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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bigscience/T0 | 37f8b7565a0c9945db6a0215b0b823a55e337f4f | 2022-06-21T01:25:09.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | bigscience | null | bigscience/T0 | 18,430 | 23 | transformers | 500 | ---
datasets:
- bigscience/P3
language: en
license: apache-2.0
widget:
- text: "A is the son's of B's uncle. What is the family relationship between A and B?"
- text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."
- text: "Task: copy but say the opposite.\n
PSG won its match again... |
facebook/opt-30b | 463007d7da4e87fe962909a027811a8c0b32ede8 | 2022-06-23T16:42:12.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-30b | 18,428 | 71 | transformers | 501 | ---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://g... |
microsoft/deberta-v3-xsmall | a4c41b18332abc2f8df65cd0528883373cf09923 | 2022-01-13T18:28:06.000Z | [
"pytorch",
"tf",
"deberta-v2",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"deberta",
"deberta-v3",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-v3-xsmall | 18,409 | 10 | transformers | 502 | ---
language: en
tags:
- deberta
- deberta-v3
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa m... |
ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli | 5b605abab9b75bc87ab66cfc049ef58d9d64b8ed | 2021-05-20T23:17:23.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"transformers",
"license:mit"
] | text-classification | false | ynie | null | ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli | 18,330 | 3 | transformers | 503 | ---
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
license: mit
---
This is a strong pre-trained RoBERTa-Large NLI model.
The training data is a combination of well-known NLI datasets: [`SNLI`](https://nlp.stanford.edu/projects/snli/), [`MNLI`](https://cims.nyu.edu/~sbowman/multinli/), [`FEVER-NLI`... |
sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english | 1dfc430017617e79fd19a2b60b30d6b217f64d28 | 2021-05-20T07:12:23.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | sshleifer | null | sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english | 18,202 | null | transformers | 504 | Entry not found |
savasy/bert-base-turkish-sentiment-cased | 330ec37b18140dcd5c5dd6357d59463ae9deb2e0 | 2021-05-20T04:55:01.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"tr",
"transformers"
] | text-classification | false | savasy | null | savasy/bert-base-turkish-sentiment-cased | 18,114 | 5 | transformers | 505 | ---
language: tr
---
# Bert-base Turkish Sentiment Model
https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
This model is used for Sentiment Analysis, which is based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased
## Dataset
The dataset is taken from the studies [[... |
megagonlabs/transformers-ud-japanese-electra-base-ginza-510 | 8498b019a97bdf693fa138efcf7e118f099e2c4e | 2021-12-05T12:12:12.000Z | [
"pytorch",
"electra",
"feature-extraction",
"ja",
"dataset:mC4",
"dataset:UD_Japanese_BCCWJ r2.8",
"dataset:GSK2014-A(2019)",
"arxiv:1910.10683",
"transformers",
"PyTorch",
"Transformers",
"spaCy",
"ELECTRA",
"GiNZA",
"mC4",
"UD_Japanese-BCCWJ",
"GSK2014-A",
"MIT",
"license:mit"
... | feature-extraction | false | megagonlabs | null | megagonlabs/transformers-ud-japanese-electra-base-ginza-510 | 18,065 | null | transformers | 506 | ---
language:
- ja
thumbnail: "https://raw.githubusercontent.com/megagonlabs/ginza/static/docs/images/GiNZA_logo_4c_s.png"
tags:
- PyTorch
- Transformers
- spaCy
- ELECTRA
- GiNZA
- mC4
- UD_Japanese-BCCWJ
- GSK2014-A
- ja
- MIT
license: "mit"
datasets:
- mC4
- UD_Japanese_BCCWJ r2.8
- GSK2014-A(2019)
metrics:
- UAS... |
fnlp/bart-base-chinese | cac82280f9cafd2e7bb76fc32123717e335397ba | 2021-10-31T15:05:30.000Z | [
"pytorch",
"bart",
"feature-extraction",
"zh",
"arxiv:2109.05729",
"transformers",
"text2text-generation",
"Chinese",
"seq2seq",
"BART"
] | feature-extraction | false | fnlp | null | fnlp/bart-base-chinese | 18,058 | 16 | transformers | 507 | ---
tags:
- text2text-generation
- Chinese
- seq2seq
- BART
language: zh
---
# Chinese BART-Base
## Model description
This is an implementation of Chinese BART-Base.
[**CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation**](https://arxiv.org/pdf/2109.05729.pdf)
Yunfan S... |
kssteven/ibert-roberta-base | 4f98e9110b04a8958444d3af8ed39287834fbb90 | 2021-11-22T10:09:32.000Z | [
"pytorch",
"ibert",
"fill-mask",
"arxiv:1907.11692",
"arxiv:2101.01321",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kssteven | null | kssteven/ibert-roberta-base | 17,996 | null | transformers | 508 | # I-BERT base model
This model, `ibert-roberta-base`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this paper](https://arxiv.org/abs/2101.01321).
