetuned-panx-de | jbreunig | 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 | 1,314 | 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | 45c0c78c58705d301b013ed518f7066e |
anas-awadalla/distilroberta-base-task-specific-distilation-on-squad | anas-awadalla | roberta | 32 | 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 | 962 | 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. -->
# distilroberta-base-task-specific-distilation-on-squad
This model is a fine-tuned version of [distilroberta-base](https://hugging... | d7cc6c1af862bd8ba74b5caf040cd7b1 |
csarron/roberta-base-squad-v1 | csarron | roberta | 10 | 181 | transformers | 0 | question-answering | true | false | true | mit | ['en'] | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['question-answering', 'roberta', 'roberta-base'] | false | true | true | 2,411 | false |
## RoBERTa-base fine-tuned on SQuAD v1
This model was fine-tuned from the HuggingFace [RoBERTa](https://arxiv.org/abs/1907.11692) base checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
This model is case-sensitive: it makes a difference between english and English.
## Details
| Dataset | Split ... | 14e8fcc27a5ed545053ccaadb923abd2 |
Helsinki-NLP/opus-mt-zh-en | Helsinki-NLP | marian | 12 | 162,987 | transformers | 70 | translation | true | true | false | cc-by-4.0 | ['zh', 'en'] | null | null | 3 | 1 | 1 | 1 | 1 | 1 | 0 | ['translation'] | false | true | true | 3,102 | false |
### zho-eng
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [How to Get Started With the Model](#how-to-get-started-with-the-mod... | 3ec52a58e11a0072e5ec5de1a9e888d9 |
neelan-elucidate-ai/wav2vec2-tcrs | neelan-elucidate-ai | wav2vec2 | 10 | 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 | 2,980 | 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-tcrs
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-larg... | 083b67d4eb21983fa41f50b6403ecb45 |
anas-awadalla/bert-base-uncased-few-shot-k-64-finetuned-squad-seed-4 | anas-awadalla | bert | 16 | 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 | 1,000 | 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. -->
# bert-base-uncased-few-shot-k-64-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | 6ca0dc834e39a7313276f3ed8fa8f903 |
jonatasgrosman/exp_w2v2t_fa_vp-fr_s165 | jonatasgrosman | wav2vec2 | 10 | 7 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['fa'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'fa'] | false | true | true | 469 | false | # exp_w2v2t_fa_vp-fr_s165
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that yo... | 8c903bff2661e6e0b135851d9e57d8c9 |
ThatGuyVanquish/mt5-base-finetuned-rabbi-kook-nave-4 | ThatGuyVanquish | mt5 | 11 | 5 | 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 | 1,397 | false |
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# mt5-base-finetuned-rabbi-kook-nave-4
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-b... | e6c887d58be0c023daca439cba1fc002 |
sschet/biomedical-ner-all | sschet | distilbert | 8 | 7 | transformers | 0 | token-classification | true | false | false | apache-2.0 | ['en'] | ['tner/bc5cdr', 'commanderstrife/jnlpba', 'bc2gm_corpus', 'drAbreu/bc4chemd_ner', 'linnaeus', 'chintagunta85/ncbi_disease'] | 0.0279399890043426 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['Token Classification'] | false | true | true | 1,449 | false |
## About the Model
An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased
- Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942
... | 21d0b25d28068dccbee2e11a4e02ff3e |
Geotrend/bert-base-en-de-cased | Geotrend | bert | 8 | 1,451 | transformers | 0 | fill-mask | true | true | true | apache-2.0 | ['multilingual'] | ['wikipedia'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,292 | false |
# bert-base-en-de-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly th... | 6cad93fd4e52515edb7d3fe3a86f865f |
l3cube-pune/hindi-tweets-bert-v2 | l3cube-pune | bert | 8 | 4 | transformers | 0 | fill-mask | true | false | false | cc-by-4.0 | ['hi'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 552 | false |
## HindTweetBERT
A HindBERT (l3cube-pune/hindi-bert-v2) model finetuned on Hindi Tweets.<br>
More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2210.04267'> link </a>)<br>
```
@article{gokhale2022spread,
title={Spread Love Not Hate: Undermining the I... | 1b457a9014efcb374a37cacfd8c694da |
Graphcore/lxmert-vqa-uncased | Graphcore | lxmert | 14 | 1 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['Graphcore/vqa-lxmert'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 3,944 | false |
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# Graphcore/lxmert-vqa-uncased
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-op... | d8e35078e8ee0cc0645dae920da9c20e |
Matthijs/mobilevit-small | Matthijs | mobilevit | 8 | 6 | transformers | 0 | image-classification | true | false | false | other | null | ['imagenet-1k'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['vision', 'image-classification'] | false | true | true | 4,423 | false |
# MobileViT (small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](ht... | 91fbbc2e6e5447f91edb7186368ec6f3 |
W4nkel/distilbertBase128KTrain | W4nkel | distilbert | 8 | 1 | transformers | 0 | text-classification | false | true | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,615 | false |
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# W4nkel/distilbertBase128KTrain
This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmd... | a6c2e7d6b835faa64c075bdbe0f8e761 |
kompactss/JeBERT_ko_je_v2 | kompactss | encoder-decoder | 7 | 1 | transformers | 0 | text2text-generation | true | false | false | afl-3.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 732 | false |
# 🍊 제주 방언 번역 모델 🍊
- 표준어 -> 제주어
- Made by. 구름 자연어처리 과정 3기 3조!!
