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Qalam/Lei | Qalam | null | 2 | 0 | null | 0 | text-to-image | false | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 28,989 | false | <p align="center">
<br>
<img src="./docs/source/en/imgs/diffusers_library.jpg" width="400"/>
<br>
<p>
<p align="center">
<a href="https://github.com/huggingface/diffusers/blob/main/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue">
</... | 5649976f381a19c93af23495becb8bf5 |
nateraw/vit-base-patch16-224-cifar10 | nateraw | vit | 5 | 300 | transformers | 4 | image-classification | true | false | false | apache-2.0 | null | ['cifar10'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['image-classification', 'vision', 'pytorch'] | false | true | true | 2,211 | false |
# Vision Transformer Fine Tuned on CIFAR10
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) and **fine-tuned on CIFAR10** at resolution 224x224.
Check out the code at my [my Github repo](https://github.com/nateraw/huggingface-vit-finetune).
## Usage
```python
from tran... | a7720f05c366487247c0c8ddec5f5f70 |
jeapaul/languagemodel | jeapaul | wav2vec2 | 13 | 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 | 1,806 | 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. -->
# languagemodel
This model is a fine-tuned version of [monideep2255/XLRS-torgo](https://huggingface.co/monideep2255/XLRS-torgo) on... | 04bbbdb7edbd59c5b2c31d25803acb7f |
Helsinki-NLP/opus-mt-de-efi | Helsinki-NLP | marian | 10 | 9 | 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-de-efi
* source languages: de
* target languages: efi
* OPUS readme: [de-efi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-efi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](... | 78c1aa5620eb159180800cab78b7e81e |
Cwhgn/DAMO-YOLO-T | Cwhgn | null | 5 | 0 | null | 1 | null | false | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 4,105 | false |
## Model Description
This **DAMO-YOLO-T** model is a tiny-size object detection model with fast inference speed and high accuracy, trained by **DAMO-YOLO**.
DAMO-YOLO is a fast and accurate object detection method, which is developed by TinyML Team from Alibaba DAMO Data Analytics and Intelligence Lab. And it achieve... | 2b6545482d3b485a60db785e800a5f36 |
espnet/realzza-meld-asr-hubert-transformer | espnet | null | 21 | 0 | espnet | 0 | automatic-speech-recognition | false | false | false | cc-by-4.0 | ['en'] | ['meld'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'automatic-speech-recognition', 'spoken-language-understanding'] | false | true | true | 1,636 | false | # ESPnet2: Meld Recipe
## Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/meld/asr1/
./run.sh
```
## Environments
- date: `Thu Nov 10 09:07:40 EST 2022`
- python version: `3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]`
- espnet version: `espnet 202207`
- pytorch version: `pytorch 1.8.1+c... | 7519deaf7b47d3610deb6a523d6f610e |
gorkemgoknar/gpt2-small-turkish | gorkemgoknar | gpt2 | 9 | 160 | transformers | 4 | text-generation | true | false | true | apache-2.0 | ['tr'] | ['wikipedia-turkish'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['gpt2', 'turkish'] | false | true | true | 3,479 | false |
# Turkish GPT2 Model Finetuned
# Türkçe GPT2 Modeli
## Model description
This is a GPT2-Small English based model finetuned and additionaly trainied with Wikipedia Articles in Turkish as of 28-10-2020
Live demo based on this work at : https://www.metayazar.com/
Fine tuned writer on this model: https://huggingface... | 45f1507b46de4efe36497523568a73a3 |
davanstrien/distilbert-base-cased_fine_tuned_food_ner | davanstrien | distilbert | 12 | 12 | 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 | 5,875 | 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-cased_fine_tuned_food_ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/d... | dfc415a57928faeff60711e7d211362a |
yip-i/wav2vec2-demo-F04-2 | yip-i | wav2vec2 | 10 | 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 | 3,203 | 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-demo-F04-2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2... | 9611873b854e5b846fc5f901066a2684 |
rajistics/informal_formal_style_transfer | rajistics | t5 | 10 | 4 | transformers | 2 | text2text-generation | true | false | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,495 | false |
## Source
A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual. The original model is at [https://github.com/PrithivirajDamodaran/Styleformer](https://github.com/PrithivirajDamodaran/Styleformer).