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only... |
facebook/deit-small-distilled-patch16-224 | 3559f915da8724f0eba2252acb20cc96649c6289 | 2022-07-13T11:41:21.000Z | [
"pytorch",
"tf",
"deit",
"image-classification",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/deit-small-distilled-patch16-224 | 17,980 | null | transformers | 509 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
---
# Distilled Data-efficient Image Transformer (small-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was firs... |
hf-internal-testing/processor_with_lm | 5477bdaf3c221237d0859ebcd6c6aa49e4a7d804 | 2022-01-18T13:19:58.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | hf-internal-testing | null | hf-internal-testing/processor_with_lm | 17,977 | null | transformers | 510 | Please leave this README for testing purposes |
nlp-waseda/roberta-base-japanese | 87e4c4bb0e741b81e03db376c92f0af288f3be49 | 2022-06-10T23:32:53.000Z | [
"pytorch",
"roberta",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | nlp-waseda | null | nlp-waseda/roberta-base-japanese | 17,807 | 10 | transformers | 511 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "早稲田 大学 で 自然 言語 処理 を [MASK] する 。"
---
# nlp-waseda/roberta-base-japanese
## Model description
This is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How to... |
KoboldAI/GPT-Neo-2.7B-Shinen | 551fbd85138d4f29589ab07000cca813cb8a62ea | 2022-03-20T18:49:18.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-Neo-2.7B-Shinen | 17,802 | 2 | transformers | 512 | ---
language: en
license: mit
---
# GPT-Neo 2.7B - Shinen
## Model Description
GPT-Neo 2.7B-Shinen is a finetune created using EleutherAI's GPT-Neo 2.7B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
**Warning: THIS model is NOT suitable for use by minors. The model will output... |
dbmdz/bert-base-german-cased | 56c3dce79f5d93e466f3b800d8e57cddfe13a6d4 | 2021-05-19T14:52:56.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-german-cased | 17,797 | 5 | transformers | 513 | ---
language: de
license: mit
---
# 🤗 + 📚 dbmdz German BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models 🎉
# German BERT
## Stats
In addition to the recently released [German BERT](https://deepset.ai/german-bert)
model by [d... |
sshleifer/bart-tiny-random | 69bce9237e4fa10ea015446395ec0108067890cf | 2021-06-14T07:44:43.000Z | [
"pytorch",
"tf",
"jax",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/bart-tiny-random | 17,763 | null | transformers | 514 | Entry not found |
microsoft/trocr-base-printed | 191a64dd5078db39e975a76b798b6fd026a96fa6 | 2022-07-01T07:35:28.000Z | [
"pytorch",
"vision-encoder-decoder",
"arxiv:2109.10282",
"transformers",
"trocr",
"image-to-text"
] | image-to-text | false | microsoft | null | microsoft/trocr-base-printed | 17,758 | 12 | transformers | 515 | ---
tags:
- trocr
- image-to-text
---
# TrOCR (base-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the [SROIE dataset](https://rrc.cvc.uab.es/?ch=13). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li e... |
uer/gpt2-chinese-poem | 01a2a7e8de2df28f48dfa4262b759dd5858b84bd | 2022-02-20T05:00:26.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"transformers"
] | text-generation | false | uer | null | uer/gpt2-chinese-poem | 17,737 | 4 | transformers | 516 | ---
language: zh
widget:
- text: "[CLS] 万 叠 春 山 积 雨 晴 ，"
- text: "[CLS] 大 漠"
---
# Chinese Poem GPT2 Model
## Model description
The model is used to generate Chinese ancient poems. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFac... |
google/t5-large-lm-adapt | 96ce18564557a62d6ff1cb3771af167433827961 | 2021-11-01T14:00:11.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"transformers",
"t5-lm-adapt",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-large-lm-adapt | 17,713 | 3 | transformers | 517 | ---
language: en
datasets:
- c4
tags:
- t5-lm-adapt
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transform... |
textattack/roberta-base-SST-2 | a029a4679e8a56a958d932d1132d6a4f68803214 | 2021-05-20T22:11:39.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-SST-2 | 17,616 | null | transformers | 518 | Entry not found |
EleutherAI/gpt-neox-20b | 364ae95407723fadd1d47b023c1efb92a4d891c3 | 2022-04-07T22:14:56.000Z | [
"pytorch",
"gpt_neox",
"text-generation",
"transformers"
] | text-generation | false | EleutherAI | null | EleutherAI/gpt-neox-20b | 17,557 | 41 | transformers | 519 | Entry not found |
facebook/rag-sequence-nq | c0d9c6ceda8a69c78091abb7aa734a97b75b89fd | 2021-03-12T11:04:28.000Z | [
"pytorch",
"tf",
"rag",
"en",
"dataset:wiki_dpr",
"arxiv:2005.11401",
"transformers",
"license:apache-2.0"
] | null | false | facebook | null | facebook/rag-sequence-nq | 17,541 | 1 | transformers | 520 | ---
language: en
license: apache-2.0
datasets:
- wiki_dpr
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
## RAG
This is the RAG-Sequence Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patrick Lewis, Ethan Perez, A... |
sentence-transformers/bert-base-nli-stsb-mean-tokens | 1bd90bb33d5c6601f5fbd26d91e955a65059ee55 | 2022-06-15T20:01:00.000Z | [
"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/bert-base-nli-stsb-mean-tokens | 17,507 | 1 | sentence-transformers | 521 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
**⚠️ 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 ... |
Helsinki-NLP/opus-mt-da-en | 8971eb3839ec41bddd060128b9b83038bb43fd96 | 2021-09-09T21:29:52.000Z | [
"pytorch",
"marian",
"text2text-generation",
"da",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-da-en | 17,464 | 2 | transformers | 522 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-da-en
* source languages: da
* target languages: en
* OPUS readme: [da-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/da-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
jhgan/ko-sroberta-multitask | ab957ae6a91e99c4cad36d52063a2a9cf1bf4419 | 2022-06-13T16:34:48.000Z | [
"pytorch",
"tf",
"roberta",
"feature-extraction",
"ko",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | jhgan | null | jhgan/ko-sroberta-multitask | 17,438 | 4 | sentence-transformers | 523 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language: ko
---
# ko-sroberta-multitask
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used ... |
facebook/wav2vec2-large-xlsr-53 | c3f9d884181a224a6ac87bf8885c84d1cff3384f | 2022-03-18T16:11:44.000Z | [
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"multilingual",
"dataset:common_voice",
"arxiv:2006.13979",
"transformers",
"speech",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-large-xlsr-53 | 17,356 | 26 | transformers | 524 | ---
language: multilingual
datasets:
- common_voice
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-XLSR-53
[Facebook's XLSR-Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure t... |
sentence-transformers/stsb-xlm-r-multilingual | bc1a68705f2e397259207e96349a36ccbc7e6493 | 2022-06-15T21:42:42.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/stsb-xlm-r-multilingual | 17,252 | 3 | sentence-transformers | 525 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/stsb-xlm-r-multilingual
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dens... |
huggingtweets/anal_sex42069 | c6a452118cfc25d59bbedcf918acba47df3ee243 | 2021-05-21T18:49:44.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/anal_sex42069 | 17,247 | null | transformers | 526 | ---
language: en
thumbnail: https://www.huggingtweets.com/anal_sex42069/1617757256637/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/13759311308... |
google/tapas-base | 00456266840bb0a319cd6748ebf7da3caf98816b | 2021-11-29T10:03:33.000Z | [
"pytorch",
"tf",
"tapas",
"feature-extraction",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"transformers",
"TapasModel",
"license:apache-2.0"
] | feature-extraction | false | google | null | google/tapas-base | 17,168 | 3 | transformers | 527 | ---
language: en
tags:
- tapas
- TapasModel
license: apache-2.0
---
# TAPAS base model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas... |
vicgalle/xlm-roberta-large-xnli-anli | 81de27372786f6a034c81c4bf1c53ebe9afa10d7 | 2021-03-04T17:05:03.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"multilingual",
"dataset:mnli",
"dataset:xnli",
"dataset:anli",
"transformers",
"zero-shot-classification",
"nli",
"license:mit"
] | zero-shot-classification | false | vicgalle | null | vicgalle/xlm-roberta-large-xnli-anli | 17,093 | 8 | transformers | 528 | ---
language: multilingual
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- mnli
- xnli
- anli
license: mit
pipeline_tag: zero-shot-classification
widget:
- text: "De pugna erat fantastic. Nam Crixo decem quam dilexit et praeciderunt caput aemulus."
