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 | readme stringlengths 0 186k | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ramsrigouthamg/t5_paraphraser | d78f7749656e21d8b6fdf372efb5c5d1dbce577f | 2020-12-11T22:00:04.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ramsrigouthamg | null | ramsrigouthamg/t5_paraphraser | 9,713 | 6 | transformers | ## Model in Action 🚀
```python
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
def set_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
set_seed(42)
model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_paraphra... | [
-0.06329762190580368,
-0.05019315704703331,
0.028862779960036278,
0.03912495821714401,
0.013211025856435299,
-0.0549628771841526,
-0.0054979827255010605,
0.046711500734090805,
-0.06236286461353302,
-0.052693966776132584,
0.03415695205330849,
-0.05880880355834961,
-0.05016383156180382,
-0.0... |
valhalla/t5-small-e2e-qg | feec82746b18ab037724c14f11277f320bd73920 | 2021-07-30T13:10:33.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:squad",
"arxiv:1910.10683",
"transformers",
"question-generation",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/t5-small-e2e-qg | 9,563 | 3 | transformers | ---
datasets:
- squad
tags:
- question-generation
widget:
- text: "Python is developed by Guido Van Rossum and released in 1991. </s>"
license: mit
---
## T5 for question-generation
This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for end-to-end question generation task. Simply input the text and the... | [
-0.08515087515115738,
-0.04181338846683502,
-0.0586128793656826,
0.029416777193546295,
-0.005845144856721163,
0.011947006918489933,
-0.001649213139899075,
-0.003913224209100008,
0.000490907987114042,
-0.035534728318452835,
0.024766242131590843,
-0.09457182884216309,
0.08185736835002899,
-0... |
KoboldAI/GPT-J-6B-Skein | 95a7ea75328cc8e117fdbf967b9fa12f49d1d24c | 2022-03-14T22:44:49.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-J-6B-Skein | 9,531 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
allenai/longformer-large-4096-finetuned-triviaqa | 4a10c0999bd77b29f6fd122663787c770afa197e | 2021-03-10T02:31:53.000Z | [
"pytorch",
"tf",
"longformer",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | allenai | null | allenai/longformer-large-4096-finetuned-triviaqa | 9,500 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
ImAPizza/DialoGPT-medium-alberttwo | bedcf2148b3c45ebc5c0c8632d41fe4f4cde1d9f | 2021-08-29T13:39:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ImAPizza | null | ImAPizza/DialoGPT-medium-alberttwo | 9,477 | 1 | transformers | ---
tags:
- conversational
---
# Alberttwo DialoGPT Model | [
-0.011641668155789375,
-0.04896824434399605,
0.015683777630329132,
0.02879466488957405,
0.022008497267961502,
0.028402237221598625,
0.0800098106265068,
0.06098068878054619,
0.10993342101573944,
-0.05415920913219452,
-0.014822045341134071,
-0.039920873939991,
-0.04851925000548363,
-0.006196... |
google/long-t5-tglobal-base | c910dec42392b5586a643ee547d65a9f111059eb | 2022-06-22T09:05:39.000Z | [
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/long-t5-tglobal-base | 9,314 | 1 | transformers | ---
license: apache-2.0
language: en
---
# LongT5 (transient-global attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in... | [
-0.15084710717201233,
-0.0806349664926529,
0.03506012260913849,
-0.006590761244297028,
0.027178561314940453,
-0.011744457297027111,
-0.125850647687912,
0.01856338232755661,
-0.013142061419785023,
-0.06534656137228012,
0.03606047481298447,
0.07059571892023087,
-0.008150135166943073,
0.03291... |
BM-K/KoSimCSE-roberta-multitask | 2b1aaf3c27691ae2c06cc65387c6f1d60ea6eef0 | 2022-06-03T01:48:14.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"ko",
"transformers",
"korean"
] | feature-extraction | false | BM-K | null | BM-K/KoSimCSE-roberta-multitask | 9,306 | 1 | transformers | ---
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... | [
-0.051165420562028885,
-0.04539553076028824,
0.011456276290118694,
0.054888639599084854,
-0.0006184738595038652,
0.07306286692619324,
0.028538821265101433,
0.04796873405575752,
0.0036484936717897654,
-0.049174800515174866,
0.051703937351703644,
-0.11606522649526596,
0.02288612350821495,
0.... |
openclimatefix/nowcasting_cnn_v3 | f083f2c4de6ec7a0e5acbff167cb817c506d6113 | 2022-07-18T15:51:53.000Z | [
