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facebook/wmt21-dense-24-wide-en-x | facebook | m2m_100 | 9 | 736 | transformers | 17 | translation | true | false | false | mit | ['multilingual', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de', 'en'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['translation', 'wmt21'] | false | true | true | 2,414 | false |
# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and first released in [this](https://github.com/pytorch/fairseq/tree/main/examples/wmt21) repository.
The model can ... | e6801e52b08ba47b4a091ac3bd5b73ed |
IMSyPP/hate_speech_it | IMSyPP | bert | 6 | 83 | transformers | 0 | text-classification | true | false | false | mit | ['it'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 654 | false |
# Hate Speech Classifier for Social Media Content in Italian Language
A monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-t... | 2d246f7625b8a4e4c498e31cb2d0e93c |
kadirnar/AnimeSR_Paper_Model | kadirnar | null | 3 | 0 | null | 0 | object-detection | false | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['object-detection', 'computer-vision', 'gan', 'animegan'] | false | true | true | 621 | false |
### Model Description
[AnimeSR](https://arxiv.org/abs/2206.07038): Learning Real-World Super-Resolution Models for Animation Videos
### Installation
```
pip install animesr
```
### Anime GAN
```python
from animesr.inference_animesr_video import main
main(source='test.mp4', 'kadirnar/AnimeSR_Paper_Model')
```
### ... | 95d5089787beeaa0cd245e516b052863 |
chuchun9/distilbert-base-uncased-finetuned-squad | chuchun9 | distilbert | 25 | 6 | 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 | 1,379 | 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... | 959278c3725aed02c22b53080c87593a |
shivi/sd-album-covers | shivi | null | 57 | 20 | diffusers | 2 | 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 | 7,072 | false | ### sd-album-covers Dreambooth model trained by shivi 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... | 0518e74ccd4f7fedf2899045b2a4c9f0 |
espnet/wanchichen_fleurs_asr_conformer_scctc | espnet | null | 29 | 0 | espnet | 0 | null | false | false | false | cc-by-4.0 | ['en'] | ['google/fleurs'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['espnet', 'audio', 'speech-recognition'] | false | true | true | 1,404 | false |
## ESPnet2 ASR model
### `espnet/wanchichen_fleurs_asr_conformer_sctctc`
This model was trained by William Chen using the fleurs recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/fleurs/asr1
./run.sh
```
<!-- Generated by scripts/utils/s... | 20def58ff860df51c9ffa070273edc4f |
JorisCos/DCCRNet_Libri1Mix_enhsingle_16k | JorisCos | null | 3 | 5,590 | asteroid | 5 | audio-to-audio | true | false | false | cc-by-sa-4.0 | null | ['Libri1Mix', 'enh_single'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['asteroid', 'audio', 'DCCRNet', 'audio-to-audio', 'speech-enhancement'] | false | true | true | 1,598 | false |
## Asteroid model `JorisCos/DCCRNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
n_src: 1
sampl... | fa8a452225fe528568f044161cc0c6ab |
flax-community/gpt-neo-1.3B-apps | flax-community | gpt_neo | 12 | 5 | transformers | 3 | text-generation | true | false | true | mit | ['en', 'python'] | ['apps'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['gpt_neo', 'code_synthesis'] | false | true | true | 5,174 | false |
# GPT-Neo-1.3B-APPS
> **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot**
## Model Description
GPT-Neo-1.3B-APPS is a GPT-Neo-125M finetuned on APPS dataset. This model is specialized ... | 251281f4f3f24aab72ae032fd8510187 |
Sushant45/Canadian_Armed_Forces-clustered | Sushant45 | distilbert | 8 | 29 | 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 | 1,871 | 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. -->
# Sushant45/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/... | 68759fc95135ad275b6300da3db62019 |
cankeles/DPTNet_WHAMR_enhsingle_16k | cankeles | null | 3 | 13 | asteroid | 1 | audio-to-audio | true | false | false | cc-by-sa-4.0 | null | ['Libri1Mix', 'enh_single'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['asteroid', 'audio', 'DPTNet', 'audio-to-audio'] | false | true | true | 1,325 | false | ## Asteroid model `cankeles/DPTNet_WHAMR_enhsignle_16k`
Description:
This model was trained by M. Can Keleş using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
mode: min
nondefault_ns... | 0a8718a840c9477ccf30de8a56e1c8a4 |
wietsedv/xlm-roberta-base-ft-udpos28-hyw | wietsedv | xlm-roberta | 8 | 8 | transformers | 0 | token-classification | true | false | false | apache-2.0 | ['hyw'] | ['universal_dependencies'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['part-of-speech', 'token-classification'] | true | true | true | 578 | false |
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Western Armenian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from ... | 92ebb7758c347d46c212592b59145783 |
emmyapi/distilbart-podimo-data-eval-3 | emmyapi | bart | 13 | 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 | 2,206 | 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. -->