candidate_labels: "violent, peaceful"
- text: "La pelícu... |
lidiya/bart-large-xsum-samsum | 5f600a362ff4b9efbaf7e3cbbdd853a3a89c118f | 2022-07-20T14:55:59.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"seq2seq",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | lidiya | null | lidiya/bart-large-xsum-samsum | 17,031 | 8 | transformers | 529 | ---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: |
Hannah: Hey, do you have Betty's number?
Amanda: Lemme check
Amanda: Sorry, can't find it.
Amanda: Ask Larry
Amanda: He called her last time we were at the park together
Hannah: I do... |
xlm-clm-ende-1024 | bfe86064e0ce0060985a2a47637cb2efa6631a8d | 2022-07-22T08:04:36.000Z | [
"pytorch",
"tf",
"xlm",
"fill-mask",
"multilingual",
"en",
"de",
"arxiv:1901.07291",
"arxiv:1910.09700",
"transformers",
"autotrain_compatible"
] | fill-mask | false | null | null | xlm-clm-ende-1024 | 16,967 | null | transformers | 530 | ---
language:
- multilingual
- en
- de
---
# xlm-clm-ende-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical... |
google/bigbird-pegasus-large-arxiv | 9d87686a36f9c732db7deeae1dcac5fb085d0b90 | 2022-06-29T20:40:12.000Z | [
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"en",
"dataset:scientific_papers",
"arxiv:2007.14062",
"transformers",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | google | null | google/bigbird-pegasus-large-arxiv | 16,870 | 8 | transformers | 531 | ---
language: en
license: apache-2.0
datasets:
- scientific_papers
tags:
- summarization
model-index:
- name: google/bigbird-pegasus-large-arxiv
results:
- task:
type: summarization
name: Summarization
dataset:
name: scientific_papers
type: scientific_papers
config: pubmed
sp... |
facebook/muppet-roberta-base | caf238c63db946bdfbd00575713462838e823997 | 2021-06-28T21:44:23.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2101.11038",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | facebook | null | facebook/muppet-roberta-base | 16,709 | 2 | transformers | 532 | ---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# Muppet: Massive Multi-task Representations with Pre-Finetuning
# RoBERTa base model
This is a Massive Multi-task Pre-finetuned version of Roberta base. It was introduced in
[this paper](https://arxiv.org/abs/2101.11038). The model i... |
KB/bert-base-swedish-cased | 81c7baa04742a30cb6732c181e678721868cb42e | 2022-06-07T16:31:14.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"sv",
"transformers",
"autotrain_compatible"
] | fill-mask | false | KB | null | KB/bert-base-swedish-cased | 16,702 | 1 | transformers | 533 | ---
language: sv
---
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and ... |
facebook/mbart-large-50-many-to-one-mmt | aadfc3b9db11b773f823ad936d5d683c470e7683 | 2022-05-26T22:28:02.000Z | [
"pytorch",
"jax",
"mbart",
"text2text-generation",
"multilingual",
"ar",
"cs",
"de",
"en",
"es",
"et",
"fi",
"fr",
"gu",
"hi",
"it",
"ja",
"kk",
"ko",
"lt",
"lv",
"my",
"ne",
"nl",
"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"af",
"az",
"bn",
"fa",
"he",
... | text2text-generation | false | facebook | null | facebook/mbart-large-50-many-to-one-mmt | 16,684 | 2 | transformers | 534 | ---
language:
- multilingual
- ar
- cs
- de
- en
- es
- et
- fi
- fr
- gu
- hi
- it
- ja
- kk
- ko
- lt
- lv
- my
- ne
- nl
- ro
- ru
- si
- tr
- vi
- zh
- af
- az
- bn
- fa
- he
- hr
- id
- ka
- km
- mk
- ml
- mn
- mr
- pl
- ps
- pt
- sv
- sw
- ta
- te
- th
- tl
- uk
- ur
- xh
- gl
- sl
tags:
- mbart-50
---
# mBART-... |
sentence-transformers/stsb-roberta-base | 20afd582a7bbc12014f37486dbef9b0c990f91bd | 2022-06-15T20:36:10.000Z | [
"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/stsb-roberta-base | 16,676 | null | sentence-transformers | 535 | ---
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... |
facebook/opt-6.7b | 8dc17cdd7b9381612e631064e569f4142d776d88 | 2022-06-24T05:22:09.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-6.7b | 16,669 | 7 | transformers | 536 | ---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://g... |
SEBIS/legal_t5_small_trans_it_en | 94eb68e173197648813e7d2fe7cd40ad91989f14 | 2021-06-23T10:00:46.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"Italian English",
"dataset:dcep europarl jrc-acquis",
"transformers",
"translation Italian English model",
"autotrain_compatible"
] | text2text-generation | false | SEBIS | null | SEBIS/legal_t5_small_trans_it_en | 16,578 | null | transformers | 537 |
---
language: Italian English
tags:
- translation Italian English model
datasets:
- dcep europarl jrc-acquis
widget:
- text: "Oggetto: Libertà di culto in Turchia"
---
# legal_t5_small_trans_it_en model
Model on translating legal text from Italian to English. It was first released in
[this repository](https://gi... |
pdelobelle/robbert-v2-dutch-base | e28720e1a6cdf68ed3418c67a1964392905a7c8a | 2022-05-19T20:45:14.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2001.06286",
"arxiv:2004.02814",
"arxiv:2010.13652",
"arxiv:2101.05716",
"arxiv:1907.11692",
"arxiv:... | fill-mask | false | pdelobelle | null | pdelobelle/robbert-v2-dutch-base | 16,556 | 8 | transformers | 538 | ---
language: "nl"
thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
license: mit
datasets:
- oscar
- oscar (NL)
- dbrd
- lassy-ud
- europarl-mono
- conll2002
widget:
- text: "Hallo, ik ben RobBERT, een <mask> taalmodel van de KU Leuven."
