modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
class | author stringlengths 2 38 ⌀ | config null | id stringlengths 4 112 | downloads float64 0 36.8M ⌀ | likes float64 0 712 ⌀ | library_name stringclasses 17
values | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
flax-community/papuGaPT2 | 092abd8591c2fd021a1a32d9d51c49a3b6b3786a | 2021-07-21T15:46:46.000Z | [
"pytorch",
"jax",
"tensorboard",
"pl",
"text-generation"
] | text-generation | false | flax-community | null | flax-community/papuGaPT2 | 841 | 1 | null | 1,900 | ---
language: pl
tags:
- text-generation
widget:
- text: "Najsmaczniejszy polski owoc to"
---
# papuGaPT2 - Polish GPT2 language model
[GPT2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) was released in 2019 and surprised many with its text generat... |
sentence-transformers/msmarco-distilroberta-base-v2 | 77b284287cf59954131cae3ea58ae8a2850f96d2 | 2022-06-15T21:58:56.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-distilroberta-base-v2 | 841 | null | sentence-transformers | 1,901 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilroberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensiona... |
ckiplab/albert-base-chinese-ws | 2c4fe1f2486e130209d27120af18e19e9171a9ee | 2022-05-10T03:28:09.000Z | [
"pytorch",
"albert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/albert-base-chinese-ws | 840 | null | transformers | 1,902 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word seg... |
ddemszky/supervised_finetuning_hist0_is_question_switchboard_question_detection.json_bs32_lr0.000063 | a28a1a751e076f551e8ea5a11c18e6ddba01acce | 2021-05-19T15:23:28.000Z | [
"pytorch",
"tensorboard",
"bert",
"transformers"
] | null | false | ddemszky | null | ddemszky/supervised_finetuning_hist0_is_question_switchboard_question_detection.json_bs32_lr0.000063 | 835 | null | transformers | 1,903 | Entry not found |
BM-K/KoSimCSE-bert-multitask | 36bbddfbd319358f15f47c9d4fd79bc860f947a2 | 2022-06-03T01:48:04.000Z | [
"pytorch",
"bert",
"feature-extraction",
"ko",
"transformers",
"korean"
] | feature-extraction | false | BM-K | null | BM-K/KoSimCSE-bert-multitask | 835 | 2 | transformers | 1,904 | ---
language: ko
tags:
- korean
---
https://github.com/BM-K/Sentence-Embedding-is-all-you-need
# Korean-Sentence-Embedding
🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.
## Quick tour
```py... |
eleldar/theme-classification | 017327ef4bf772e5bd0f60bc430190e810b44abb | 2022-05-24T08:36:26.000Z | [
"pytorch",
"jax",
"rust",
"bart",
"text-classification",
"dataset:multi_nli",
"arxiv:1910.13461",
"arxiv:1909.00161",
"transformers",
"license:mit",
"zero-shot-classification"
] | zero-shot-classification | false | eleldar | null | eleldar/theme-classification | 834 | 1 | transformers | 1,905 | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
---
# Clone from [https://huggingface.co/facebook/bart-large-mnli](bart-large-mnli)
This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after be... |
Rostlab/prot_bert_bfd_membrane | 19b4644ba13c8562e4fa65181da72362495c802a | 2021-05-18T22:08:28.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Rostlab | null | Rostlab/prot_bert_bfd_membrane | 832 | 2 | transformers | 1,906 | Entry not found |
cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR | 0e85bb72e60edfb0ddde7ad756b51898ad7e2854 | 2021-05-27T18:49:34.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2010.11784",
"transformers"
] | feature-extraction | false | cambridgeltl | null | cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR | 831 | null | transformers | 1,907 | ---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-XLMR
SapBERT [(Liu et... |
ltrctelugu/bert_ltrc_telugu | 469815eaeec6eef3033ad62495a9b957488045f2 | 2021-05-19T22:09:24.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ltrctelugu | null | ltrctelugu/bert_ltrc_telugu | 831 | null | transformers | 1,908 | Entry not found |
gilf/french-camembert-postag-model | 20dcf94faa1d071027b36191220475bf9865f1f3 | 2020-12-11T21:41:07.000Z | [
"pytorch",
"tf",
"camembert",
"token-classification",
"fr",
"transformers",
"autotrain_compatible"
] | token-classification | false | gilf | null | gilf/french-camembert-postag-model | 830 | 1 | transformers | 1,909 | ---
language: fr
widget:
- text: "Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages"
---
## About
The *french-camembert-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset availab... |
sentence-transformers/roberta-base-nli-mean-tokens | 993765530351e8b2a4da74bed694d80de826cbb3 | 2022-06-15T21:54:45.000Z | [
"pytorch",
"tf",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/roberta-base-nli-mean-tokens | 830 | null | sentence-transformers | 1,910 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ 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... |
Nonzerophilip/bert-finetuned-ner_swedish_small_set_health_and_standart | e8457dc919931cb03fc4b56f6bc57aee2e0430ae | 2022-07-12T12:42:31.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | Nonzerophilip | null | Nonzerophilip/bert-finetuned-ner_swedish_small_set_health_and_standart | 830 | 0 | transformers | 1,911 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner_swedish_small_set_health_and_standart
results: []
---
# Named Entity Recognition model for swedish
This model is a fine-tuned version of [KBLab/bert-base-swedish-cased-ner](https://huggingface.co/K... |
ceshine/t5-paraphrase-quora-paws | fabd672433e675b3d9916595fa4cb768b18e15c5 | 2021-09-22T08:16:42.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"transformers",
"paraphrasing",
"paraphrase",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | ceshine | null | ceshine/t5-paraphrase-quora-paws | 829 | 1 | transformers | 1,912 | ---
language: en
tags:
- t5
- paraphrasing
- paraphrase
license: apache-2.0
---
# T5-base Parapharasing model fine-tuned on PAWS and Quora
More details in [ceshine/finetuning-t5 Github repo](https://github.com/ceshine/finetuning-t5/tree/master/paraphrase) |
gchhablani/bert-base-cased-finetuned-sst2 | e3a2a13efbaaf56afd02eb7333952ea22a693c45 | 2021-09-20T09:09:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"en",
"dataset:glue",
"arxiv:2105.03824",
"transformers",
"generated_from_trainer",
"fnet-bert-base-comparison",
"license:apache-2.0",
"model-index"
] | text-classification | false | gchhablani | null | gchhablani/bert-base-cased-finetuned-sst2 | 829 | null | transformers | 1,913 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: g... |
lvwerra/bert-imdb | 2e60eb015f5ace0a52a9d0394b63f9db23819139 | 2021-05-19T22:12:49.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | lvwerra | null | lvwerra/bert-imdb | 829 | null | transformers | 1,914 | # BERT-IMDB