- github link : https://github.com/Goormnlpteam3/JeBERT
## 1. Seq2Seq Transformer Model
- encoder : BertConfig
- decoder : BertConfig
- Tokenizer : WordPiece Tokenizer
## 2. Dataset
- Jit Dataset
- AI HUB(+아래아 문자)_v2
## 3.... | a5b95519c32c5ac5fffe4732cd9b31d8 |
anas-awadalla/t5-base-few-shot-k-256-finetuned-squad-infilling-seed-4 | anas-awadalla | t5 | 17 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 965 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# t5-base-few-shot-k-256-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://hugg... | 5dcb7a2d61a3d8cde604553b3150832f |
zhiguoxu/chinese-macbert-base-finetuned-ner | zhiguoxu | bert | 218 | 6 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,357 | false |
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# chinese-macbert-base-finetuned-ner
This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co/hfl/c... | 2a463960b4873fcfcfd597ff81f9c2f7 |
Helsinki-NLP/opus-mt-tc-big-en-pt | Helsinki-NLP | marian | 13 | 3,251 | transformers | 4 | translation | true | true | false | cc-by-4.0 | ['en', 'pt', 'pt_br'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['translation', 'opus-mt-tc'] | true | true | true | 5,634 | false | # opus-mt-tc-big-en-pt
Neural machine translation model for translating from English (en) to Portuguese (pt).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | f45be0cd5669a4b113d710e511bf949e |
gokuls/bert-base-uncased-mrpc | gokuls | bert | 17 | 73 | 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 | 2,061 | 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-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | 8d7a27554db4dfb535b333e658cfded3 |
transformersbook/distilbert-base-uncased-finetuned-clinc | transformersbook | distilbert | 47 | 53 | transformers | 1 | 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 | 1,838 | 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | f347d9bc19ca04737cd515774e8f2231 |
gcmsrc/xlm-roberta-base-finetuned-panx-fr | gcmsrc | xlm-roberta | 10 | 13 | 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-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | 1140ef4f8127d67f70b904f222ee2b96 |
m-aliabbas/idrak_wav2vec_tr | m-aliabbas | wav2vec2 | 13 | 7 | 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 | 1,058 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# idrak_wav2vec_tr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-x... | 6e4bb4a8c2691c05cfcd139b600ecc59 |
SetFit/distilbert-base-uncased__sst2__train-32-2 | SetFit | distilbert | 10 | 5 | 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 | 2,137 | false |
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# distilbert-base-uncased__sst2__train-32-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | 0d3d33e01df81430bc0ebe65da897672 |
anton-l/wav2vec2-large-xlsr-53-chuvash | anton-l | wav2vec2 | 9 | 8 | transformers | 0 | automatic-speech-recognition | true | false | true | apache-2.0 | ['cv'] | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | true | true | true | 3,724 | false |
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chuvash using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | 7efeceeea52fc8412ced499ef42a9c9f |
WALIDALI/asmagalally-with-protogen-v2-2 | WALIDALI | null | 18 | 8 | diffusers | 0 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['text-to-image', 'stable-diffusion'] | false | true | true | 441 | false | ### Asmagalally-with-Protogen-v2.2- Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/... | 1aa2f288e4a2d94761f2a31e558b2849 |
muhtasham/tiny-mlm-glue-stsb-target-glue-mrpc | muhtasham | bert | 10 | 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 | 1,643 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tiny-mlm-glue-stsb-target-glue-mrpc
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-stsb](https://huggingface.co/... | e7d94714f8f33eb35efcc8610a09e800 |
asapp/sew-d-mid-400k | asapp | sew-d | 5 | 31 | transformers | 1 | feature-extraction | true | false | false | apache-2.0 | ['en'] | ['librispeech_asr'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['speech'] | false | true | true | 1,699 | false |
# SEW-D-mid
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
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 on a downstream task, like Automatic Speech Recognition, Speak... | 3ea3c5d66233dfbfb7aff8575436b206 |
MarioPenguin/bert-model-english1 | MarioPenguin | bert | 8 | 7 | transformers | 0 | text-classification | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,462 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# bert-model-english1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown data... | 0b852fec4a973ed5dc1425d625b8d9e5 |