---
<p... |
sentence-transformers/bert-large-nli-stsb-mean-tokens | ab23972c686d191c5a1915b71cf453e20647cff1 | 2022-06-15T22:48:23.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/bert-large-nli-stsb-mean-tokens | 16,463 | null | sentence-transformers | 539 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
**⚠️ 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... |
facebook/opt-2.7b | c9c15109b9dac40871c063892227d45b85cb3952 | 2022-06-22T09:54:30.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-2.7b | 16,441 | 5 | transformers | 540 | ---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://g... |
DeepPavlov/distilrubert-tiny-cased-conversational | a70de709fe33d6879bd82337162bdd2ea19442bd | 2022-06-28T17:10:33.000Z | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | false | DeepPavlov | null | DeepPavlov/distilrubert-tiny-cased-conversational | 16,195 | 1 | transformers | 541 | ---
language:
- ru
---
WARNING: This is `distilrubert-small-cased-conversational` model uploaded with wrong name. This one is the same as [distilrubert-small-cased-conversational](https://huggingface.co/DeepPavlov/distilrubert-small-cased-conversational). `distilrubert-tiny-cased-conversational` could be found in [dis... |
pin/senda | 0ad8d1953f6a3a27a432a20b957d7e1129cdcbbc | 2021-08-20T11:00:39.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"da",
"transformers",
"danish",
"sentiment",
"polarity",
"license:cc-by-4.0"
] | text-classification | false | pin | null | pin/senda | 16,186 | 2 | transformers | 542 | ---
language: da
tags:
- danish
- bert
- sentiment
- polarity
license: cc-by-4.0
widget:
- text: "Sikke en dejlig dag det er i dag"
---
# Danish BERT fine-tuned for Sentiment Analysis with `senda`
This model detects polarity ('positive', 'neutral', 'negative') of Danish texts.
It is trained and tested on Tweets anno... |
sentence-transformers/multi-qa-distilbert-cos-v1 | 4ee499ef6882f9c48c82085c3ead10ed8ac6be28 | 2022-07-11T21:07:27.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/QQP",
"dataset:embed... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/multi-qa-distilbert-cos-v1 | 16,171 | 5 | sentence-transformers | 543 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- search_qa
- eli5
- natural_questions
- trivia_qa
- embedding-data/QQP
- embedding-data/PAQ_pairs
- embedding-d... |
bigscience/bloom-350m | ef09465f3d95a3fda91faf0d814b95fd3521b73d | 2022-07-21T08:04:09.000Z | [
"pytorch",
"jax",
"bloom",
"feature-extraction",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
... | text-generation | false | bigscience | null | bigscience/bloom-350m | 16,140 | 5 | transformers | 544 | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-gener... |
textattack/bert-base-uncased-yelp-polarity | a4d0a85ea6c1d5bb944dcc12ea5c918863e469a4 | 2021-05-20T07:49:07.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-yelp-polarity | 16,133 | null | transformers | 545 | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this ... |
KB/bert-base-swedish-cased-ner | e1c9ae76afa22ce28d2097310ab95312d73e4e3a | 2022-06-07T16:34:49.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"sv",
"transformers",
"autotrain_compatible"
] | token-classification | false | KB | null | KB/bert-base-swedish-cased-ner | 16,095 | null | transformers | 546 | ---
language: sv
---
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and... |
facebook/wav2vec2-large-xlsr-53-french | e7c54e9cde9f6d84be783c7d04d34e1c8efcc1d1 | 2021-07-06T02:40:56.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:common_voice",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-xlsr-53-french | 15,879 | 6 | transformers | 547 | ---
language: fr
datasets:
- common_voice
tags:
- speech
- audio
- automatic-speech-recognition
license: apache-2.0
---
## Evaluation on Common Voice FR Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
... |
yarongef/DistilProtBert | 9e3f170d21d907fe7d1370360f1993c710777bf2 | 2022-06-14T12:39:15.000Z | [
"pytorch",
"bert",
"fill-mask",
"protein",
"dataset:Uniref50",
"transformers",
"protein language model",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | yarongef | null | yarongef/DistilProtBert | 15,770 | 1 | transformers | 548 | ---
license: mit
language: protein
tags:
- protein language model
datasets:
- Uniref50
---
# DistilProtBert
A distilled version of [ProtBert-UniRef100](https://huggingface.co/Rostlab/prot_bert) model.