# distilbart-podimo-data-eval-3
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshle... | 72b1cca90d88c8c60dc2c213298ad133 |
rajistics/donut-base-sroiev2 | rajistics | 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 | 983 | 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. -->
# donut-base-sroiev2
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut... | 53163a091a88e6264f3e9ac001b62b46 |
jarvisx17/wav2vec2-base-timit-small | jarvisx17 | 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 | 2,986 | 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-small
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | 0dc34a9ff135d598509d460aa446ffb7 |
johko/capdec_025 | johko | null | 3 | 0 | null | 0 | image-to-text | false | false | false | apache-2.0 | ['en'] | ['MS-COCO', 'Flickr30k'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['Image Captioning'] | false | true | true | 1,348 | false |
# CapDec - NoiseLevel: 0.025
## Model Description
These are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf).
Their method aims to train CLIP with only text samples. Therefore they are injecting ze... | 98c3c1dca52a704b8370370e0a94108d |
JovialValley/model_broadclass_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 | 11,482 | 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_broadclass_onSet1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/... | a358b3250a42c7dc150a15a2437218b8 |
mariolinml/roberta_large-ner-conll2003_0818_v1 | mariolinml | roberta | 14 | 5 | transformers | 0 | token-classification | true | false | false | mit | null | ['conll2003'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,441 | 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. -->
# roberta_large-ner-conll2003_0818_v1
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) ... | ea60ea814e16a53d41e7393b7a8bc1b0 |
aXhyra/demo_hate_1234567 | aXhyra | distilbert | 10 | 10 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['tweet_eval'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,389 | 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. -->
# demo_hate_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased... | ac1685c5a7422439ced714d264808437 |
cataluna84/xlm-roberta-base-finetuned-panx-de-fr | cataluna84 | xlm-roberta | 10 | 11 | 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... | 924504ad4da58d47ae22bb170de9f040 |
HuggingAlex1247/gelectra-large-germaner | HuggingAlex1247 | electra | 18 | 9 | transformers | 0 | token-classification | false | true | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,365 | 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. -->
# HuggingAlex1247/gelectra-large-germaner
This model is a fine-tuned version of [deepset/gelectra-large](https://huggingface.co/deepset/... | 1cbba9c71c758dec92e9f2d570c5d01d |
jonatasgrosman/exp_w2v2t_it_vp-it_s411 | jonatasgrosman | wav2vec2 | 10 | 7 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['it'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'it'] | false | true | true | 469 | false | # exp_w2v2t_it_vp-it_s411
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that yo... | 5f20bb60c4d8b6601c8fccee3708df93 |
sd-concepts-library/a-female-hero-from-the-legend-of-mir | sd-concepts-library | null | 11 | 0 | null | 4 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,612 | false | ### a female hero from The Legend of Mir on Stable Diffusion
This is the `a <female-hero> from The Legend of Mir` 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... | a8c32f1a9f0ed8b24e11419cd313cb94 |
jonatasgrosman/wav2vec2-xls-r-1b-italian | jonatasgrosman | wav2vec2 | 25 | 9 | transformers | 1 | automatic-speech-recognition | true | false | false | apache-2.0 | ['it'] | ['mozilla-foundation/common_voice_8_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'it', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | true | true | true | 3,056 | false |
# Fine-tuned XLS-R 1B model for speech recognition in Italian
Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Italian using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual TEDx](http... | ae960af97aa23a706bd5680f71c55ff0 |
Helsinki-NLP/opus-mt-fr-bzs | Helsinki-NLP | marian | 10 | 7 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 776 | false |
### opus-mt-fr-bzs
* source languages: fr
* target languages: bzs
* OPUS readme: [fr-bzs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-bzs/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](... | 4057788f15cc524c7e58817be5dc085f |
aipicasso/cool-japan-diffusion-2-1-1-beta | aipicasso | null | 21 | 318 | diffusers | 10 | text-to-image | false | false | false | other | null | null | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | ['stable-diffusion', 'text-to-image'] | false | true | true | 7,137 | false |
# Cool Japan Diffusion 2.1.1 Beta Model Card
[注意事项。中国将对图像生成的人工智能实施法律限制。 ](http://www.cac.gov.cn/2022-12/11/c_1672221949318230.htm) （中国国内にいる人への警告）
English version is [here](README_en.md).