## What is it?
BERT (`bert-large-cased`) trained for sentiment classification on the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews).
## Training setting
The model was trained on 80% of the IMDB dataset for sentiment classification for three epochs with a learning... |
ufal/eleczech-lc-small | f30a3b6aeb23d99ff4e342f853e9b4dca4fd90fb | 2022-04-24T11:47:37.000Z | [
"pytorch",
"tf",
"electra",
"cs",
"transformers",
"Czech",
"Electra",
"ÚFAL",
"license:cc-by-nc-sa-4.0"
] | null | false | ufal | null | ufal/eleczech-lc-small | 828 | null | transformers | 1,915 | ---
language: "cs"
tags:
- Czech
- Electra
- ÚFAL
license: "cc-by-nc-sa-4.0"
---
# EleCzech-LC model
THe `eleczech-lc-small` is a monolingual small Electra language representation
model trained on lowercased Czech data (but with diacritics kept in place).
It is trained on the same data as the
[RobeCzech model](https... |
urduhack/roberta-urdu-small | 88b0711632a90aa462d37b3fd01b3db5a999901f | 2021-05-20T22:52:23.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"ur",
"transformers",
"roberta-urdu-small",
"urdu",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | urduhack | null | urduhack/roberta-urdu-small | 827 | 1 | transformers | 1,916 | ---
language: ur
thumbnail: https://raw.githubusercontent.com/urduhack/urduhack/master/docs/_static/urduhack.png
tags:
- roberta-urdu-small
- urdu
- transformers
license: mit
---
## roberta-urdu-small
[](https://github.com/urduhack/urduhack/blob/master/... |
rinna/japanese-clip-vit-b-16 | 577833e50353203aad9b0f01c9ed54f45d7f0dd9 | 2022-07-19T05:46:31.000Z | [
"pytorch",
"clip",
"feature-extraction",
"ja",
"arxiv:2103.00020",
"transformers",
"japanese",
"vision",
"license:apache-2.0"
] | feature-extraction | false | rinna | null | rinna/japanese-clip-vit-b-16 | 825 | 3 | transformers | 1,917 | ---
language: ja
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
license: apache-2.0
tags:
- feature-extraction
- ja
- japanese
- clip
- vision
---
# rinna/japanese-clip-vit-b-16
This is a Japanese [CLIP (Contrastive Language-Image Pre-Training)](http... |
allenai/ivila-row-layoutlm-finetuned-s2vl-v2 | 274db24cfd62b34df30d0f25705716a76ea285fc | 2022-07-06T00:00:40.000Z | [
"pytorch",
"layoutlm",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | allenai | null | allenai/ivila-row-layoutlm-finetuned-s2vl-v2 | 823 | null | transformers | 1,918 | Entry not found |
cross-encoder/quora-roberta-large | 5ebcad2722b5ebd1e04eff48be928eab15088827 | 2021-08-05T08:41:41.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/quora-roberta-large | 822 | null | transformers | 1,919 | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questi... |
twigs/cwi-regressor | df4bd35ae50d6cbc357dcfafeb015d705881fb94 | 2022-07-16T20:11:55.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | twigs | null | twigs/cwi-regressor | 821 | null | transformers | 1,920 | Entry not found |
Helsinki-NLP/opus-mt-ca-es | 3b93f0ccce95f7d8c7a78d56ec5c658271f6d244 | 2021-09-09T21:28:22.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ca",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ca-es | 820 | null | transformers | 1,921 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ca-es
* source languages: ca
* target languages: es
* OPUS readme: [ca-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ca-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
textattack/bert-base-uncased-RTE | 44f1d994cbd4a349cb7867681940bdb1f0472f53 | 2021-05-20T07:36:18.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-RTE | 817 | null | transformers | 1,922 | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... |
sonoisa/sentence-bert-base-ja-en-mean-tokens-v2 | 183edaac7298717e619cae545da453870aae1090 | 2022-05-31T09:07:58.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | sonoisa | null | sonoisa/sentence-bert-base-ja-en-mean-tokens-v2 | 815 | null | transformers | 1,923 | Entry not found |
lsanochkin/deberta-large-feedback | 9c2c8e80c27264968be42d2dd8ba18da34b0ac43 | 2022-06-08T12:48:08.000Z | [
"pytorch",
"deberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | lsanochkin | null | lsanochkin/deberta-large-feedback | 815 | null | transformers | 1,924 | Entry not found |
Geotrend/distilbert-base-en-zh-cased | 1ec581bd42270966d6d7f748c16dc58e909b3a3f | 2021-08-16T13:56:31.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/distilbert-base-en-zh-cased | 813 | null | transformers | 1,925 | ---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-zh-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same repres... |
VietAI/vit5-base | 1e7647a478bbceb29ab07307b2ceab6fd34fddee | 2022-07-25T14:15:09.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"vi",
"dataset:cc100",
"transformers",
"summarization",
"translation",
"question-answering",
"license:mit",
"autotrain_compatible"
] | question-answering | false | VietAI | null | VietAI/vit5-base | 811 | null | transformers | 1,926 | ---
language: vi
datasets:
- cc100
tags:
- summarization
- translation
- question-answering
license: mit
---
# ViT5-base
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
## How to use
For more details, do check out [our Github repo](https://github.com/vietai/ViT5).