##... | 3e92178b50846e4c0e85b6bddc271780 |
imvladikon/wav2vec2-xls-r-300m-lm-hebrew | imvladikon | wav2vec2 | 16 | 12 | transformers | 1 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 1 | 1 | 0 | ['generated_from_trainer', 'he', 'robust-speech-event'] | true | true | true | 1,048 | false |
# wav2vec2-xls-r-300m-lm-hebrew
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset
with adding ngram models according to [Boosting Wav2Vec2 with n-grams in 🤗 Transformers](https://huggingface.co/blog/wav2vec2-with-ngram)
## ... | dd0b6d26ec6bd6985c2566c9b1b831b5 |
TencentMedicalNet/MedicalNet-Resnet10 | TencentMedicalNet | null | 5 | 0 | null | 2 | null | false | false | false | mit | ['en'] | ['MRBrainS18'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['MedicalNet', 'medical images', 'medical', '3D', 'Med3D'] | false | true | true | 1,531 | false | # MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset... | 3b78cd30983091b59fd000537cc9ab87 |
danieleV9H/hubert-base-libri-clean-ft100h | danieleV9H | hubert | 12 | 4 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['librispeech_asr'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 3,400 | 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. -->
# hubert-base-libri-clean-ft100h
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/faceboo... | 8324194e16045b7cc5cddb2ba388c513 |
DrishtiSharma/whisper-large-v2-hungarian-400-steps | DrishtiSharma | whisper | 15 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['hu'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,312 | 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 Large Nepali - Drishti Sharma
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai... | 13caa302125caf15f8975418c5c656a6 |
paola-md/recipe-lr1e05-wd0.01-bs16 | paola-md | roberta | 6 | 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 | 1,467 | 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. -->
# recipe-lr1e05-wd0.01-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-... | 658cadc2476f5c2ef3581b45b0ea7834 |
sentence-transformers/bert-base-nli-max-tokens | sentence-transformers | bert | 15 | 310 | sentence-transformers | 0 | sentence-similarity | true | true | true | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | true | true | 3,816 | false |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-nli-max-tokens
This is a [sentence-tran... | 01424900dc45c408817091f060f291da |
kha-white/manga-ocr-base | kha-white | vision-encoder-decoder | 8 | 35,462 | transformers | 18 | image-to-text | true | false | false | apache-2.0 | ['ja'] | ['manga109s'] | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['image-to-text'] | false | true | true | 620 | false |
# Manga OCR
Optical character recognition for Japanese text, with the main focus being Japanese manga.
It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) framework.
Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to prov... | 01ad2a2f436ea34209d9527bd1aa6468 |
xliu128/xlm-roberta-base-finetuned-panx-de | xliu128 | xlm-roberta | 12 | 6 | 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
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... | e8ba46ccc2397d2774a76da7f86d30d6 |
wdcqc/starcraft-platform-terrain-32x32 | wdcqc | null | 17 | 21 | diffusers | 8 | other | true | false | false | creativeml-openrail-m | null | ['wdcqc/starcraft-remastered-melee-maps'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | true | true | 3,157 | false |
# DreamBooth model for Starcraft:Remastered terrain
This is a Stable Diffusion model fine-tuned on Starcraft terrain images on the Space Platform tileset with DreamBooth. It can be used by adding the `instance_prompt`: **isometric scspace terrain**
It was trained on 32x32 terrain images from 265 melee maps including... | 14350a45f4811851417304f551104815 |
jperezv/bert-finetuned-ner | jperezv | bert | 12 | 3 | 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 |
<!-- 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-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | 113e53e09798f02595c30622bb91e235 |
yanaiela/roberta-base-epoch_69 | yanaiela | roberta | 9 | 2 | transformers | 0 | fill-mask | true | false | false | mit | ['en'] | ['wikipedia', 'bookcorpus'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['roberta-base', 'roberta-base-epoch_69'] | false | true | true | 2,102 | false |
# RoBERTa, Intermediate Checkpoint - Epoch 69
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randoml... | 4a0fe2a00a3cc71cbc560697dc698607 |
projecte-aina/mt-aina-en-ca | projecte-aina | null | 5 | 0 | null | 0 | null | false | false | false | cc-by-4.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 8,803 | false | ## Aina Project's English-Catalan machine translation model
## Table of Contents
- [Model Description](#model-description)
- [Intended Uses and Limitations](#intended-use)
- [How to Use](#how-to-use)
- [Training](#training)
- [Training data](#training-data)
- [Training procedure](#training-procedure)
- [Data ... | c00058da64d3154b5ae406178924eaca |
Helsinki-NLP/opus-mt-sv-mos | Helsinki-NLP | marian | 10 | 9 | 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-sv-mos