# はじめに
Cool Japan Diffusion (for learning) はStable Diffsionをファインチューニングして、アニメやマンガ、ゲームなどのクールジャパンを表現することに特化... | 128612d4fd5b2b28bac1f818fbffc7bb |
DOOGLAK/Article_100v7_NER_Model_3Epochs_UNAUGMENTED | DOOGLAK | bert | 13 | 5 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['article100v7_wikigold_split'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,561 | 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. -->
# Article_100v7_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | 45a69054538a670287f756a212b421b0 |
SummerZhang/distilbert-base-uncased-finetuned-squad | SummerZhang | distilbert | 18 | 2 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad_v2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,180 | 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... | 4f22353604ecba850f196094655a5d0f |
clp/vit-base-patch16-224-finetuned | clp | vit | 9 | 9 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['imagefolder'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,454 | 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. -->
# vit-base-patch16-224-finetuned
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google... | 2c010138d535421ded164f5c002d62f0 |
Yuri/xlm-roberta-base-finetuned-panx-de | Yuri | xlm-roberta | 26 | 5 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,320 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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... | f293cf457c742fd4dfac4044c29cf151 |
chrisvinsen/wav2vec2-15 | chrisvinsen | 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 | 2,420 | 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. -->
# wav2vec2-15
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the... | 71746766eb5f78afa54b73d4061cf224 |
Gabriel/bart-base-cnn-swe | Gabriel | bart | 27 | 96 | transformers | 0 | summarization | true | false | false | mit | ['sv'] | ['Gabriel/cnn_daily_swe'] | {'emissions': 0.0334, 'source': 'Google Colab', 'training_type': 'fine-tuning', 'geographical_location': 'Fredericia, Denmark', 'hardware_used': 'Tesla P100-PCIE-16GB'} | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['summarization'] | true | true | true | 4,544 | false |
# bart-base-cnn-swe
This model is a W.I.P
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct... | 6ea19655482812f44ac5593f6b66fe37 |
hiiamsid/BETO_es_binary_classification | hiiamsid | bert | 7 | 4 | transformers | 2 | text-classification | true | false | false | apache-2.0 | ['es'] | ['self made to classify whether text is related to technology or not.'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['es', 'ticket classification'] | false | true | true | 905 | false | # BETO(cased)
This model was built using pytorch.