```python
from... |
sentence-transformers/msmarco-distilbert-base-v2 | f948558941b586773dcbc8f8df8576cd00a7547e | 2022-06-15T21:47:13.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-distilbert-base-v2 | 810 | null | sentence-transformers | 1,927 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilbert-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional d... |
nvidia/segformer-b4-finetuned-cityscapes-1024-1024 | 1c5bd529b95681dbd2781393c3c0d1f4049f5aa3 | 2022-07-20T09:53:27.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:cityscapes",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b4-finetuned-cityscapes-1024-1024 | 809 | null | transformers | 1,928 | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- cityscapes
widget:
- src: https://www.researchgate.net/profile/Anurag-Arnab/publication/315881952/figure/fig5/AS:667673876779033@1536197265755/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.jpg
example_ti... |
ugaray96/biobert_ncbi_disease_ner | 5c229c40de74adc3ba6ce7266edbbb72338957d9 | 2021-05-20T08:46:47.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ugaray96 | null | ugaray96/biobert_ncbi_disease_ner | 808 | 3 | transformers | 1,929 | Entry not found |
OFA-Sys/OFA-large | 89faeabf5697e4f6cc32c16a258fee8555a00ea3 | 2022-07-25T11:50:28.000Z | [
"pytorch",
"ofa",
"transformers",
"license:apache-2.0"
] | null | false | OFA-Sys | null | OFA-Sys/OFA-large | 806 | 3 | transformers | 1,930 | ---
license: apache-2.0
---
# OFA-large
This is the **large** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.... |
Helsinki-NLP/opus-mt-ml-en | 6a3938f58579cd6b4aab8ffcd6d8f6ccbe96571e | 2021-09-10T13:58:12.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ml",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ml-en | 805 | null | transformers | 1,931 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ml-en
* source languages: ml
* target languages: en
* OPUS readme: [ml-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ml-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
GanjinZero/coder_eng_pp | 1b92944b53581cdee9c7a6a6c85d3706c5f39a87 | 2022-04-25T02:25:08.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2204.00391",
"transformers",
"biomedical",
"license:apache-2.0"
] | feature-extraction | false | GanjinZero | null | GanjinZero/coder_eng_pp | 803 | 1 | transformers | 1,932 | ---
language:
- en
license: apache-2.0
tags:
- bert
- biomedical
---
Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations.
CODER++
```
@misc{https://doi.org/10.48550/arxiv.2204.00391,
doi = {10.48550/ARXIV.2204.00391},
url = {https://arxiv.org/abs/2204.00391},
author = {Zeng, ... |
yechen/bert-large-chinese | 0b30aa41703e93273f06788ce54ca2a678fa3461 | 2021-05-20T09:22:07.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"transformers",
"autotrain_compatible"
] | fill-mask | false | yechen | null | yechen/bert-large-chinese | 803 | 1 | transformers | 1,933 | ---
language: zh
---
|
nvidia/segformer-b1-finetuned-ade-512-512 | cfbb1c34d34eae30bcf0a6d5e26a650f9b7263de | 2022-07-20T09:53:21.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:scene_parse_150",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b1-finetuned-ade-512-512 | 802 | null | transformers | 1,934 | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- scene_parse_150
widget:
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
example_title: House
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_... |
sentence-transformers/msmarco-MiniLM-L-12-v3 | 0ff694ede6574bbed1625216e5bd1a2f517abaf1 | 2022-06-16T00:16:13.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-MiniLM-L-12-v3 | 802 | null | sentence-transformers | 1,935 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-MiniLM-L-12-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense... |
fmikaelian/camembert-base-squad | 026a804142163cd9756f66ba35cfb463d914e4e4 | 2020-12-11T21:40:12.000Z | [
"pytorch",
"camembert",
"question-answering",
"fr",
"transformers",
"autotrain_compatible"
] | question-answering | false | fmikaelian | null | fmikaelian/camembert-base-squad | 800 | null | transformers | 1,936 | ---
language: fr
---
# camembert-base-squad
## Description
A baseline model for question-answering in french ([CamemBERT](https://camembert-model.fr/) model fine-tuned on [french-translated SQuAD 1.1 dataset](https://github.com/Alikabbadj/French-SQuAD))
## Training hyperparameters
```shell
python3 ./e... |
ahmedrachid/FinancialBERT-Sentiment-Analysis | 656931965473ec085d195680bd62687b140c038f | 2022-02-07T14:58:57.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:financial_phrasebank",
"transformers",
"financial-sentiment-analysis",
"sentiment-analysis"
] | text-classification | false | ahmedrachid | null | ahmedrachid/FinancialBERT-Sentiment-Analysis | 798 | 6 | transformers | 1,937 | ---
language: en
tags:
- financial-sentiment-analysis
- sentiment-analysis
datasets:
- financial_phrasebank
widget:
- text: Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.