* source languages: sv
* target languages: mos
* OPUS readme: [sv-mos](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-mos/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](... | 43b2b1761968671d22f82fab09dd2ed5 |
joey234/whisper-small-vi | joey234 | whisper | 55 | 5 | transformers | 1 | automatic-speech-recognition | true | false | false | apache-2.0 | ['vi'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,552 | 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 Vietnamese
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-smal... | 48f84e65873df9efa4ae9927b20eb30e |
qanastek/whisper-large-french-uncased | qanastek | whisper | 17 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['fr'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | true | true | true | 1,310 | 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 Large French
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) o... | f14b5761e15606a957ae0332eb91336e |
alexjercan/codet5-base-buggy-error-description | alexjercan | t5 | 11 | 5 | transformers | 1 | text2text-generation | true | false | false | apache-2.0 | null | null | 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
should probably proofread and complete it, then remove this comment. -->
# codet5-base-buggy-error-description
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesf... | aa9c5535d99bba236370cceda837f19e |
lct-rug-2022/edos-2023-baseline-distilbert-base-uncased-label_sexist | lct-rug-2022 | distilbert | 10 | 4 | 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 | 1,544 | 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. -->
# edos-2023-baseline-distilbert-base-uncased-label_sexist
This model is a fine-tuned version of [distilbert-base-uncased](https://... | 060ab1672e994f920d3af913bfb5a3d5 |
JovialValley/model_syllable_onSet1 | JovialValley | wav2vec2 | 13 | 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 | 11,452 | 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. -->
# model_syllable_onSet1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wa... | 74b22347f41a4e124dd113a44611d4fe |
arampacha/whisper-large-uk | arampacha | whisper | 13 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['uk'] | ['mozilla-foundation/common_voice_11_0', 'google/fleurs'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 976 | false |
# whisper-base-uk
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3201
- eval_wer: 10.2869
## Model description
More information needed
## Inten... | ab69e623d71ab9b9903e7079b2244bdc |
jonatasgrosman/exp_w2v2r_en_xls-r_accent_us-5_england-5_s334 | jonatasgrosman | wav2vec2 | 10 | 3 | 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 | 475 | false | # exp_w2v2r_en_xls-r_accent_us-5_england-5_s334
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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure t... | e50e85324282c1ee7dc346d1d93bae19 |
TheNateTCY/fulltrain_optmodel | TheNateTCY | opt | 8 | 0 | transformers | 0 | text-generation | false | true | false | other | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,521 | 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. -->
# TheNateTCY/fulltrain_optmodel
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on a... | 3be524e9197c1900fc86c15cc8c37ee3 |
ieborhan/irisg444_4c0-Species-classification | ieborhan | null | 4 | 0 | sklearn | 0 | tabular-classification | false | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['tabular-classification', 'baseline-trainer'] | false | true | true | 7,540 | false |
## Baseline Model trained on irisg444_4c0 to apply classification on Species
**Metrics of the best model:**
accuracy 0.953333
recall_macro 0.953333
precision_macro 0.956229
f1_macro 0.953216
Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64
**See mod... | ec8f759a2cbcd3838ac5a9ae0eee5a5b |
Helsinki-NLP/opus-mt-fr-bi | 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 | 768 | false |
### opus-mt-fr-bi
* source languages: fr
* target languages: bi
* OPUS readme: [fr-bi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-bi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](http... | 5649db29ae0810704144daf9ba068e0f |
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-4 | 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 | 2,215 | 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__hate_speech_offensive__train-32-4
This model is a fine-tuned version of [distilbert-base-uncased](https... | 5f378270188a2cd951033abe2aa32a85 |
jonatasgrosman/exp_w2v2t_es_no-pretraining_s807 | jonatasgrosman | wav2vec2 | 10 | 4 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['es'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'es'] | false | true | true | 414 | false | # exp_w2v2t_es_no-pretraining_s807
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has be... | 1608e6c97878c5322a3d7e3d3d806c08 |
MEDT/ChatBot | MEDT | gpt2 | 9 | 4 | transformers | 0 | conversational | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['conversational'] | false | true | true | 1,752 | false |
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script... | a2d65dd0fa0e00364c69ac839da931ff |