In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language mo... |
sentence-transformers/paraphrase-albert-small-v2 | b8a76dca618575852d8874313e2ad84d423f333f | 2022-07-08T04:07:04.000Z | [
"pytorch",
"tf",
"rust",
"albert",
"feature-extraction",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:s2orc",
"dataset:ms_marco",
"dataset:wiki_atomic_edits",
"dataset:snli",
"dataset:multi_nli",
"dataset:embedding-data/altlex",
"dataset:embedding-data/simple-wiki",
"data... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-albert-small-v2 | 15,743 | 1 | sentence-transformers | 549 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- flax-sentence-embeddings/stackexchange_xml
- s2orc
- ms_marco
- wiki_atomic_edits
- snli
- multi_nli
- embedding-data/altlex
- embedding-data/simple-wiki
- embedd... |
bigscience/bloom-1b3 | 889e0ff2dc58b977bbd4b22738ff110be5b4e400 | 2022-07-13T09:02:56.000Z | [
"pytorch",
"jax",
"bloom",
"feature-extraction",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
... | text-generation | false | bigscience | null | bigscience/bloom-1b3 | 15,738 | 37 | transformers | 550 | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-gener... |
ctrl | e3789f31af06f7dfbc086b59c562557a1d86d33b | 2022-07-22T08:04:22.000Z | [
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause"
] | null | false | null | null | ctrl | 15,719 | null | transformers | 551 | ---
language: en
license: bsd-3-clause
---
# ctrl
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications... |
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | 2033d0d1de807e8181ebfa0e53d2a8e526412b0f | 2022-05-06T11:57:05.000Z | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | false | DeepPavlov | null | DeepPavlov/distilrubert-tiny-cased-conversational-v1 | 15,712 | 1 | transformers | 552 | ---
language:
- ru
---
# distilrubert-tiny-cased-conversational
Conversational DistilRuBERT-tiny \(Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversatio... |
KBLab/bert-base-swedish-cased | 087b126005a1eee27f85e9665401161dabb4665d | 2022-07-28T14:11:35.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"sv",
"transformers",
"autotrain_compatible"
] | fill-mask | false | KBLab | null | KBLab/bert-base-swedish-cased | 15,649 | 4 | transformers | 553 | ---
language: sv
---
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on aproximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and ... |
iarfmoose/bert-base-cased-qa-evaluator | edfb0e29d78453325a95d9a61d4d26d3598e402b | 2021-05-19T20:15:52.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | iarfmoose | null | iarfmoose/bert-base-cased-qa-evaluator | 15,628 | 4 | transformers | 554 | # BERT-base-cased-qa-evaluator
This model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained [BERT-base-cased](https://huggingface.co/bert-base-cased) with a sequence classification head... |
aubmindlab/bert-base-arabertv2 | 599b85458968e0cbad56126802f8328e649b3bec | 2022-04-06T15:22:30.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"arxiv:2003.00104",
"transformers",
"autotrain_compatible"
] | fill-mask | false | aubmindlab | null | aubmindlab/bert-base-arabertv2 | 15,620 | 1 | transformers | 555 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
widget:
- text: " عاصم +ة لبنان هي [MASK] ."
---
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png"... |
moussaKam/mbarthez | 303813ade47c885979189a169c1449669fdc546f | 2021-11-15T13:01:46.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"fr",
"arxiv:2010.12321",
"transformers",
"summarization",
"license:apache-2.0",
"fill-mask",
"autotrain_compatible"
] | fill-mask | false | moussaKam | null | moussaKam/mbarthez | 15,549 | 4 | transformers | 556 | ---
tags:
- summarization
language:
- fr
license: apache-2.0
pipeline_tag: "fill-mask"
---
A french sequence to sequence pretrained model based on [BART](https://huggingface.co/facebook/bart-large). <br>
BARThez is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 66GB of french raw text ... |
transfo-xl-wt103 | 62696ec9dfce23d1930af723c40fd921eb5b1255 | 2022-07-22T08:06:44.000Z | [
"pytorch",
"tf",
"transfo-xl",
"text-generation",
"en",
"dataset:wikitext-103",
"arxiv:1901.02860",
"transformers",
"model-index"
] | text-generation | false | null | null | transfo-xl-wt103 | 15,538 | 3 | transformers | 557 | ---
datasets:
- wikitext-103
tags:
- text-generation
language: en
model-index:
- name: transfo-xl-wt103
results: []
task:
name: Text Generation
type: text-generation
---
# Transfo-xl-wt103
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#ris... |
sentence-transformers/stsb-roberta-large | b6cd86898ba049a6d160dab42e298f677c5e63b6 | 2022-06-15T20:28:37.000Z | [
"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/stsb-roberta-large | 15,533 | 1 | sentence-transformers | 558 | ---
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... |
jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli | 33e5835aad1c32ac8707971141b65c3fc5ff1904 | 2022-02-25T19:07:57.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | jbetker | null | jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli | 15,508 | 2 | transformers | 559 | This checkpoint is a wav2vec2-large model that is useful for generating transcriptions with punctuation. It is intended for use in building transcriptions for TTS models, where punctuation is very important for prosody.
This model was created by fine-tuning the `facebook/wav2vec2-large-robust-ft-libri-960h` checkpoint... |
sentence-transformers/paraphrase-MiniLM-L12-v2 | 8f010f24d5c0e1ee9735f056d024fcda6557f70f | 2022-06-15T20:18:46.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-MiniLM-L12-v2 | 15,505 | 1 | sentence-transformers | 560 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional den... |
textattack/bert-base-uncased-SST-2 | 95f0f6f859b35c8ff0863ae3cd4e2dbc702c0ae2 | 2021-05-20T07:37:12.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-SST-2 | 15,351 | null | transformers | 561 | Entry not found |
sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1 | bf5baa9f012675785f0ffb2b509d99ca1b49fcde | 2022-06-15T22:09:18.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1 | 15,282 | null | sentence-transformers | 562 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 d... |
m3rg-iitd/matscibert | 24a4e4318dda9bc18bff5e6a45debdcb3e1780e3 | 2022-06-02T19:07:10.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | m3rg-iitd | null | m3rg-iitd/matscibert | 15,267 | 4 | transformers | 563 | # MatSciBERT
## A Materials Domain Language Model for Text Mining and Information Extraction
This is the pretrained model presented in [MatSciBERT: A materials domain language model for text mining and information extraction](https://rdcu.be/cMAp5), which is a BERT model trained on material science research papers.