- text: Bids or offers include at least 1,000 shares and the value of the ... |
allenai/wmt19-de-en-6-6-big | ad688bb4b2d008debf4477e33a7c809eed3f198b | 2020-12-11T21:33:31.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"de",
"en",
"dataset:wmt19",
"arxiv:2006.10369",
"transformers",
"translation",
"wmt19",
"allenai",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | allenai | null | allenai/wmt19-de-en-6-6-big | 795 | null | transformers | 1,938 |
---
language:
- de
- en
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en.
For more details, please, see [Deep E... |
monilouise/ner_news_portuguese | 3d99719c7273f8509954b0023ecc273228f9cdd7 | 2021-09-13T17:12:03.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"pt",
"arxiv:1909.10649",
"transformers",
"ner",
"autotrain_compatible"
] | token-classification | false | monilouise | null | monilouise/ner_news_portuguese | 795 | 6 | transformers | 1,939 | ---
language:
- pt
tags:
- ner
metrics:
- f1
- accuracy
- precision
- recall
---
# RiskData Brazilian Portuguese NER
## Model description
This is a finetunned version from [Neuralmind BERTimbau] (https://github.com/neuralmind-ai/portuguese-bert/blob/master/README.md) for Portuguese language.
## Intended uses & lim... |
tdopierre/ProtAugment-LM-Clinic150 | 4be859e9b5cfd551558a1868f05eff9acf49742d | 2021-07-01T13:57:20.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | tdopierre | null | tdopierre/ProtAugment-LM-Clinic150 | 795 | null | transformers | 1,940 | Entry not found |
Amo/GPT-J-6B-PNY | 6ea745f4c4d4105f8d5bb802651de7d23505be81 | 2022-07-08T14:13:05.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers"
] | text-generation | false | Amo | null | Amo/GPT-J-6B-PNY | 795 | 1 | transformers | 1,941 | Entry not found |
Intel/dpt-large | 755bcbcb26ef6d8ae380fef8b193bd60bc729026 | 2022-04-14T08:29:11.000Z | [
"pytorch",
"dpt",
"arxiv:2103.13413",
"transformers",
"vision",
"depth-estimation",
"license:apache-2.0"
] | null | false | Intel | null | Intel/dpt-large | 794 | 9 | transformers | 1,942 | ---
license: apache-2.0
tags:
- vision
- depth-estimation
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/m... |
tau/splinter-base-qass | c400ac0b86b16bc4d73508561d3eca78f89c52d7 | 2021-09-03T08:47:00.000Z | [
"pytorch",
"splinter",
"question-answering",
"en",
"transformers",
"SplinterModel",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | tau | null | tau/splinter-base-qass | 793 | null | transformers | 1,943 | ---
language: en
tags:
- splinter
- SplinterModel
license: apache-2.0
---
# Splinter base model (with pretrained QASS-layer weights)
Splinter-base is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its ... |
textattack/albert-base-v2-ag-news | f2a40f157353809cd0f76610fc9657385e8761d4 | 2020-07-07T21:59:15.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/albert-base-v2-ag-news | 790 | null | transformers | 1,944 | ## TextAttack Model CardThis `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... |
castorini/mdpr-question-nq | b560ea82c60118c04b388a339f1c089c2a720c70 | 2021-08-20T15:07:57.000Z | [
"pytorch",
"dpr",
"feature-extraction",
"transformers"
] | feature-extraction | false | castorini | null | castorini/mdpr-question-nq | 785 | null | transformers | 1,945 | Entry not found |
indonesian-nlp/wav2vec2-large-xlsr-indonesian | 68fbcbd947e32184a704b401b71973d6c27de0c1 | 2021-07-06T06:15:38.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"id",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | indonesian-nlp | null | indonesian-nlp/wav2vec2-large-xlsr-indonesian | 785 | 1 | transformers | 1,946 | ---
language: id
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Indonesian by Indonesian NLP
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
da... |
uer/roberta-base-word-chinese-cluecorpussmall | 9122f6001eb2eedea9d34eeafc254a4f83f653aa | 2022-02-19T15:58:50.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/roberta-base-word-chinese-cluecorpussmall | 785 | 2 | transformers | 1,947 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "最近一趟去北京的[MASK]几点发车"
---
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/... |
google/pegasus-reddit_tifu | b786930d75e6108dd9a42fc062658a5ff77386ff | 2020-10-22T16:33:34.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-reddit_tifu | 784 | null | transformers | 1,948 | ---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@... |
funnel-transformer/large | 3f56dd1209e9470b922bd24715c78db03af0fe51 | 2022-06-07T15:26:48.000Z | [
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | funnel-transformer | null | funnel-transformer/large | 783 | 1 | transformers | 1,949 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
- gigaword
---
# Funnel Transformer large model (B8-8-8 with decoder)
Pretrained model on English language using a similar objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https... |
ydshieh/vit-gpt2-coco-en | 65636df6deb3da3e23106321ae33cb82adbc845a | 2022-01-09T15:53:19.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"vision-encoder-decoder",
"generic",
"image-classification"
] | image-classification | false | ydshieh | null | ydshieh/vit-gpt2-coco-en | 782 | 7 | generic | 1,950 | ---
tags:
- image-classification
library_name: generic
---
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
**I... |
facebook/data2vec-audio-large | 8aebfda61f5749e532f5577d176072ea38a6b349 | 2022-04-18T16:29:14.000Z | [
"pytorch",
"data2vec-audio",
"feature-extraction",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/data2vec-audio-large | 782 | null | transformers | 1,951 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Data2Vec-Audio-Large
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The large model pretrained on 16kHz sampled speech audio. When u... |
dmis-lab/biosyn-biobert-ncbi-disease | 7ef898a280b7fd3d60c16880c7304dcaeda0860b | 2021-10-25T14:43:20.000Z | [
"pytorch",
"transformers"
] | null | false | dmis-lab | null | dmis-lab/biosyn-biobert-ncbi-disease | 780 | 1 | transformers | 1,952 | Entry not found |
MoritzLaurer/policy-distilbert-7d | dd0868b3441cfeb208771aea159f755bb7a3e4ab | 2021-01-04T20:22:18.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers"
] | text-classification | false | MoritzLaurer | null | MoritzLaurer/policy-distilbert-7d | 779 | 2 | transformers | 1,953 | ---
language:
- en
tags:
- text-classification
metrics:
- accuracy (balanced)
- F1 (weighted)
widget:
- text: "70-85% of the population needs to get vaccinated against the novel coronavirus to achieve herd immunity."