k3lana/xlm-roberta-base-finetuned-panx-de-fr | k3lana | xlm-roberta | 10 | 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 | 1,321 | 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-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | cf47ea12a762581cc79bd9c003e3e485 |
csam/finetuning-sentiment-model-3000-samples | csam | distilbert | 13 | 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... | e5b4c5ee4a3ad64138b404e64d7135cb |
gchhablani/bert-base-cased-finetuned-stsb | gchhablani | bert | 52 | 88 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | true | true | true | 2,394 | 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-cased-finetuned-stsb
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | 82422fc3000b327511490fcfb35bf262 |
Helsinki-NLP/opus-mt-tc-big-es-zle | Helsinki-NLP | marian | 13 | 5 | transformers | 0 | translation | true | true | false | cc-by-4.0 | ['be', 'es', 'ru', 'uk', 'zle'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['translation', 'opus-mt-tc'] | true | true | true | 5,963 | false | # opus-mt-tc-big-es-zle
Neural machine translation model for translating from Spanish (es) to East Slavic languages (zle).
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... | 26ea00e38fd0841a9c2ea4611b0ed9b6 |
gostrive/distilbert-base-uncased-finetuned-squad-d5716d28 | gostrive | distilbert | 8 | 5 | transformers | 0 | question-answering | true | false | false | apache-2.0 | ['en'] | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['question-answering'] | false | true | true | 1,392 | false |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | 206913b81dd6917c52eb8c6176e2b1eb |
Evelyn18/distilbert-base-uncased-becasv3-1 | Evelyn18 | distilbert | 19 | 5 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['becasv3'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,530 | 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-becasv3-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | 8c273b223c303663038329160bf83339 |
Sandeepanie/clinical-finetuned-AgitationModel | Sandeepanie | bert | 18 | 1 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,584 | 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. -->
# clinical-finetuned-AgitationModel
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | c42143ba868f11ba4d7dc20e46e7983d |
kSaluja/new-test-model2 | kSaluja | bert | 14 | 5 | 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 | 2,163 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# new-test-model2
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unkn... | f12660b975911f1b3a692931bcbadc8d |
Helsinki-NLP/opus-mt-hy-en | Helsinki-NLP | marian | 10 | 192 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 770 | false |
### opus-mt-hy-en
* source languages: hy
* target languages: en
* OPUS readme: [hy-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hy-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](http... | 85ae1b911f80c333af96c75f2d35f3bd |
popcornell/chime7_task1_asr1_baseline | popcornell | null | 23 | 7 | espnet | 0 | automatic-speech-recognition | false | false | false | cc-by-4.0 | ['en'] | ['chime7_task1'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | true | true | 10,211 | false |
## ESPnet2 ASR model
### `popcornell/chime7_task1_asr1_baseline`
This model was trained by popcornell using chime7_task1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [CHiME-7 DASR installation instructions](https://github.com/espnet/espnet/blob/master/egs2/chim... | 445aeb1f46c12b854264b9da438a80c1 |
minjibi/test1000v2 | minjibi | wav2vec2 | 12 | 3 | 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 | 1,638 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# test1000v2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-larg... | 367a1b02d526e0d712b87447f337eb8c |
ibm/ColD-Fusion-itr21-seed2 | ibm | roberta | 9 | 3 | transformers | 0 | text-classification | true | false | false | mit | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['exbert'] | false | true | true | 3,148 | false |
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning t... | 5c8fa2b9a466ea10f283dd893ce2d1a5 |
Kurapka/koja | Kurapka | null | 18 | 4 | 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 | 606 | false | ### koja Dreambooth model trained by Kurapka 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/TheLastBen/fast-stable-diffu... | 469cbb48bbcbaa34569f445e688eabe1 |
jonatasgrosman/exp_w2v2t_sv-se_r-wav2vec2_s418 | jonatasgrosman | wav2vec2 | 10 | 7 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['sv-SE'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'sv-SE'] | false | true | true | 468 | false | # exp_w2v2t_sv-se_r-wav2vec2_s418
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that you... | 68a2706abb7fec9a7d722f709415d789 |
MortalSage/Strange_Dedication | MortalSage | null | 38 | 0 | null | 16 | text-to-image | false | false | false | unknown | ['en'] | null | null | 1 | 0 | 0 | 1 | 1 | 1 | 0 | ['stable-diffusion', 'text-to-image'] | false | true | true | 1,952 | false |
.safetensor model for automatic1111 webui.