## Model description
Input for the model: Any spanish text
Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
```python
# You can include s... | c0796c1e7e6ddef709aa47bf6978c39f |
MichaelCHomeX/distilbert-base-uncased-finetuned-imdb | MichaelCHomeX | distilbert | 9 | 0 | 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
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... | 9c960819ac12a41385c34a9928e1818a |
Helsinki-NLP/opus-mt-lue-sv | Helsinki-NLP | marian | 10 | 7 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 776 | false |
### opus-mt-lue-sv
* source languages: lue
* target languages: sv
* OPUS readme: [lue-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lue-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](... | 374430b2d9c55913259feb666ef2bf1b |
eslamxm/mt5-base-finetuned-en-cnn | eslamxm | mt5 | 13 | 5 | transformers | 0 | summarization | true | false | false | apache-2.0 | null | ['cnn_dailymail'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['summarization', 'en', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | true | true | true | 1,211 | 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-base-finetuned-en-cnn
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the... | ccf87daaab582ca64c4b9e62cb3f6dab |
anas-awadalla/t5-base-few-shot-k-64-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 | 968 | 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-64-finetuned-squad-infilling-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggi... | a032ba6168e4ccd0afb4954f4eb1dd16 |
MultiBertGunjanPatrick/multiberts-seed-4-80k | MultiBertGunjanPatrick | bert | 7 | 4 | transformers | 0 | null | true | false | false | apache-2.0 | ['en'] | ['bookcorpus', 'wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | true | true | 6,479 | false | # MultiBERTs Seed 4 Checkpoint 80k (uncased)
Seed 4 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | 4ce124bc741d1ae75dde73c3de397f82 |
Suya03/my_awesome_billsum_model | Suya03 | t5 | 10 | 0 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['billsum'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,707 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum datase... | 16fe2c675025318a10f7c18de86252ed |
rossanez/t5-small-finetuned-de-en-256-wd-01 | rossanez | t5 | 12 | 3 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['wmt14'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,167 | 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-de-en-256-wd-01
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt1... | b8b02b90d4ebd02f3dabfe81cb7931a2 |
leonadase/distilbert-base-uncased-finetuned-ner | leonadase | distilbert | 13 | 5 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['conll2003'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,556 | 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... | 90cd0e08a55b66d816cf64aa2ef4406e |
yanaiela/roberta-base-epoch_21 | yanaiela | roberta | 9 | 3 | transformers | 0 | fill-mask | true | false | false | mit | ['en'] | ['wikipedia', 'bookcorpus'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['roberta-base', 'roberta-base-epoch_21'] | false | true | true | 2,102 | false |
# RoBERTa, Intermediate Checkpoint - Epoch 21
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... | e89f68a90e32894fa255fcc1af61f64b |
dxiao/bert-finetuned-ner | dxiao | bert | 12 | 5 | 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
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# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | 1778afb0eb36cc68d90613cb29fafcaa |
Helsinki-NLP/opus-mt-tc-big-fa-itc | Helsinki-NLP | marian | 13 | 11 | transformers | 0 | translation | true | true | false | cc-by-4.0 | ['fa', 'fr', 'pt', 'ro'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation', 'opus-mt-tc'] | true | true | true | 8,334 | false | # opus-mt-tc-big-fa-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citatio... | a2c948bbe7481ad1742ec5e9b3aba7fa |
DmitryPogrebnoy/MedDistilBertBaseRuCased | DmitryPogrebnoy | distilbert | 8 | 12 | transformers | 0 | fill-mask | true | false | false | apache-2.0 | ['ru'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,245 | false |
# Model DmitryPogrebnoy/MedDistilBertBaseRuCased
# Model Description
This model is fine-tuned version of [DmitryPogrebnoy/distilbert-base-russian-cased](https://huggingface.co/DmitryPogrebnoy/distilbert-base-russian-cased).
The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/M... | fd7213be524b0f36697de322b67a056b |
amartyobanerjee/bert-finetuned-ner | amartyobanerjee | bert | 14 | 21 | 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
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# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | 7db5987e855774fab53bf71050e73fcd |
Helsinki-NLP/opus-mt-pt-tl | Helsinki-NLP | marian | 11 | 8 | transformers | 0 | translation | true | true | false | apache-2.0 | ['pt', 'tl'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 2,000 | false |
### por-tgl
* source group: Portuguese
* target group: Tagalog
* OPUS readme: [por-tgl](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/por-tgl/README.md)
* model: transformer-align
* source language(s): por
* target language(s): tgl_Latn