anas-awadalla/roberta-base-few-shot-k-16-finetuned-squad-seed-10 | anas-awadalla | roberta | 17 | 6 | transformers | 0 | question-answering | true | false | false | mit | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 986 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-base-few-shot-k-16-finetuned-squad-seed-10
This model is a fine-tuned version of [roberta-base](https://huggingface.co/r... | 43e7b551d18145546b48735148db9da6 |
scasutt/wav2vec2-base_toy_train_data_augmented | 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 | 2,390 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-base_toy_train_data_augmented
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/fac... | cfc6e71bd7ab5e9f7c8b82a44c4c74e2 |
sd-concepts-library/roblox-avatar | sd-concepts-library | null | 10 | 0 | null | 1 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,257 | false | ### Roblox avatar on Stable Diffusion
why am i spending time making these?, anyways.
This is the `<roblox-avatar>` 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/stab... | b24717809dc59e098e97bcd19616a555 |
adityavithaldas/distilbert-base-uncased-finetuned-ner | adityavithaldas | distilbert | 11 | 13 | transformers | 1 | token-classification | true | false | false | apache-2.0 | null | ['conll2003'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | false | true | true | 930 | 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | 7a2173108a35872520c76a54cb3813ec |
polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc-new | polejowska | swin | 11 | 1 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,709 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# swin-tiny-patch4-window7-224-lcbsi-wbc-new
This model is a fine-tuned version of [polejowska/swin-tiny-patch4-window7-224-lcbsi-... | 571c16bb87d562f958279ef3fd7e2997 |
AkashKhamkar/InSumT510k | AkashKhamkar | t5 | 7 | 1 | transformers | 0 | text2text-generation | true | false | false | afl-3.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 888 | false | ---
About :
This model can be used for text summarization.
The dataset on which it was fine tuned consisted of 10,323 articles.
The Data Fields :
- "Headline" : title of the article
- "articleBody" : the main article content
- "source" : the link to the readmore page.
The data splits were :
- Train : 8258.... | f19a50db7e0b912f0f5a488eff5c7e5f |
anas-awadalla/roberta-large-data-seed-0 | anas-awadalla | roberta | 17 | 3 | transformers | 0 | question-answering | true | false | false | mit | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,028 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-large-data-seed-0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squ... | fdde17cdd471889cf7d09d07bc5348d2 |
anas-awadalla/roberta-base-few-shot-k-128-finetuned-squad-seed-42 | anas-awadalla | roberta | 13 | 5 | transformers | 0 | question-answering | true | false | false | mit | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,041 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-base-few-shot-k-128-finetuned-squad-seed-42
This model is a fine-tuned version of [roberta-base](https://huggingface.co/... | 16650d889caab94e4ea52460c9d251e3 |
lmqg/flan-t5-small-squad-ae | lmqg | t5 | 13 | 5 | transformers | 0 | text2text-generation | true | false | false | cc-by-4.0 | ['en'] | ['lmqg/qg_squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['answer extraction'] | true | true | true | 4,375 | false |
# Model Card of `lmqg/flan-t5-small-squad-ae`
This model is fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) for answer extraction on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-... | 224f7ad3a025255cbe91c101491e0314 |
WillHeld/t5-base-vanilla-cstop_artificial | WillHeld | mt5 | 11 | 4 | 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 | 1,953 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# t5-base-vanilla-cstop_artificial
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base)... | d57079ed1326c09958e679b24d89c6ab |
muhtasham/tiny-vanilla-target-glue-wnli | muhtasham | bert | 10 | 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 | 1,438 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tiny-vanilla-target-glue-wnli
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/g... | 88a346a2792f245696171a00f6d98940 |
kohya-ss/kawase-hasui-diffusion | kohya-ss | null | 8 | 0 | null | 5 | text-to-image | false | false | false | creativeml-openrail-m | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'text-to-image'] | false | true | true | 1,323 | false |
Kawase Hasui Diffusion is trained on pantings by [KAWASE Hasui（川瀬巴水）](https://en.wikipedia.org/wiki/Hasui_Kawase).