Strange_Dedication_v3 is an improvement to Strange_Dedication_v2 using Anything_v4.5.
It's better at the cutesexyrobutts style, without having to use a trigger.
Also, it's good at shiny_skin and shiny_clothes and artistical backgrounds.
I have only used it with "vae-ft-ms... | 2ac69240c9ec8a6839e66c10c790cb88 |
Sa1i/gakki-mix-512-young | Sa1i | null | 22 | 2 | diffusers | 1 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-to-image', 'stable-diffusion', 'gakki'] | false | true | true | 529 | false | # VAE
Highly recommended for use with VAE
# legal & risk
⚠️⚠ It is prohibited to use this model for commercial purposes and any scenarios of illegal acts and purposes.
Sample pictures of this concept:
 on an unkn... | 7d22385a960de6372ace2a5ceff99557 |
jcblaise/electra-tagalog-small-uncased-generator | jcblaise | electra | 6 | 4 | transformers | 0 | fill-mask | true | false | false | gpl-3.0 | ['tl'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['electra', 'tagalog', 'filipino'] | false | true | true | 1,393 | false |
# ELECTRA Tagalog Small Uncased Generator
Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This is the generator model used to sample synthetic text and pr... | 8b6ffc4dd3c28bb5c24f4a941aa87675 |
arvkevi/nba_pbp_distilgpt2 | arvkevi | gpt2 | 21 | 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 | 1,251 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# nba_pbp_distilgpt2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on text files containin... | 8350dd6b5f7786145e6b0bef1e2ad520 |
muhtasham/small-mlm-glue-qqp-custom-tokenizer | muhtasham | bert | 12 | 0 | transformers | 0 | fill-mask | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,457 | 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. -->
# small-mlm-glue-qqp-custom-tokenizer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingfac... | aedf9d49499f69fb9e8b14113d294bb2 |
jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-8_sixties-2_s945 | jonatasgrosman | wav2vec2 | 10 | 0 | 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 | 475 | false | # exp_w2v2r_de_xls-r_age_teens-8_sixties-2_s945
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 sure t... | eeff3a02f55872ba95b04bc82f8f8efd |
2020uee0139/distilbert-base-uncased-finetuned-squad | 2020uee0139 | distilbert | 12 | 3 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,284 | 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | bdd84eba2f64d8fa267d694ea40d857a |
Intel/bert-base-uncased-mrpc-int8-dynamic | Intel | bert | 9 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['mrpc'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | true | true | 1,445 | false |
# INT8 BERT base uncased finetuned MRPC
## Post-training dynamic quantization
### PyTorch
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The origina... | 439aac8e766a0d7796c3738f812e06b4 |
prakharz/DIAL-FLANT5-XL | prakharz | t5 | 8 | 729 | transformers | 3 | text2text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,332 | false |
# InstructDial
Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialo... | 8551ede43863e78a31514b0a652dc412 |
huggingnft/alpacadabraz | huggingnft | null | 5 | 10 | transformers | 1 | unconditional-image-generation | false | false | false | mit | null | ['huggingnft/alpacadabraz'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | true | true | 2,190 | false |
# Hugging NFT: alpacadabraz
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/alpacadabraz)... | 8099a5c6818b8263c173d2cc7ee8d440 |
megantosh/flair-arabic-MSA-aqmar | megantosh | null | 11 | 44 | flair | 0 | token-classification | true | false | false | apache-2.0 | ['ar'] | ['AQMAR', 'ANERcorp'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | true | true | 3,938 | false | # Arabic NER Model for AQMAR dataset
Training was conducted over 86 epochs, using a linear decaying learning rate of 2e-05, starting from 0.3 and a batch size of 48 with fastText and Flair forward and backward embeddings.