* model: transformer-align
* pre-processing: normaliz... | be1b5b77920f27a3ba1d656692459b2a |
jhaochenz/finetuned_distilgpt2_sst2_negation0.0001_pretrainedTrue_epochs1 | jhaochenz | gpt2 | 14 | 0 | transformers | 0 | text-generation | true | false | false | apache-2.0 | null | ['sst2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,165 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuned_distilgpt2_sst2_negation0.0001_pretrainedTrue_epochs1
This model is a fine-tuned version of [distilgpt2](https://huggi... | fb01b1fc9a9f222fba58c58144df7f0f |
farleyknight-org-username/vit-base-mnist | farleyknight-org-username | vit | 28 | 1,137 | transformers | 1 | image-classification | true | false | false | apache-2.0 | null | ['mnist'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['image-classification', 'vision', 'generated_from_trainer'] | true | true | true | 1,490 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-mnist
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | 7cb20593448115bc3bb6d2ac8d823bd7 |
vesteinn/fasttext_is_rmh | vesteinn | null | 6 | 0 | null | 0 | null | false | false | false | agpl-3.0 | ['is'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,307 | false | # FastText model trained on Icelandic
This model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5)
This model can not be loaded directly since it uses gensim, clone the rep... | 67b024dad59e6b1b494414fcda681f02 |
JeremiahZ/reproduce-unsup-roberta-base-avg | JeremiahZ | roberta | 23 | 1 | transformers | 0 | null | true | false | false | mit | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,004 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# reproduce-unsup-roberta-base-avg
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an... | c1e535b2239bf90bf99eaabce8291092 |
abyaugustinek/distilbert-base-uncased-finetuned | abyaugustinek | distilbert | 12 | 3 | transformers | 0 | token-classification | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,843 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# abyaugustinek/distilbert-base-uncased-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | 0a31b3f0d07889cc7ab2c9801cd855a7 |
celine98/canine-s-finetuned-sst2 | celine98 | canine | 11 | 4 | transformers | 1 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,451 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# canine-s-finetuned-sst2
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the g... | a0fcef5435cbc9ee297b80bd3f16e03f |
Helsinki-NLP/opus-mt-fi-en | Helsinki-NLP | marian | 11 | 57,390 | transformers | 3 | translation | true | true | false | apache-2.0 | ['fi', 'en'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 2,486 | false |
### fin-eng
* source group: Finnish
* target group: English
* OPUS readme: [fin-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md)
* model: transformer-align
* source language(s): fin
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + ... | fd1c9c87004561b6ee405089b3fe0ce1 |
groar/distilgpt2-finetuned-wikitext2 | groar | gpt2 | 13 | 4 | 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 | 1,140 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | c575780dd6439fc19b8441af8fb014c8 |
mshoaibsarwar/finetuning-sentiment-model-samples | mshoaibsarwar | distilbert | 12 | 4 | 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 | 922 | 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-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distil... | 363ee81c0eabcf08c48a15de7bbed8be |
jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-0_belgium-10_s198 | jonatasgrosman | wav2vec2 | 10 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['fr'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'fr'] | false | true | true | 480 | false | # exp_w2v2r_fr_xls-r_accent_france-0_belgium-10_s198
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make s... | 487a51881b3bef1a55fe6c7e2c8ecb84 |
PaddlePaddle/ernie-m-large | PaddlePaddle | ernie_m | 9 | 0 | paddlenlp | 3 | null | false | false | false | apache-2.0 | ['fr', 'es', 'en', 'de', 'sw', 'ru', 'zh', 'el', 'bg', 'ar', 'vi', 'th', 'hi', 'ur'] | ['xnli', 'mlqa', 'paws-x'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 11,214 | false | [](https://github.com/PaddlePaddle/PaddleNLP)
# PaddlePaddle/ernie-m-base
## Ernie-M
ERNIE-M, proposed by Baidu, is a new training method that encourages the model to align the representation of m... | 57650c4833830b0f2c96e68358655206 |
responsibility-framing/predict-perception-xlmr-focus-victim | responsibility-framing | xlm-roberta | 12 | 21 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 7,857 | false |
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# predict-perception-xlmr-focus-victim
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta... | 3bf33957ecf48466fb7cb71a72141068 |
nielsr/nt5-small-rc1 | nielsr | t5 | 10 | 91 | transformers | 2 | text2text-generation | true | false | true | apache-2.0 | null | ['drop'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 4,140 | false |
# NT5, a T5 model trained to perform numerical reasoning
T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on [DROP](https://allennlp.org/drop.html). It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and fir... | b540745020a818731eda3ce899c58b69 |