The model has been trained on Stable Diffusion v2-1 with DreamBooth method with a learning rate of 1.0e-6 for 2,600 steps with the batch size of 8 (8 train or reg images) on 169 training images and 664 re... | 086d7eb54501527320b043b047d1b708 |
sd-concepts-library/thunderdome-cover | sd-concepts-library | null | 42 | 0 | null | 2 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,178 | false | ### thunderdome-cover on Stable Diffusion
This is the `<thunderdome-cover>` 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) noteb... | d03f878f97e393d669e915a38a430438 |
ali2066/finetuned_token_2e-05_16_02_2022-14_25_47 | ali2066 | distilbert | 13 | 10 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,787 | 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. -->
# finetuned_token_2e-05_16_02_2022-14_25_47
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english... | 41bca4d58f49ff76cc57d3a5caf88950 |
keras-io/pointnet_segmentation | keras-io | null | 6 | 8 | keras | 2 | null | false | false | false | cc0-1.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['pointnet', 'segmentation', '3d', 'image'] | false | true | true | 1,990 | false | ## Point cloud segmentation with PointNet
This repo contains [an Implementation of a PointNet-based model for segmenting point clouds.](https://keras.io/examples/vision/pointnet_segmentation/).
Full credits to [Soumik Rakshit](https://github.com/soumik12345), [Sayak Paul](https://github.com/sayakpaul)
## Background... | 42b96852d159c991508ee0650972b821 |
timm/convnext_large.fb_in1k | timm | null | 4 | 471 | timm | 0 | image-classification | true | false | false | apache-2.0 | null | ['imagenet-1k'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['image-classification', 'timm'] | false | true | true | 21,330 | false | # Model card for convnext_large.fb_in1k
A ConvNeXt image classification model. Pretrained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 197.8
- GMACs: 34.4
- Activations (M): 43.1
- Image size: 224 x 224
- **Papers... | d53d486a6ab822a34689167675fbd898 |
sztanki/hulk-style-v3 | sztanki | null | 37 | 10 | diffusers | 0 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 2 | 1 | 1 | 0 | 0 | 0 | 0 | ['text-to-image'] | false | true | true | 2,211 | false | ### hulk-style-v3 Dreambooth model trained by sztanki 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... | 41e38a897ea87b586fe7a4356e3484c5 |
flamesbob/rimu_model | flamesbob | null | 3 | 0 | null | 0 | null | false | false | false | creativeml-openrail-m | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 945 | false | Token class word for this model is `rimu` using this will draw attention to the training data that was used and help increase the quality of the image.