---
# Policy-DistilBERT-7d
## Model description
This model was trained on 129.669 manually annota... |
microsoft/tapex-large-finetuned-wtq | 0cd74d3e2a094d1712e631095e38ec6256e22867 | 2022-07-14T10:12:06.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:wikitablequestions",
"arxiv:2107.07653",
"transformers",
"tapex",
"table-question-answering",
"license:mit",
"autotrain_compatible"
] | table-question-answering | false | microsoft | null | microsoft/tapex-large-finetuned-wtq | 779 | 2 | transformers | 1,954 | ---
language: en
tags:
- tapex
- table-question-answering
datasets:
- wikitablequestions
license: mit
---
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weiz... |
CEBaB/bert-base-uncased.CEBaB.sa.5-class.exclusive.seed_42 | 31f7c9163f86707a680430a9a4950f76f35382e3 | 2022-05-10T23:56:18.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB.sa.5-class.exclusive.seed_42 | 779 | null | transformers | 1,955 | Entry not found |
Sigma/financial-sentiment-analysis | d78ca172e07e94390f615739cee98a2154381f7e | 2022-05-14T11:48:56.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:financial_phrasebank",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | Sigma | null | Sigma/financial-sentiment-analysis | 779 | null | transformers | 1,956 | ---
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- accuracy
- f1
model-index:
- name: financial-sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
args: s... |
kykim/funnel-kor-base | 6bc460ff2e566cd8a18ce99d58141f0238bab2bd | 2021-01-22T01:56:37.000Z | [
"pytorch",
"tf",
"funnel",
"feature-extraction",
"ko",
"transformers"
] | feature-extraction | false | kykim | null | kykim/funnel-kor-base | 777 | null | transformers | 1,957 | ---
language: ko
---
# Funnel-transformer base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import FunnelTokenizer, FunnelModel
tok... |
techthiyanes/chinese_sentiment | 215de6a818f1fbd430e8a1d45b7a4681c0936102 | 2021-05-20T07:28:06.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | techthiyanes | null | techthiyanes/chinese_sentiment | 773 | 3 | transformers | 1,958 | Entry not found |
naver-clova-ix/donut-base | a8138504038547801557466623fbc4946bb9bb68 | 2022-07-19T13:50:39.000Z | [
"pytorch",
"donut",
"transformers",
"license:mit"
] | null | false | naver-clova-ix | null | naver-clova-ix/donut-base | 773 | null | transformers | 1,959 | ---
license: mit
---
|
allenai/tk-instruct-3b-def | 3d9f0e250c341d4d5a8ff1018938379c65c6ac52 | 2022-05-27T06:28:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:natural instructions v2.0",
"arxiv:1910.10683",
"arxiv:2204.07705",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/tk-instruct-3b-def | 771 | null | transformers | 1,960 | ---
language: en
license: apache-2.0
datasets:
- natural instructions v2.0
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built... |
lanwuwei/BERTOverflow_stackoverflow_github | 50faf74c11305823a594e001eacba6be9cda1939 | 2021-05-19T00:15:32.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | lanwuwei | null | lanwuwei/BERTOverflow_stackoverflow_github | 769 | null | transformers | 1,961 |
# BERTOverflow
## Model description
We pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: [Code and Named Entity Recognition in StackOverflow](https://www.aclweb.org/anthology/2020.acl-main.443/).