csikasote/xlsr-53-bemba-10hrs | csikasote | wav2vec2 | 13 | 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 | 1,829 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlsr-53-bemba-10hrs
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2... | 7173341e150869e8c8d1b491e411b7ca |
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-2_sixties-8_s877 | 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 | 497 | false | # exp_w2v2r_de_vp-100k_age_teens-2_sixties-8_s877
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using t... | 084836cb11805feb241ed2f905e45981 |
Go2Heart/BERT_Mod_2 | Go2Heart | distilbert | 10 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,106 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# BERT_Mod_2
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the gl... | af593e5ce812e1bd58d08b527cb8f9cf |
jkhan447/sarcasm-detection-Bert-base-uncased-POS | jkhan447 | bert | 13 | 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 | 1,030 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# sarcasm-detection-Bert-base-uncased-POS
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-ba... | a8d79419c610a983fc2f35e52013ffdd |
Helsinki-NLP/opus-mt-sv-xh | 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-sv-xh
* source languages: sv
* target languages: xh
* OPUS readme: [sv-xh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-xh/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](http... | 71bbfcc71ccc84ea466d09e8cc1d3cbf |
Gladiator/microsoft-deberta-v3-large_ner_conll2003 | Gladiator | deberta-v2 | 13 | 378 | transformers | 0 | token-classification | true | false | false | mit | null | ['conll2003'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,742 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# microsoft-deberta-v3-large_ner_conll2003
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.... | 39612d0797d739fd477c87d38c79298e |
alexcaillet/ddpm-butterflies-128 | alexcaillet | null | 11 | 3 | diffusers | 0 | null | false | false | false | apache-2.0 | ['en'] | ['huggan/smithsonian_butterflies_subset'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,233 | false |
<!-- This model card has been generated automatically according to the information the training script had access to. You
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# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | ad7b91bc39cbcef26ac2ed52d9939f9f |
AndrewR/distilgpt2-finetuned-imdb-lm | AndrewR | gpt2 | 18 | 9 | 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 | 1,246 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilgpt2-finetuned-imdb-lm
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None d... | 27288f1adcde48ce3d6ec7c82bdd7b75 |
jonatasgrosman/exp_w2v2t_sv-se_vp-it_s975 | 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 | 475 | false | # exp_w2v2t_sv-se_vp-it_s975
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) 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 t... | 967e0f73b0f66078197154f0009b7790 |
IIIT-L/roberta-large-finetuned-code-mixed-DS | IIIT-L | roberta | 11 | 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 | 3,097 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-large-finetuned-code-mixed-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large... | e45f9cec2d0796c6c4d40061fdf8138a |
Keneston/distilbert-base-uncased-finetuned-emotion | Keneston | 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 | 1,344 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | e3cbfe91d1e60b669d5720bbdbed3324 |
Salesforce/codegen-6B-mono | Salesforce | codegen | 10 | 2,517 | transformers | 4 | text-generation | true | false | false | bsd-3-clause | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,933 | false | # CodeGen (CodeGen-Mono 6B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savare... | 88ac0d512da89b9c091387665c74d7d5 |
dbmdz/bert-base-italian-cased | dbmdz | bert | 8 | 14,147 | transformers | 4 | fill-mask | true | true | true | mit | ['it'] | ['wikipedia'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 5,938 | false |
# 🤗 + 📚 dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models 🎉
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikipedia dump and
various texts from the [OPUS corpora](http:/... | 544d14c6a93c01548ec7443a691e36d2 |
microsoft/git-large-textvqa | microsoft | git | 10 | 113 | transformers | 1 | visual-question-answering | true | false | false | mit | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['vision'] | false | true | true | 3,046 | false |
# GIT (GenerativeImage2Text), large-sized, fine-tuned on TextVQA
GIT (short for GenerativeImage2Text) model, large-sized version, fine-tuned on TextVQA. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first relea... | 5ce5f9d4f6ee60632d13d5101a40fe6c |
shkim/distilbert-base-uncased-finetuned-imdb | shkim | distilbert | 9 | 2 | 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... | 3719492cfadf1c3c1d535a819bb8a14f |
nbroad/openai-detector-base | nbroad | roberta | 10 | 25 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 9,255 | false |
# USE THIS SPACE: https://huggingface.co/spaces/nbroad/openai-detector-base
The following is copied from this repo: https://huggingface.co/roberta-base-openai-detector