License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License ... | c33132429820d76803cdf9d449148e20 |
anas-awadalla/bart-base-finetuned-squad-infilling-lr-5e-6-decay-01 | anas-awadalla | bart | 18 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,065 | 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-squad-infilling-lr-5e-6-decay-01
This model is a fine-tuned version of [facebook/bart-base](https://huggingf... | ce91f9ffaac74170882cded277d86aef |
KoichiYasuoka/roberta-classical-chinese-large-upos | KoichiYasuoka | roberta | 8 | 9 | transformers | 0 | token-classification | true | false | false | apache-2.0 | ['lzh'] | ['universal_dependencies'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | true | true | 1,239 | false |
# roberta-classical-chinese-large-upos
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [roberta-classical-chinese-large-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-char). Every word is tagged by [U... | 5669f9d115e4e069f6ffb4250b49e876 |
kaisuke/finetuning-sentiment-model-3000-samples | kaisuke | distilbert | 13 | 9 | 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 | 1,053 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | 29d831044ffcad3b4eb00868a8707eaa |
nandysoham/Gregorian_calendar-theme-finetuned-overfinetuned | nandysoham | distilbert | 10 | 4 | 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 | 3,273 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# nandysoham/Gregorian_calendar-theme-finetuned-overfinetuned
This model is a fine-tuned version of [nandysoham/distilbert-base-uncased-... | e253d57aad2cb713c9187105fcaf9a99 |
asi/albert-act-base | asi | albert_act | 9 | 2 | transformers | 1 | null | true | true | false | apache-2.0 | ['en'] | ['wikipedia', 'bookcorpus'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | true | true | true | 2,690 | false |
# Adaptive Depth Transformers
Implementation of the paper "How Many Layers and Why? An Analysis of the Model Depth in Transformers". In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of ... | f563522b392d36031d7aee2be607b42c |
nimrah/wav2vec2-large-xls-r-300m-my_hindi_home-colab | nimrah | wav2vec2 | 12 | 7 | 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 | 1,111 | 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-my_hindi_home-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggi... | 6a5b1094b45d4a59effe4ebf57c576ea |
muhtasham/small-vanilla-target-glue-cola-linear-probe | muhtasham | bert | 10 | 5 | 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 | 1,538 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# small-vanilla-target-glue-cola-linear-probe
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://hu... | 95290fcccf9b2a6e42778bf11525917e |
timm/efficientformer_l3.snap_dist_in1k | timm | null | 4 | 17 | timm | 0 | image-classification | true | false | false | apache-2.0 | null | ['imagenet-1k'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['image-classification', 'timm'] | false | true | true | 3,519 | false | # Model card for efficientformer_l3.snap_dist_in1k
A EfficientFormer image classification model. Pretrained with distillation on ImageNet-1k.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 31.4
- GMACs: 3.9
- Activations (M): 12.0
- Image size: 224 ... | 12a7131f375305df7256d1d2191b6b34 |
porpaul/t5-small-finetuned-xsum | porpaul | t5 | 11 | 4 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['xlsum'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,365 | 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
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xlsum dataset.
... | 293a1b2f0a69971006c77c5f10b9fea0 |
jonatasgrosman/exp_w2v2t_pt_wavlm_s118 | jonatasgrosman | wavlm | 10 | 3 | 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 | 439 | false | # exp_w2v2t_pt_wavlm_s118
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) 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 speech input is sampled at ... | e6edf14121895ca2912118f15ca05a88 |
UGARIT/grc-alignment | UGARIT | xlm-roberta | 8 | 8 | transformers | 0 | fill-mask | true | false | false | cc-by-4.0 | null | null | null | 1 | 0 | 0 | 1 | 0 | 0 | 0 | [] | false | true | true | 2,759 | false | # Automatic Translation Alignment of Ancient Greek Texts
GRC-ALIGNMENT model is an XLM-RoBERTa-based model, fine-tuned for automatic multilingual text alignment at the word level.
The model is trained on 12 million monolingual ancient Greek tokens with Masked Language Model (MLM) training objective. Further, the model... | 2740ef2bd51db4cd72b5cab55969a1fc |
IsaMaks/distilbert-base-uncased-finetuned-ner | IsaMaks | distilbert | 15 | 9 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 6,873 | 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | 1d9d092daaf12928d97ca9d0c02225e7 |
tftransformers/albert-xxlarge-v1 | tftransformers | null | 6 | 2 | null | 0 | null | false | false | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 6,481 | false |
# ALBERT XXLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not m... | e66851dd6b5079b79877ab72ab0e02c4 |
Kugos/KgSelfie_lr_15e-6 | Kugos | null | 35 | 2 | diffusers | 0 | null | true | false | false | mit | null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['pytorch', 'diffusers', 'dreambooth'] | false | true | true | 2,151 | false |
# Model Card for Dreambooth model trained on My pet Pintu's images
This model is a diffusion model for unconditional image generation of my cute pet dog Pintu trained using Dreambooth concept. The token to use is sks .