## Original Dataset:
- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/)
## Results:
- F1-score (m... | 11e4388388b4286db8293fe9dc815596 |
roscazo/CTEBMSP_ANAT_DISO | roscazo | roberta | 17 | 1 | 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 | 3,589 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# CTEBMSP_ANAT_DISO
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-... | 98835688bbc75a121215ab68e234fcfc |
dxiao/bert-finetuned-ner-20percent | dxiao | bert | 12 | 7 | 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 | 1,525 | 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-ner-20percent
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on ... | 140bd2b8399a76158210abbebd816fef |
Simon17/Klassifizierung-Heizung | Simon17 | bert | 12 | 1 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,318 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Klassifizierung-Heizung
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-c... | 92a43128590c7933cb7f3d2552f8f4ec |
sd-concepts-library/james-web-space-telescope | sd-concepts-library | null | 9 | 0 | null | 0 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,150 | false | ### James Web space Telescope on Stable Diffusion
This is the `<James-Web-Telescope>` 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.ip... | 1fa34a6ac260d9bca1ad288d3ec7d4a6 |
tau/bart-base-sled-contractnli | tau | tau/sled | 5 | 0 | transformers | 0 | null | true | false | false | mit | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 4,972 | false |
# BART-SLED (SLiding-Encoder and Decoder, base-sized model)
SLED models use pretrained, short-range encoder-decoder models, and apply them over
long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder
## Model description
This SLED model i... | 652ac6c93ae67a41b4f8d885f27845b1 |
thyagosme/gpt2-wikitext2 | thyagosme | gpt2 | 9 | 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 | 1,216 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | db207bf12cddb2e1fba07948e78679ce |
omriuz/distilbert-base-uncased-finetuned-mnli | omriuz | distilbert | 14 | 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 | 1,291 | 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-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | eb2b9fcf50cead783754391fa6a139fb |
jonatasgrosman/exp_w2v2t_nl_unispeech_s493 | jonatasgrosman | unispeech | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['nl'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'nl'] | false | true | true | 469 | false | # exp_w2v2t_nl_unispeech_s493
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that yo... | 835abbb190bd60eeb21e6199a2587a74 |
Vishfeb27/wav2vec2-base-timit-demo-colab | Vishfeb27 | wav2vec2 | 14 | 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 | 1,014 | 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... | 7812217c55e44d62bc2b6221acd290ad |
96harsh56/bert-large-cased-berta-finetuned-subjqa_1 | 96harsh56 | bert | 12 | 2 | 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 | 939 | 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-large-cased-berta-finetuned-subjqa_1
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-l... | 3ee5acb3b2366a86a716270a5f0d353e |
lmqg/mt5-base-itquad-ae | lmqg | mt5 | 13 | 66 | transformers | 0 | text2text-generation | true | false | false | cc-by-4.0 | ['it'] | ['lmqg/qg_itquad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['answer extraction'] | true | true | true | 4,612 | false |
# Model Card of `lmqg/mt5-base-itquad-ae`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).... | ce2b2e616da685f4f5bc6498b07e925d |
Raccourci/t5-sentiment | Raccourci | t5 | 11 | 1 | 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 | 1,807 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# t5-sentiment-hub
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achie... | 3b6f02c97009fbf9ab363eed36da4aed |
flax-community/alberti-bert-base-multilingual-cased | flax-community | bert | 47 | 96 | transformers | 4 | fill-mask | true | false | true | cc-by-4.0 | ['es'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['multilingual', 'bert'] | false | true | true | 4,318 | false |
# ALBERTI
ALBERTI is a set of two BERT-based multilingual model for poetry. One for verses and another one for stanzas. This model has been further trained with the PULPO corpus for verses using [Flax](https://github.com/google/flax), including training scripts.