T... |
pierreguillou/bert-large-cased-squad-v1.1-portuguese | e09c341e0255ed5120a4857db94e3209699353b6 | 2022-01-04T09:57:00.000Z | [
"pytorch",
"tf",
"bert",
"question-answering",
"pt",
"dataset:brWaC",
"dataset:squad",
"dataset:squad_v1_pt",
"transformers",
"bert-large",
"license:mit",
"autotrain_compatible"
] | question-answering | false | pierreguillou | null | pierreguillou/bert-large-cased-squad-v1.1-portuguese | 15,248 | 12 | transformers | 564 | ---
language: pt
license: mit
tags:
- question-answering
- bert
- bert-large
- pytorch
datasets:
- brWaC
- squad
- squad_v1_pt
metrics:
- squad
widget:
- text: "Quando começou a pandemia de Covid-19 no mundo?"
context: "A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de ... |
openclimatefix/nowcasting_cnn_v2 | 5b9c2a9194869afae91651ffdf2ba1de1952d866 | 2022-05-25T10:41:01.000Z | [
"pytorch",
"transformers",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit"
] | null | false | openclimatefix | null | openclimatefix/nowcasting_cnn_v2 | 15,138 | null | transformers | 565 | ---
license: mit
tags:
- nowcasting
- forecasting
- timeseries
- remote-sensing
---
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes i... |
sentence-transformers/stsb-distilbert-base | 815bb3e4dbcba340b5f8c0c0489800230880e06e | 2022-06-15T19:43:41.000Z | [
"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/stsb-distilbert-base | 15,046 | 3 | sentence-transformers | 566 | ---
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... |
mrm8488/bert-tiny-finetuned-sms-spam-detection | 49541ae4a47b52d4b2e6d5a3a1875c5e35aebeb1 | 2021-05-20T00:40:14.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"dataset:sms_spam",
"transformers",
"sms",
"spam",
"detection"
] | text-classification | false | mrm8488 | null | mrm8488/bert-tiny-finetuned-sms-spam-detection | 14,989 | 5 | transformers | 567 | ---
language: en
tags:
- sms
- spam
- detection
datasets:
- sms_spam
widget:
- text: "Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days."
---
# BERT-Tiny fine-tuned on on sms_spam dataset for spam detection
Validation accuray: **0.98** |
sberbank-ai/rugpt3large_based_on_gpt2 | aa2b602c1939938541eed9283347d6e08536f6f8 | 2021-09-21T19:33:09.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"ru",
"transformers",
"PyTorch",
"Transformers"
] | text-generation | false | sberbank-ai | null | sberbank-ai/rugpt3large_based_on_gpt2 | 14,965 | 10 | transformers | 568 | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/ru-gpts"
---
# rugpt3large\_based\_on\_gpt2
Model was trained with sequence length 1024 using transformers lib by [SberDevices](https://sberdevices.ru/) team on 80B tokens for 3 epochs. After that model was finetuned 1 epoch w... |
pysentimiento/robertuito-emotion-analysis | 2a1fb82f525912c23a8187eeea418751049d5056 | 2021-12-10T13:30:24.000Z | [
"pytorch",
"roberta",
"text-classification",
"es",
"arxiv:2106.09462",
"arxiv:2111.09453",
"transformers",
"emotion-analysis",
"twitter"
] | text-classification | false | pysentimiento | null | pysentimiento/robertuito-emotion-analysis | 14,906 | 4 | transformers | 569 | ---
language:
- es
tags:
- emotion-analysis
- twitter
---
# Emotion Analysis in Spanish
## robertuito-emotion-analysis
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish... |
mrm8488/bert-tiny-finetuned-squadv2 | 178467c709f17b74c9bf05ed6d41cb5fa2cf684c | 2022-07-15T09:46:32.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1908.08962",
"transformers",
"QA",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-tiny-finetuned-squadv2 | 14,890 | 1 | transformers | 570 | ---
language: en
thumbnail:
tags:
- QA
---
# BERT-Tiny fine-tuned on SQuAD v2
[BERT-Tiny](https://github.com/google-research/bert/) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
**Mode size** (... |
valhalla/t5-base-qa-qg-hl | 0286be61d8d9de5650fdd21ed8923a7bc226e704 | 2020-12-11T22:03:44.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:squad",
"arxiv:1910.10683",
"transformers",
"question-generation",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/t5-base-qa-qg-hl | 14,782 | 2 | transformers | 571 | ---
datasets:
- squad
tags:
- question-generation
widget:
- text: "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>"
- text: "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>"
license: mit
---
## T5 for multi-task QA and QG
This is multi-... |
Helsinki-NLP/opus-mt-et-en | 3649f4761e2a1b25f2c19e3f1732c6ba9ef61519 | 2021-09-09T21:46:01.000Z | [
"pytorch",
"marian",
"text2text-generation",
"et",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-et-en | 14,726 | null | transformers | 572 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-et-en
* source languages: et
* target languages: en
* OPUS readme: [et-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/et-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
mrm8488/t5-base-finetuned-emotion | e44a316825f11230724b36412fbf1899c76e82de | 2021-06-23T12:46:24.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:emotion",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-emotion | 14,718 | 9 | transformers | 573 | ---
language: en
datasets:
- emotion
widget:
- text: "I wish you were here but it is impossible"
---
# T5-base fine-tuned for Emotion Recognition 😂😢😡😃😯
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [emotion recognition](https://github.com/dair-ai/em... |
iarfmoose/t5-base-question-generator | 1bfc9d4b2b0078e0b65cf40c6e2e2e974fbab6b0 | 2022-02-24T08:41:19.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | iarfmoose | null | iarfmoose/t5-base-question-generator | 14,640 | 17 | transformers | 574 | # Model name