#### Ho... |
naver-clova-ix/donut-base-finetuned-docvqa | 679d8ec7f097ca70e59b35359726db1a4d92ffe5 | 2022-07-19T14:00:09.000Z | [
"pytorch",
"donut",
"transformers",
"license:mit"
] | null | false | naver-clova-ix | null | naver-clova-ix/donut-base-finetuned-docvqa | 769 | null | transformers | 1,962 | ---
license: mit
---
|
Averium/FabioBot | fd68f97eb35228980128efd5dae17769262996ee | 2022-06-23T22:21:28.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Averium | null | Averium/FabioBot | 768 | null | transformers | 1,963 | ---
tags:
- conversational
---
# Twin-Tailed Fabio DialoGPT Model
|
wietsedv/bert-base-dutch-cased-finetuned-sentiment | c9802e7e1da3cbef9e33a3e745d6af1e923a4c8b | 2022-06-23T14:05:56.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | wietsedv | null | wietsedv/bert-base-dutch-cased-finetuned-sentiment | 767 | 1 | transformers | 1,964 | Entry not found |
alenusch/mt5large-ruparaphraser | 12e8714a889fcd0c4a7a5db904ae09760ed1362d | 2020-12-18T15:50:04.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | alenusch | null | alenusch/mt5large-ruparaphraser | 766 | null | transformers | 1,965 | Entry not found |
lordtt13/emo-mobilebert | 55364424cefcffb88aaec8ee8129030e3b069174 | 2020-12-11T21:49:42.000Z | [
"pytorch",
"tf",
"mobilebert",
"text-classification",
"en",
"dataset:emo",
"arxiv:2004.02984",
"transformers"
] | text-classification | false | lordtt13 | null | lordtt13/emo-mobilebert | 766 | 2 | transformers | 1,966 | ---
language: en
datasets:
- emo
---
## Emo-MobileBERT: a thin version of BERT LARGE, trained on the EmoContext Dataset from scratch
### Details of MobileBERT
The **MobileBERT** model was presented in [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by *Zhiqin... |
Geotrend/distilbert-base-da-cased | 6e07346d5a45ed6002fc19662ce2f17dd1335b02 | 2021-07-27T19:35:33.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"da",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/distilbert-base-da-cased | 765 | null | transformers | 1,967 | ---
language: da
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-da-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations pro... |
PlanTL-GOB-ES/roberta-large-bne-capitel-ner | 521c79d4bbcedb062d03ecd4bec577525fb66756 | 2022-04-06T14:43:32.000Z | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/roberta-large-bne-capitel-ner | 764 | null | transformers | 1,968 | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "capitel"
- "ner"
datasets:
- "bne"
- "capitel"
metrics:
- "f1"
inference:
parameters:
aggregation_strategy: "first"
---
# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (N... |
allenyummy/chinese-bert-wwm-ehr-ner-sl | 0020467f0a55b08401f9d94d3f5d7edc2d92c7e1 | 2021-05-19T11:42:42.000Z | [
"pytorch",
"bert",
"zh-tw",
"transformers"
] | null | false | allenyummy | null | allenyummy/chinese-bert-wwm-ehr-ner-sl | 764 | null | transformers | 1,969 | ---
language: zh-tw
---
# Model name
Chinese-bert-wwm-electrical-health-records-ner-sequence-labeling
#### How to use
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("allenyummy/chinese-bert-wwm-ehr-ner-sl")
model = AutoModelForTokenClassific... |
google/t5-small-ssm-nq | 5a100af5c3b308940b33784587c511e062572c67 | 2021-06-23T01:51:51.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:natural_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-small-ssm-nq | 758 | 1 | transformers | 1,970 | ---
language: en
datasets:
- c4
- wikipedia
- natural_questions
pipeline_tag: text2text-generation
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C... |
mrm8488/TinyBERT-spanish-uncased-finetuned-ner | a1eb804867635f501d62dda14e7ae8a182f96dd6 | 2021-05-20T00:18:21.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"es",
"transformers",
"autotrain_compatible"
] | token-classification | false | mrm8488 | null | mrm8488/TinyBERT-spanish-uncased-finetuned-ner | 756 | null | transformers | 1,971 | ---
language: es
thumbnail:
---
# Spanish TinyBERT + NER
This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) of a [Spanish Tiny Bert](https://huggingface.co/mrm8488/es-tinybert-v1-1) model I created using *distillation* for **NER** downstream task. The **size** of the model is **55MB*... |
uclanlp/visualbert-vqa | e3da2dbe26fd794128d151228cc771a584f56c6e | 2021-05-31T11:32:07.000Z | [
"pytorch",
"visual_bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | uclanlp | null | uclanlp/visualbert-vqa | 756 | 2 | transformers | 1,972 | Entry not found |
emanjavacas/MacBERTh | d709ce9a3cf02bf27e580e65fa50847edf02f64d | 2022-01-17T16:02:47.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | emanjavacas | null | emanjavacas/MacBERTh | 753 | 4 | transformers | 1,973 | Documentation In Progress... |
microsoft/layoutlmv3-base-chinese | 8cb50a024b05cb4f598084df3c60f7536557c5b4 | 2022-07-20T09:36:16.000Z | [
"pytorch",
"layoutlmv3",
"zh",
"arxiv:2204.08387",
"transformers",
"license:cc-by-nc-sa-4.0"
] | null | false | microsoft | null | microsoft/layoutlmv3-base-chinese | 753 | 9 | transformers | 1,974 | ---
language: zh
license: cc-by-nc-sa-4.0
---
# LayoutLMv3
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3)
## Model description
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The sim... |
PlanTL-GOB-ES/gpt2-large-bne | b2220f1ffa1ea27bbd8a97707c7ba575860cf23b | 2022-04-06T14:41:27.000Z | [
"pytorch",
"gpt2",
"text-generation",
"es",
"dataset:bne",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0"
] | text-generation | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/gpt2-large-bne | 752 | 6 | transformers | 1,975 | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
---
# GPT2-large trained with data from National Library of Spain (BNE)
## Model Description
GPT2-large-bne is a transformer-based model for the Spanish language. It is based on the [GP... |
chinhon/headline_writer | 5b3da936c338942644c2f310a859fa8e6f514838 | 2021-10-24T17:00:55.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:chinhon/autonlp-data-sg_headline_generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | chinhon | null | chinhon/headline_writer | 751 | 6 | transformers | 1,976 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- chinhon/autonlp-data-sg_headline_generator
co2_eq_emissions: 114.71292762345828
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25965855
- CO2 Emissions (in grams): 114.71292762345828
## Validation Metrics
- L... |
abhibisht89/spanbert-large-cased-finetuned-ade_corpus_v2 | 54ef513694d36c0c8278511c48504849d1a464cf | 2021-11-21T15:23:59.000Z | [
"pytorch",
"bert",
"token-classification",
"en",
"dataset:ade_corpus_v2",
"transformers",
"spanbert",
"autotrain_compatible"
] | token-classification | false | abhibisht89 | null | abhibisht89/spanbert-large-cased-finetuned-ade_corpus_v2 | 750 | 1 | transformers | 1,977 | ---
language: en
tags:
- spanbert
datasets:
- ade_corpus_v2
widget:
- text: "Having fever after taking paracetamol."