# RoBERTa Base OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limi... | c04c353f6b1f6a6fe6dc0595e87ef048 |
sandorscog/finetuning-sentiment-model-3000-samples | sandorscog | distilbert | 19 | 0 | 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 | 1,092 | 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... | e01df45d07c46394122f98063c891c43 |
manandey/wav2vec2-large-xlsr-breton | manandey | wav2vec2 | 9 | 9 | transformers | 0 | automatic-speech-recognition | true | false | true | apache-2.0 | ['br'] | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | true | true | true | 3,304 | false |
# Wav2Vec2-Large-XLSR-53-Breton
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Breton using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be... | 0335cb6e6008a8030d4294a0446637d6 |
theojolliffe/bart-large-cnn-finetuned-roundup-2-2 | theojolliffe | bart | 18 | 3 | transformers | 0 | text2text-generation | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,561 | 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-large-cnn-finetuned-roundup-2-2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/face... | e9bae9aa2f0239a5042a086c6bb853f0 |
ZhiyuanQiu/camembert-base-finetuned-sans-symbole-dd | ZhiyuanQiu | camembert | 12 | 6 | transformers | 0 | token-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,635 | 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. -->
# camembert-base-finetuned-sans-symbole-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert... | c4eee46fa24f0b720650561a17c47227 |
Neha2608/xlm-roberta-base-finetuned-panx-fr | Neha2608 | xlm-roberta | 10 | 2 | transformers | 0 | null | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,260 | 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-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | e043ec375c3c7f0f0ccd088b46dd5a64 |
KoichiYasuoka/roberta-base-coptic-upos | KoichiYasuoka | roberta | 9 | 7 | transformers | 0 | token-classification | true | false | false | cc-by-sa-4.0 | ['cop'] | ['universal_dependencies'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | true | true | 881 | false |
# roberta-base-coptic-upos
## Model Description
This is a RoBERTa model pre-trained with [UD_Coptic](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [roberta-base-coptic](https://huggingface.co/KoichiYasuoka/roberta-base-coptic). Every word is tagged by [UPOS](https://uni... | b106a9afaae02a0d575f8c2976fd0775 |
kyryl0s/gpt2-uk-xxs | kyryl0s | gpt2 | 7 | 4 | transformers | 0 | text-generation | true | false | false | afl-3.0 | ['uk'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 713 | false | ## GPT2 being trained on Ukrainian news.
### General info:
The model is not ready yet but I'm working on it. It also has a relatively small context window, which makes it quite uninteresting.
### Example of usage:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_p... | bb87cf4669b21514e9386dd8a7c8ca47 |
fanzru/distilbart-cnn-6-6-finetuned-xsum-intro-test | fanzru | bart | 13 | 3 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['xsum'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,482 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbart-cnn-6-6-finetuned-xsum-intro-test
This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggin... | 4034c96a038de7046e9093d0b8fa7462 |
Maheshnma/distilbert-base-uncased-finetuned-emotion | Maheshnma | distilbert | 12 | 4 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,344 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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... | cf2046a716a400452a1f448987dd0eb9 |
BenTata-86/distilbert-base-turkish-cased-finetuned-emotion | BenTata-86 | distilbert | 18 | 1 | transformers | 0 | text-classification | true | false | false | mit | null | ['turkish-multiclass-dataset'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,430 | 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-turkish-cased-finetuned-emotion
This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](http... | de93228f9c0b285b77bcae3579923059 |
alxdfy/noggles6000 | alxdfy | null | 20 | 3 | diffusers | 0 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 | ['text-to-image'] | false | true | true | 1,329 | false | ### noggles6000 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### Model by alxdfy
This your the Stable Diffusion model fine-tuned the noggles6000 concept taught ... | 27be4a03619e80253aba594d4bc7cdc9 |
Helsinki-NLP/opus-mt-ro-fi | Helsinki-NLP | marian | 10 | 32 | 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-ro-fi
* source languages: ro
* target languages: fi
* OPUS readme: [ro-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ro-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](http... | 0d3704a45957a233988dc7cb6075d826 |
Helsinki-NLP/opus-mt-tc-big-he-en | Helsinki-NLP | marian | 13 | 2,233 | transformers | 0 | translation | true | true | false | cc-by-4.0 | ['en', 'he'] | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['translation', 'opus-mt-tc'] | true | true | true | 5,253 | false | # opus-mt-tc-big-he-en
Neural machine translation model for translating from Hebrew (he) to English (en).