This is part of the
[Flax/Jax Community Week](https://... | ea460dc4946fd092a8847a10d71f798a |
Rgl73/xlm-roberta-base-finetuned-panx-de | Rgl73 | xlm-roberta | 26 | 11 | 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
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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... | 76067d9f672e706ab5a9b7a4af0d61ce |
willcai/wav2vec2_common_voice_accents_indian_only_rerun | willcai | wav2vec2 | 11 | 4 | 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 | 2,504 | 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_common_voice_accents_indian_only_rerun
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hug... | d42e48f0246fc894f956177536df3fa4 |
KarelDO/bert-base-uncased.CEBaB_confounding.food_service_positive.sa.5-class.seed_44 | KarelDO | bert | 14 | 2 | transformers | 0 | null | true | false | false | apache-2.0 | ['en'] | ['OpenTable'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,131 | 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.CEBaB_confounding.food_service_positive.sa.5-class.seed_44
This model is a fine-tuned version of [bert-base-un... | 7cafd07dc3b5011af25ccac708b96d7f |
tahazakir/wav2vec2-base-timit-demo-colab0 | tahazakir | wav2vec2 | 12 | 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 | 1,342 | 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-timit-demo-colab0
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | 53254c2d38088ab00e6ed79748fd605b |
Geotrend/bert-base-pt-cased | Geotrend | bert | 8 | 39 | transformers | 0 | fill-mask | true | true | true | apache-2.0 | ['pt'] | ['wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,283 | false |
# bert-base-pt-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 the s... | 54f05ce2de162a9d4d61144222fbe932 |
BrianT/distilbert-base-uncased-finetuned-cola | BrianT | distilbert | 13 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 1 | 1 | 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... | aca2c0303da4cf2f214551eeecec8f45 |
Mascariddu8/distilbert-base-uncased-finetuned-imdb | Mascariddu8 | distilbert | 9 | 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 | 1,318 | 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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | b3aa13b2f8da3c0f2b3d34b10d34cd60 |
tuwonga/marblesh | tuwonga | null | 5 | 0 | null | 20 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 6 | 5 | 1 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'text-to-image'] | false | true | true | 1,692 | false | ### marblesh
This is a fine-tuned Stable Diffusion model (based on v1.5) trained on screenshots from marble statues. This model is a merge from 2 checkpoints trained on different marble statues. Use the token "**marblesh**" in your prompt for person and animals. If you have veichles or other object in your prompt use t... | d474d47365c22203b8db9f6e421bc723 |
osanseviero/test123 | osanseviero | null | 2 | 0 | spacy | 0 | token-classification | false | false | false | cc-by-sa-4.0 | ['de'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['spacy', 'token-classification'] | false | true | true | 598,363 | false | UD v2.5 benchmarking pipeline for UD_German-HDT
| Feature | Description |
| --- | --- |
| **Name** | `de_udv25_germanhdt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_t... | d0c98d305581ae211f1adb30ae12cb24 |
frgfm/resnet18 | frgfm | null | 5 | 6 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['frgfm/imagenette'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['image-classification', 'pytorch', 'onnx'] | false | true | true | 2,771 | false |
# ResNet-18 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ResNet architecture was introduced in [this paper](https://arxiv.org/pdf/1512.03385.pdf).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection... | 7e4410f0dc2025ea66303aa8771819d5 |
Andranik/blinding1 | Andranik | bert | 13 | 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 | 1,376 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# blinding
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT... | 1f25cd0d415930a9d50353e050f3623a |
matthh/gpt2-poetry-model | matthh | gpt2 | 11 | 3 | transformers | 0 | text-generation | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 864 | 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. -->
# gpt2-poetry-model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model des... | d7f19035e03e97b8ab4f657e72e40a7b |
rycont/emoji-diffusion | rycont | null | 7 | 0 | diffusers | 0 | null | false | false | false | apache-2.0 | ['en'] | ['microsoft/fluentui-emoji'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,206 | false |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# emoji-diffusion
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hugging... | 7434bb6f78f35e77c4e48f035615162b |
MBMMurad/wav2vec2_murad_with_some_new_data | MBMMurad | wav2vec2 | 17 | 1 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['cvbn'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,221 | 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_murad_with_some_new_data
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | eeca045623bd9d5bee14e118e5634262 |
jiobiala24/wav2vec2-base-checkpoint-12 | jiobiala24 | 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 | 2,362 | 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-checkpoint-12
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-11.1](https://huggingface... | 6c56fb12474a265924d879f8f2b7f773 |
jvkape/WikiHowSDModel | jvkape | null | 6 | 0 | null | 8 | null | false | false | false | openrail | null | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,523 | false | This model card is a copy-paste from https://www.reddit.com/r/StableDiffusion/comments/ybavif/wikihow_db_model_entirely_free_model_trained_with/
The template is not 100% accurate and sometimes creates erroneous images, but it is incomparable to the natural quality of SD.