example_title: "NER"
- text: "Birth defects associated with thalidomide."
example_title: "NER"
- text: "Deafness and kidney failure associated with gentamicin (an antibiotic)."
example_title: "NER"... |
ptaszynski/yacis-electra-small-japanese-cyberbullying | e75924e558e6b03482dea029d670b8da35f58274 | 2022-01-16T13:51:28.000Z | [
"pytorch",
"electra",
"text-classification",
"ja",
"dataset:YACIS corpus",
"dataset:Harmful BBS Japanese comments dataset",
"dataset:Twitter Japanese cyberbullying dataset",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | false | ptaszynski | null | ptaszynski/yacis-electra-small-japanese-cyberbullying | 749 | 1 | transformers | 1,978 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- YACIS corpus
- Harmful BBS Japanese comments dataset
- Twitter Japanese cyberbullying dataset
---
# yacis-electra-small-cyberbullying
This is an [ELECTRA](https://github.com/google-research/electra) Small model for the Japanese language finetuned for automatic c... |
uer/t5-small-chinese-cluecorpussmall | d38b6b837e2f9cbd3325fb270bfae2fda2f679f4 | 2022-07-15T08:22:32.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uer | null | uer/t5-small-chinese-cluecorpussmall | 748 | 6 | transformers | 1,979 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "作为电子extra0的平台，京东绝对是领先者。如今的刘强extra1已经是身价过extra2的老板。"
---
# Chinese T5
## Model description
This is the set of Chinese T5 models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.0565... |
SEBIS/code_trans_t5_small_code_documentation_generation_python | 372c18254de8ff9ab651e1e3c83b7dfeae22ac6a | 2021-06-23T10:09:35.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_small_code_documentation_generation_python | 746 | null | transformers | 1,980 | ---
tags:
- summarization
widget:
- text: "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
---
# CodeTrans model for code documentation generation python
Pretrained model on programming language python using the t5 small model architec... |
asi/gpt-fr-cased-base | d6f928a792f4c72aade4e1b80c88d425884bfa47 | 2022-06-01T12:31:14.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fr",
"transformers",
"license:apache-2.0"
] | text-generation | false | asi | null | asi/gpt-fr-cased-base | 746 | 13 | transformers | 1,981 | ---
language:
- fr
thumbnail: https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png
tags:
- tf
- pytorch
- gpt2
- text-generation
license: apache-2.0
---
<img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png" width="200">
## Model description
**GPT-fr** 🇫🇷 is... |
thunlp/neuba-bert | 033bcb302e36ce9ddff22585834be6d7020e7f52 | 2021-09-16T06:14:06.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | thunlp | null | thunlp/neuba-bert | 746 | null | transformers | 1,982 | Entry not found |
uw-hai/polyjuice | f5bef2f7053c2ce6c3fd19875c3cff77754479ef | 2021-05-24T01:21:24.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"counterfactual generation"
] | text-generation | false | uw-hai | null | uw-hai/polyjuice | 745 | 1 | transformers | 1,983 | ---
language: "en"
tags:
- counterfactual generation
widget:
- text: "It is great for kids. <|perturb|> [negation] It [BLANK] great for kids. [SEP]"
---
# Polyjuice
## Model description
This is a ported version of [Polyjuice](https://homes.cs.washington.edu/~wtshuang/static/papers/2021-arxiv-polyjuice.pdf), the gene... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-half | ed1803781de55d0463976677a3e736c097e16728 | 2021-09-14T14:34:06.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-msa-half | 744 | 2 | transformers | 1,984 | ---
language:
- ar
license: apache-2.0
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained langu... |
huggingtweets/realdonaldtrump | d03b45baa4168d2a87f7faa6f54ce2a67e8c8bd5 | 2021-05-22T20:32:50.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/realdonaldtrump | 744 | null | transformers | 1,985 | ---
language: en
thumbnail: https://www.huggingtweets.com/realdonaldtrump/1609765003661/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/87427619... |
nreimers/MiniLM-L3-H384-uncased | 1857068a44253cf9ab23aefdf562335601cffcda | 2021-08-30T20:05:09.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | nreimers | null | nreimers/MiniLM-L3-H384-uncased | 743 | null | transformers | 1,986 | ---
license: mit
---
## MiniLM: 3 Layer Version
This is a 3 layer version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased/) by keeping only the layer [3, 7, 11]. |
sentence-transformers/facebook-dpr-ctx_encoder-multiset-base | ae43c02d3267d1f11db4e7bfcfefc684c83b0c69 | 2022-06-15T22:51:25.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/facebook-dpr-ctx_encoder-multiset-base | 743 | null | sentence-transformers | 1,987 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/facebook-dpr-ctx_encoder-multiset-base
This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [sentence-transformers](htt... |
armheb/DNA_bert_5 | c296157b5c23f30f7cac33039c17ab7422798346 | 2021-10-10T22:40:00.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | armheb | null | armheb/DNA_bert_5 | 742 | null | transformers | 1,988 | Entry not found |
bhadresh-savani/roberta-base-emotion | fa563824e043b85c87b3f1991bbb3c3d045a68c7 | 2022-07-06T10:44:12.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"en",
"dataset:emotion",
"arxiv:1907.11692",
"transformers",
"emotion",
"license:apache-2.0",
"model-index"