## Model description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained `t5-base` model.
## Intended uses & limitations
The model is trained to generate reading comprehension-style que... |
kykim/bert-kor-base | 1779cc0982ada0216dd6de0dd4e86fb78201926d | 2021-05-19T21:17:13.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ko",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kykim | null | kykim/bert-kor-base | 14,617 | 3 | transformers | 575 | ---
language: ko
---
# Bert 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 BertTokenizerFast, BertModel
tokenizer_bert = ... |
facebook/wmt19-ru-en | d145f3063a3e25e43c04c9aa64de38999b3fb2cd | 2020-12-11T21:40:01.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"ru",
"en",
"dataset:wmt19",
"arxiv:1907.06616",
"transformers",
"translation",
"wmt19",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | facebook | null | facebook/wmt19-ru-en | 14,556 | 3 | transformers | 576 | ---
language:
- ru
- en
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/... |
microsoft/dit-base-finetuned-rvlcdip | 48a4c1ad3e7ea1dfb87e8745eba7372162cba7f5 | 2022-07-21T12:20:05.000Z | [
"pytorch",
"beit",
"image-classification",
"dataset:rvl_cdip",
"arxiv:2203.02378",
"transformers",
"dit",
"vision"
] | image-classification | false | microsoft | null | microsoft/dit-base-finetuned-rvlcdip | 14,504 | 7 | transformers | 577 | ---
tags:
- dit
- vision
- image-classification
datasets:
- rvl_cdip
widget:
- src: https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip/resolve/main/coca_cola_advertisement.png
example_title: Advertisement
- src: https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip/resolve/main/scientific_publication.p... |
deepset/tinyroberta-squad2 | 9f008c54f533ffdf142f127c0c5c9bfe23542aaa | 2022-07-25T11:44:05.000Z | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:1909.10351",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/tinyroberta-squad2 | 14,451 | 8 | transformers | 578 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/tinyroberta-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- name: Exact... |
Recognai/bert-base-spanish-wwm-cased-xnli | 6219d7e4cc59999010c795ac26d2e014102e24ba | 2021-10-15T15:55:15.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"es",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"nli",
"license:mit"
] | zero-shot-classification | false | Recognai | null | Recognai/bert-base-spanish-wwm-cased-xnli | 14,415 | 10 | transformers | 579 | ---
language: es
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- xnli
license: mit
pipeline_tag: zero-shot-classification
widget:
- text: "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
candidate_labels: "cultura, sociedad, economia, salud, deportes"
---
# bert-bas... |
cross-encoder/qnli-electra-base | 4d70c22ec2d12ec7663a70fbe3180a408c980a2a | 2021-08-05T08:41:23.000Z | [
"pytorch",
"electra",
"text-classification",
"arxiv:1804.07461",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/qnli-electra-base | 14,334 | null | transformers | 580 | ---
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
Given a question and paragraph, can the question be a... |
klue/roberta-large | 5193b95701189160c45d02a1033a4ea55bdbe259 | 2021-10-20T16:13:45.000Z | [
"pytorch",
"roberta",
"fill-mask",
"ko",
"arxiv:2105.09680",
"transformers",
"korean",
"klue",
"autotrain_compatible"
] | fill-mask | false | klue | null | klue/roberta-large | 14,205 | 14 | transformers | 581 | ---
language: ko
tags:
- korean
- klue
mask_token: "[MASK]"
widget:
- text: 대한민국의 수도는 [MASK] 입니다.
---
# KLUE RoBERTa large
Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
## How to use
_NOTE:_ Use... |
google/reformer-enwik8 | ddc3376ce78ee3917253891e59ecf581247d8a8c | 2021-06-03T13:02:12.000Z | [
"pytorch",
"reformer",
"text-generation",
"transformers"
] | text-generation | false | google | null | google/reformer-enwik8 | 14,199 | 2 | transformers | 582 | ## Reformer Language model on character level and trained on enwik8.
*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e.g.* in
the scope of the *Hutter prize*: https://en.wikipedia.org/wiki/Hutter_Prize.
`reformer-enwik8` was pretrained on the first 90M ... |
uclanlp/plbart-base | cf5287241fcff3819f6ade49635dc2d77efee032 | 2021-11-09T17:07:52.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uclanlp | null | uclanlp/plbart-base | 14,197 | 3 | transformers | 583 | Entry not found |
cross-encoder/nli-deberta-v3-base | 00259490157750c3fb567a3570e0fcc827e0cee0 | 2021-12-27T22:26:49.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"transformers",
"microsoft/deberta-v3-base",
"license:apache-2.0",
"zero-shot-classification"
] | zero-shot-classification | false | cross-encoder | null | cross-encoder/nli-deberta-v3-base | 14,193 | 3 | transformers | 584 | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- microsoft/deberta-v3-base
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/... |
allenai/PRIMERA | 550385ac1f15be0309fddaa429d72a87ed60aa6b | 2022-06-25T16:04:26.000Z | [
"pytorch",
"tf",
"led",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/PRIMERA | 14,189 | 4 | transformers | 585 | ---
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in the original github rep... |
princeton-nlp/sup-simcse-roberta-base | 4bf73c6b5df517f74188c5e9ec159b2208c89c08 | 2021-05-20T19:33:45.000Z | [
"pytorch",
"jax",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | princeton-nlp | null | princeton-nlp/sup-simcse-roberta-base | 14,162 | 1 | transformers | 586 | Entry not found |
vblagoje/bart_lfqa | 5493d5be6812cdb4835e004ce17ea2082cc25b03 | 2022-02-14T15:54:47.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:vblagoje/lfqa",
"dataset:vblagoje/lfqa_support_docs",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | vblagoje | null | vblagoje/bart_lfqa | 14,151 | 12 | transformers | 587 | ---
language: en
datasets:
- vblagoje/lfqa
- vblagoje/lfqa_support_docs
license: mit
---
## Introduction
See [blog post](https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb) for more details.