The images used for training were all CC from ... | e5bbfa0346938d29f138804b6a0f0ab1 |
jonatasgrosman/exp_w2v2r_es_xls-r_gender_male-2_female-8_s772 | jonatasgrosman | wav2vec2 | 10 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['es'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'es'] | false | true | true | 476 | false | # exp_w2v2r_es_xls-r_gender_male-2_female-8_s772
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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | f1fbfe95cd65f1bb60b50d6ceea758f3 |
Tune-A-Video-library/redshift-man-skiing | Tune-A-Video-library | null | 17 | 0 | diffusers | 2 | null | false | false | false | creativeml-openrail-m | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['tune-a-video', 'text-to-video', 'diffusers'] | false | true | true | 1,575 | false |
# Tune-A-Video - Redshift
## Model Description
- Base model: [nitrosocke/redshift-diffusion](https://huggingface.co/nitrosocke/redshift-diffusion)
- Training prompt: a man is skiing.
## Samples
Test prompt: (redshift style) [spider man/black w... | 21eb22879334f07d09f7a8e87916ef5f |
infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8 | infinitejoy | wav2vec2 | 13 | 7 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['br'] | ['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', 'br', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | true | true | true | 2,271 | 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. -->
# XLS-R-300M - Breton
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec... | e213b5597dbcc2dd6e875fce53a06e0f |
utsavnandi/fashion-mnist-ddpm-32px-5000_steps | utsavnandi | null | 3 | 0 | null | 0 | unconditional-image-generation | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['unconditional-image-generation'] | false | true | true | 487 | false | Fashion MNIST unconditional Unet Model trained using DDPM
Model Hyperparams:
- Model size: 51,834,625 params
- 3 stages: 128, 256, 512 channels
- Linear Attention in 2nd and 3rd stages, Self Attention in Middle Stage
- Optimizer: Adam
- LR: 3e-4
- Batch Size: 64
- Grad Accumulation: 8 steps
- Effectibe Batch Size: 51... | fe49b47cd35280ba30fc8f3f9a78511f |
fathyshalab/all-roberta-large-v1-banking-1000-16-5-oos | fathyshalab | roberta | 11 | 4 | 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 | 1,519 | 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-banking-1000-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](ht... | 5cea3565c5a9ccc67ced3ed0dd1b6f13 |
a1noack/bart-large-gigaword | a1noack | bart | 6 | 115 | transformers | 0 | summarization | true | false | false | mit | null | ['gigaword'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization'] | false | true | true | 1,234 | false | # BART for Gigaword
- This model was created by fine-tuning the `facebook/bart-large-cnn` weights (also on HuggingFace) for the Gigaword dataset. The model was fine-tuned on the Gigaword training set for 3 epochs, and the model with the highest ROUGE-1 score on the training set batches was kept.
- The BART Tokenizer ... | 7bc82302bc5f9e9bd8ccc20d98f05e11 |
sd-concepts-library/liminalspaces | sd-concepts-library | null | 11 | 0 | null | 3 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,286 | false | ### Liminalspaces on Stable Diffusion
This is the `<liminal image>` 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... | 4b47449245e502dbc42065b3b16d5ce5 |
jonatasgrosman/exp_w2v2t_pt_unispeech-ml_s610 | jonatasgrosman | unispeech | 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 | 500 | false | # exp_w2v2t_pt_unispeech-ml_s610
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When usin... | cc854b4b4b2721b5c73842a575abad18 |
olgaduchovny/t5-base-ner-mit-movie | olgaduchovny | t5 | 8 | 1 | transformers | 0 | text2text-generation | true | false | false | mit | ['en'] | ['conll2003'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['pytorch', 'ner', 'text generation', 'seq2seq'] | false | true | true | 1,174 | false | # t5-base-qa-ner-conll
Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
t5-base model tuned on conll2003 dataset.
https://github.com/ovbystrova/InstructionNER
## Inference
```shell
git clone https://github.com/ovbystrova/InstructionNER
cd InstructionNER
```
```python
from in... | e076b3955885d24a9b530b32e46cfec8 |
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