] | text-classification | false | bhadresh-savani | null | bhadresh-savani/roberta-base-emotion | 742 | null | transformers | 1,989 | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
model-index:
- name: bhadresh-savani/roberta-base-emotion
results:
... |
Finnish-NLP/gpt2-large-finnish | d22c61de54f8c5b4f0d37f7684d403ee7dde6a47 | 2022-06-13T16:14:00.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"transformers",
"finnish",
"license:apache-2.0"
] | text-generation | false | Finnish-NLP | null | Finnish-NLP/gpt2-large-finnish | 741 | null | transformers | 1,990 | ---
language:
- fi
license: apache-2.0
tags:
- finnish
- gpt2
datasets:
- Finnish-NLP/mc4_fi_cleaned
- wikipedia
widget:
- text: "Tekstiä tuottava tekoäly on"
---
# GPT-2 large for Finnish
Pretrained GPT-2 large model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
[this... |
pyannote/TestModelForContinuousIntegration | 0bf27df5e3413d6b2f9af2a31a44fd16c50c5929 | 2022-03-23T09:24:42.000Z | [
"pytorch",
"tensorboard",
"pyannote-audio",
"pyannote",
"pyannote-audio-model",
"license:mit"
] | null | false | pyannote | null | pyannote/TestModelForContinuousIntegration | 740 | null | pyannote-audio | 1,991 | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-model
license: mit
inference: false
---
## Dummy model used for continuous integration purposes
```bash
$ pyannote-audio-train protocol=Debug.SpeakerDiarization.Debug \
task=VoiceActivityDetection \
task.duration=2. \... |
UBC-NLP/turjuman | de10623157b685b7096fa744b82203e4b82a6943 | 2022-06-10T00:24:37.000Z | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2206.03933",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | UBC-NLP | null | UBC-NLP/turjuman | 739 | 1 | transformers | 1,992 |
<p align="center">
<br>
<img src="https://github.com/UBC-NLP/turjuman/raw/master//images/turjuman_logo.png"/>
<br>
<p>
<img src="https://github.com/UBC-NLP/turjuman/raw/master/images/turjuman.png" alt="AraT5" width="50%" height="50%" align="right"/>
Turjuman is a neural machine translation tool... |
Salesforce/codet5-large | 7430ce16cc8c0f8db091c561a925047507734575 | 2022-07-07T11:55:19.000Z | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2109.00859",
"arxiv:2207.01780",
"arxiv:1909.09436",
"transformers",
"license:bsd-3-clause",
"autotrain_compatible"
] | text2text-generation | false | Salesforce | null | Salesforce/codet5-large | 739 | 4 | transformers | 1,993 | ---
license: bsd-3-clause
---
# CodeT5 (large-size model 770M)
## Model description
CodeT5 is a family of encoder-decoder language models for code from the paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf) by Yue Wa... |
Helsinki-NLP/opus-mt-tc-big-it-en | ee0004520474d2a0e2b781c841b16cc745086cf4 | 2022-06-01T13:00:06.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"it",
"transformers",
"translation",
"opus-mt-tc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tc-big-it-en | 738 | null | transformers | 1,994 | ---
language:
- en
- it
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-it-en
results:
- task:
name: Translation ita-eng
type: translation
args: ita-eng
dataset:
name: flores101-devtest
type: flores_101
args: ita eng devtest
metrics... |
Sentdex/GPyT | b71d8cd84907e77c1b1d89c11a920244bb52104b | 2021-09-22T09:24:41.000Z | [
"pytorch",
"tf",
"gpt2",
"text-generation",
"Python",
"transformers",
"Code",
"GPyT",
"code generator",
"license:mit"
] | text-generation | false | Sentdex | null | Sentdex/GPyT | 736 | 11 | transformers | 1,995 | ---
language: Python
tags:
- Code
- GPyT
- code generator
license: mit
---
GPyT is a GPT2 model trained from scratch (not fine tuned) on Python code from Github. Overall, it was ~80GB of pure Python code, the current GPyT model is a mere 2 epochs through this data, so it may benefit greatly from continued training an... |
geckos/bart-fined-tuned-on-entailment-classification | fb58ce512c60b04ab539b179eb00c49b86d78b46 | 2021-11-11T13:38:34.000Z | [
"pytorch",
"bart",
"text-classification",
"transformers"
] | text-classification | false | geckos | null | geckos/bart-fined-tuned-on-entailment-classification | 736 | 1 | transformers | 1,996 | Entry not found |
Elron/bleurt-tiny-512 | ed346ce5b758ba2ffbdfa95f7eec221449448523 | 2022-02-10T11:10:25.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Elron | null | Elron/bleurt-tiny-512 | 735 | 2 | transformers | 1,997 | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... |
alaggung/bart-r3f | b0a1bafbf3396749e35b5b48bf9891d34f089f57 | 2022-01-11T16:18:32.000Z | [
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | alaggung | null | alaggung/bart-r3f | 735 | 1 | transformers | 1,998 | ---
language:
- ko
tags:
- summarization
widget:
- text: "[BOS]밥 ㄱ?[SEP]고고고고 뭐 먹을까?[SEP]어제 김치찌개 먹어서 한식말고 딴 거[SEP]그럼 돈까스 어때?[SEP]오 좋다 1시 학관 앞으로 오셈[SEP]ㅇㅋ[EOS]"
inference:
parameters:
max_length: 64
top_k: 5
---
# BART R3F
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
[bart-pretr... |
patrickvonplaten/wav2vec2-base | 822b936a126a5486547c640c97eda5c61bfedfdc | 2021-06-08T17:00:26.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"transformers",
"speech",
"license:apache-2.0"
] | null | false | patrickvonplaten | null | patrickvonplaten/wav2vec2-base | 735 | null | transformers | 1,999 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Base
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech... |
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