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 model... | 25f663296e9d15b58cb1976d6cd14bce |
fathyshalab/all-roberta-large-v1-utility-3-16-5 | fathyshalab | roberta | 11 | 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 | 1,512 | 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-utility-3-16-5
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://h... | 18deed91f78a092ec3b79c9f2b4a4684 |
google/t5-efficient-base-dl8 | google | t5 | 12 | 30 | transformers | 1 | text2text-generation | true | true | true | apache-2.0 | ['en'] | ['c4'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['deep-narrow'] | false | true | true | 6,248 | false |
# T5-Efficient-BASE-DL8 (Deep-Narrow version)
T5-Efficient-BASE-DL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint an... | 29e08d618507876daec5e166414f7f4c |
EIStakovskii/french_toxicity_classifier_plus_v2 | EIStakovskii | camembert | 8 | 17 | transformers | 0 | text-classification | true | false | false | other | ['fr'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,340 | false | ## Description
NB: this version of the model is the improved version of [EIStakovskii/french_toxicity_classifier_plus](https://huggingface.co/EIStakovskii/french_toxicity_classifier_plus).
To see the source code of training and the data please follow [the github link](https://github.com/eistakovskii/NLP_projects/tree/m... | a9866f52bfdf4bffe12e449fbfd23a24 |
google/t5-efficient-small-nl16 | google | t5 | 12 | 9 | transformers | 0 | text2text-generation | true | true | true | apache-2.0 | ['en'] | ['c4'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['deep-narrow'] | false | true | true | 6,256 | false |
# T5-Efficient-SMALL-NL16 (Deep-Narrow version)
T5-Efficient-SMALL-NL16 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoin... | e8452ae33a83ee155f9fcf34d2304478 |
Tatiana239/lilt-en-funsd | Tatiana239 | lilt | 19 | 1 | transformers | 0 | token-classification | true | false | false | mit | null | ['funsd-layoutlmv3'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 7,738 | 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. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt... | 8987b3598f1177cf589d772c724c72e4 |
sd-concepts-library/gim | sd-concepts-library | null | 13 | 0 | null | 2 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,469 | false | ### Gim on Stable Diffusion
This is the `<grimes-album-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can ... | 0198028516448a7757201ce05edd9773 |
inovex/multi2convai-logistics-pl-bert | inovex | bert | 8 | 3 | transformers | 2 | text-classification | true | false | false | mit | ['pl'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-classification'] | false | true | true | 866 | false |
# Multi2ConvAI-Logistics: finetuned Bert for Polish
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Logistics (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: Polish (pl)
- model... | 9305e7b18da1d982697a9e79494f15dd |
mallikrao2/new_asr_model | mallikrao2 | wav2vec2 | 17 | 4 | 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 | 1,557 | 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. -->
# new_asr_model
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav... | 2dd228b49c266ba2d7ab23d208a32218 |
asapp/sew-small-100k | asapp | sew | 5 | 5 | transformers | 0 | feature-extraction | true | false | false | apache-2.0 | ['en'] | ['librispeech_asr'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['speech'] | false | true | true | 1,696 | false |
# SEW-small
[SEW 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, Speaker... | f387400c6cfe971bda36360b08362f7f |
FluxML/densenet169 | FluxML | null | 3 | 0 | null | 0 | null | false | false | false | mit | null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 523 | false |
DenseNet169 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef).
To use this model in Julia, ... | 5537452051cc3cc30c53c852079f4798 |
Hatman/bert-finetuned-ner | Hatman | bert | 16 | 8 | 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 | 2,188 | 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... | 2382d68b18f8fa87e40f06ece9f32062 |
google/multiberts-seed_3-step_0k | google | bert | 8 | 14 | transformers | 0 | null | true | true | false | apache-2.0 | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_0k'] | false | true | true | 3,509 | false |
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 0k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different ... | a6f3cd09c532a5313fc070820ef4fd38 |
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