## Usage
```python
import torch
from transformers import AutoTokenizer, Auto... |
patrickvonplaten/led-large-16384-pubmed | 10e2f1e6bdb4fa833e899fec2be31b87751135ce | 2021-01-11T15:42:53.000Z | [
"pytorch",
"tf",
"led",
"text2text-generation",
"en",
"dataset:scientific_papers",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | patrickvonplaten | null | patrickvonplaten/led-large-16384-pubmed | 14,142 | 5 | transformers | 588 | ---
language: en
datasets:
- scientific_papers
license: apache-2.0
---
## Introduction
[Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer).
This is an unofficial *led-large-16384* checkpoint that is fine-tuned on the [pubmed dataset](https://huggingface.co/datasets/scien... |
dlb/electra-base-portuguese-uncased-brwac | 60cd1eb5aa58c9468200271ae1b4f55cd1ee2036 | 2021-12-10T12:33:58.000Z | [
"pytorch",
"pt",
"dataset:brwac",
"transformers",
"electra",
"pretraining"
] | null | false | dlb | null | dlb/electra-base-portuguese-uncased-brwac | 14,132 | 1 | transformers | 589 | ---
language: pt
tags:
- electra
- pretraining
- pytorch
datasets:
- brwac
---
|
facebook/s2t-small-librispeech-asr | 89d22e9beca033913df096434cccc2c41199d8c1 | 2022-05-24T10:45:16.000Z | [
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"arxiv:1904.08779",
"transformers",
"speech",
"audio",
"hf-asr-leaderboard",
"license:mit",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/s2t-small-librispeech-asr | 14,116 | 10 | transformers | 590 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: mit
pipeline_tag: automatic-speech-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech s... |
dmis-lab/bern2-ner | e09c2e59b9e90cdf3f5c55e3923fe2c2034070a9 | 2021-10-27T06:15:12.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | dmis-lab | null | dmis-lab/bern2-ner | 14,113 | 2 | transformers | 591 | NER Model of BERN2 system
|
climatebert/distilroberta-base-climate-f | 046b11a4b8c9db2c04a1f170bd48f863e0d2cf47 | 2022-03-09T16:49:27.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"arxiv:2110.12010",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | climatebert | null | climatebert/distilroberta-base-climate-f | 14,055 | 5 | transformers | 592 | ---
language: en
license: apache-2.0
---
Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pretrained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. T... |
google/pegasus-multi_news | e68ac31f04c1daf2956f36d5ad1701f2d6f91932 | 2020-10-22T16:33:29.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-multi_news | 14,035 | 5 | transformers | 593 | ---
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: [@... |
bigscience/T0pp | cd850304a7a82b39522a4a9b36f55c287ed72995 | 2022-06-21T01:20:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | bigscience | null | bigscience/T0pp | 13,952 | 265 | transformers | 594 | ---
datasets:
- bigscience/P3
language: en
license: apache-2.0
widget:
- text: "A is the son's of B's uncle. What is the family relationship between A and B?"
- text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."
- text: "Task: copy but say the opposite.\n
PSG won its match again... |
sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco | a03882cb371476b74ca3557366452cd868ac4f42 | 2021-04-15T08:54:28.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"en",
"dataset:ms_marco",
"arxiv:2104.06967",
"transformers",
"dpr",
"dense-passage-retrieval",
"knowledge-distillation"
] | feature-extraction | false | sebastian-hofstaetter | null | sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco | 13,937 | 9 | transformers | 595 | ---
language: "en"
tags:
- dpr
- dense-passage-retrieval
- knowledge-distillation
datasets:
- ms_marco
---
# DistilBert for Dense Passage Retrieval trained with Balanced Topic Aware Sampling (TAS-B)
We provide a retrieval trained DistilBert-based model (we call the *dual-encoder then dot-product scoring* ... |
mys/bert-base-turkish-cased-nli-mean-faq-mnr | b3b5e1a4a437ea73d00e42e3b9b92ecb1b65109e | 2022-07-07T15:08:18.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | mys | null | mys/bert-base-turkish-cased-nli-mean-faq-mnr | 13,908 | 2 | transformers | 596 | # {MODEL_NAME}
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉
## Model
This is a finetuned version of [mys/bert-base-turkish-cased-nli-mean](https://huggingface.co/) for FAQ retrieval, which is itself a finetuned version of [dbmdz/bert-base-turkish-cas... |
microsoft/deberta-large-mnli | 7296194b9009373def4f7c5dad292651e4b5cf4e | 2021-05-21T20:07:51.000Z | [
"pytorch",
"deberta",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v1",
"deberta-mnli",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/deberta-large-mnli | 13,868 | 3 | transformers | 597 | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... |
deepset/gbert-base-germandpr-question_encoder | dcbc13b5d22e49f58549a54dc7f30edec6153c39 | 2021-10-21T12:17:20.000Z | [
"pytorch",
"dpr",
"feature-extraction",
"de",
"dataset:deepset/germandpr",
"transformers",
"exbert",
"license:mit"
] | feature-extraction | false | deepset | null | deepset/gbert-base-germandpr-question_encoder | 13,861 | 4 | transformers | 598 | ---
language: de
datasets:
- deepset/germandpr
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
... |
shahrukhx01/bert-mini-finetune-question-detection | c9088454657626f5e370b3a1ec993fdcad81aaf6 | 2021-07-11T14:27:37.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"neural-search-query-classification",
"neural-search"
] | text-classification | false | shahrukhx01 | null | shahrukhx01/bert-mini-finetune-question-detection | 13,811 | 3 | transformers | 599 |
---
language: "en"
tags:
- neural-search-query-classification
- neural-search
widget:
- text: "keyword query."
---
# KEYWORD QUERY VS STATEMENT/QUESTION CLASSIFIER FOR NEURAL SEARCH
| Train Loss | Validation Acc.| Test Acc.|
| ------------- |:-------------: | -----: |
| 0.000806 | 0.99 | 0.997 |
```pyth... |
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