## Usage
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pr... | 11f97347539e3820b3fc0389ac78c145 |
Sebabrata/donut-base-sroie-s | Sebabrata | vision-encoder-decoder | 14 | 0 | transformers | 0 | null | true | false | false | mit | null | ['imagefolder'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 972 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# donut-base-sroie-s
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut... | a35b7618bcb67fcf969d3511a177420a |
jonatasgrosman/exp_w2v2t_en_xls-r_s957 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['en'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'en'] | false | true | true | 459 | false | # exp_w2v2t_en_xls-r_s957
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech ... | b67a18c09274f143a8405bac952104d8 |
anas-awadalla/roberta-large-houlsby-few-shot-k-1024-finetuned-squad-seed-2 | anas-awadalla | null | 19 | 0 | null | 0 | null | false | false | false | mit | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,095 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-large-houlsby-few-shot-k-1024-finetuned-squad-seed-2
This model is a fine-tuned version of [roberta-large](https://huggi... | 53a7152803e402f5f1e24046f75677f9 |
maastrichtlawtech/legal-distilcamembert | maastrichtlawtech | camembert | 8 | 12 | transformers | 0 | fill-mask | true | false | false | cc-by-sa-4.0 | ['fr'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['legal'] | false | true | true | 1,322 | false |
# Legal-CamemBERT
* Legal-DistilCamemBERT is a [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base)-based model further pre-trained on [23,000+ statutory articles](https://huggingface.co/datasets/maastrichtlawtech/bsard) from the Belgian legislation.
* We chose the following training set-up: 50k tra... | 2b3c207eb61eae2001c201b283162afa |
DOOGLAK/Tagged_One_50v8_NER_Model_3Epochs_AUGMENTED | DOOGLAK | bert | 13 | 5 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['tagged_one50v8_wikigold_split'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,563 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Tagged_One_50v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | 907b2dca38fd3e287e0d41f27fb66188 |
devtanumisra/finetuning-profane-model-deberta | devtanumisra | deberta-v2 | 14 | 9 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,108 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuning-profane-model-deberta
This model is a fine-tuned version of [yangheng/deberta-v3-base-absa-v1.1](https://huggingface.... | a528d7a8cb1e379db04173edd1ab2b0f |
MatsUy/wav2vec2-common_voice-nl-demo | MatsUy | wav2vec2 | 15 | 10 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['nl'] | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | true | true | true | 2,098 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-common_voice-nl-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | fb73f6cabe9295250a4c6bc31690ab44 |
jonfrank/mt5-small-finetuned-amazon-en-es | jonfrank | mt5 | 16 | 4 | 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 | 2,101 | 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. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | 798d2fcf6401e16b6ddd2db209ea4e66 |
bergurth/IceBERT-finetuned-ner | bergurth | roberta | 15 | 7 | transformers | 0 | token-classification | true | false | false | gpl-3.0 | null | ['mim_gold_ner'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,528 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | 9af5931d296106a9f419529cae812339 |
Ramos-Ramos/emb-gam-vit | Ramos-Ramos | null | 5 | 0 | sklearn | 0 | tabular-classification | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['sklearn', 'skops', 'tabular-classification', 'visual emb-gam'] | false | true | true | 9,821 | false |
# Model description
This is a LogisticRegressionCV model trained on averages of patch embeddings from the Imagenette dataset. This forms the GAM of an [Emb-GAM](https://arxiv.org/abs/2209.11799) extended to images. Patch embeddings are meant to be extracted with the [`google/vit-base-patch16-224` ViT checkpoint](http... | 7f24989726caf0368bca27d6590c1261 |
unicamp-dl/ptt5-base-en-pt-msmarco-100k-v2 | unicamp-dl | t5 | 7 | 3 | transformers | 0 | text2text-generation | true | false | false | mit | ['pt'] | ['msmarco'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | true | true | 1,366 | false | # PTT5-base Reranker finetuned on both English and Portuguese MS MARCO
## Introduction
ptt5-base-msmarco-en-pt-100k-v2 is a T5-based model pretrained in the BrWac corpus, fine-tuned on both English and Portuguese translated version of MS MARCO passage dataset. In the v2 version, the Portuguese dataset was translated us... | 0f3761d0cec355b8bd58401f3ccc3e4b |
gokuls/mobilebert_add_GLUE_Experiment_mrpc | gokuls | mobilebert | 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 | 2,178 | 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_mrpc
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/goo... | aa9b228bae9640748f4fb0680ac55cba |
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