"pytorch",
"transformers",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit"
] | null | false | openclimatefix | null | openclimatefix/nowcasting_cnn_v3 | 9,283 | null | transformers | ---
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... | [
-0.07878629863262177,
-0.0530087947845459,
0.06303685903549194,
-0.0037487992085516453,
0.08956786245107651,
-0.02325441874563694,
-0.05547712370753288,
-0.01985733024775982,
-0.015385853126645088,
-0.01869397796690464,
-0.10241496562957764,
-0.034199681133031845,
-0.020612789317965508,
0.... |
google/bert_uncased_L-4_H-512_A-8 | 606e4d55252882ac25ba1f1d1a182075830f5a90 | 2021-05-19T17:30:51.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-4_H-512_A-8 | 9,254 | null | transformers | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
-0.02777470275759697,
-0.02693094126880169,
0.07438826560974121,
0.03228488564491272,
-0.0023304771166294813,
0.018128493800759315,
-0.06253628432750702,
0.0994548574090004,
-0.014644814655184746,
0.018868697807192802,
-0.015814494341611862,
0.03585591912269592,
0.03645862638950348,
0.0455... |
facebook/wav2vec2-xls-r-300m | e842f378fdbdb09aabc11d87c52f26b8f2dde333 | 2021-11-18T16:32:15.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"multilingual",
"dataset:common_voice",
"dataset:multilingual_librispeech",
"arxiv:2111.09296",
"transformers",
"speech",
"xls_r",
"xls_r_pretrained",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-xls-r-300m | 9,246 | 22 | transformers | ---
language: multilingual
datasets:
- common_voice
- multilingual_librispeech
tags:
- speech
- xls_r
- xls_r_pretrained
license: apache-2.0
---
# Wav2Vec2-XLS-R-300M
[Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) counting **300 million** paramete... | [
-0.05261414125561714,
-0.08654969930648804,
-0.051704324781894684,
-0.04312781244516373,
0.0036449157632887363,
0.01097971759736538,
-0.08288953453302383,
-0.025692913681268692,
0.029850777238607407,
-0.043230343610048294,
-0.020624915137887,
-0.08149153739213943,
-0.011101656593382359,
-0... |
sshleifer/tiny-distilbert-base-cased | 657df2b83a6986d88e4f528740259c9b49f796b1 | 2021-05-20T07:12:39.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | sshleifer | null | sshleifer/tiny-distilbert-base-cased | 9,211 | 1 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
nghuyong/ernie-1.0 | b06176bf30ecf544330ab008933c9ac1012f1a6d | 2021-05-20T01:40:40.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"zh",
"arxiv:1904.09223",
"transformers"
] | null | false | nghuyong | null | nghuyong/ernie-1.0 | 9,177 | 9 | transformers | ---
language: zh
---
# ERNIE-1.0
## Introduction
ERNIE (Enhanced Representation through kNowledge IntEgration) is proposed by Baidu in 2019,
which is designed to learn language representation enhanced by knowledge masking strategies i.e. entity-level masking and phrase-level masking.
Experimental results show that ... | [
-0.07299181818962097,
0.011008826084434986,
0.01748579554259777,
0.01715332828462124,
-0.0076467799954116344,
0.02005341462790966,
0.02893337421119213,
-0.04142645373940468,
0.023653242737054825,
0.000059708971093641594,
0.04834264889359474,
-0.0876656025648117,
0.07494384050369263,
0.0337... |
allenai/longformer-large-4096 | cfa97f5f8c58c219bfea4da030a0259d5dbb28c4 | 2021-03-10T02:31:17.000Z | [
"pytorch",
"tf",
"longformer",
"transformers"
] | null | false | allenai | null | allenai/longformer-large-4096 | 9,152 | 9 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
castorini/monot5-base-msmarco-10k | f15657ab3d2a5dd0b9a30c8c0b6a0a73c9cb5884 | 2021-10-17T11:24:22.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/monot5-base-msmarco-10k | 9,101 | 3 | transformers | This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
This model usually has a better zero-shot performance than `monot5-base-msmarco`, i.e., it performs better on datasets different from MS MARCO.
For more details on how to use it, check the following links:
- [A sim... | [
-0.15108679234981537,
-0.05096009373664856,
0.0284718070179224,
0.030225329101085663,
-0.025211095809936523,
0.04826401546597481,
-0.08938957750797272,
0.07781503349542618,
0.0005556968972086906,
-0.012060926295816898,
-0.03720035031437874,
0.05763912573456764,
0.012371353805065155,
0.0353... |
lysandre/tiny-tapas-random-wtq | 82ff80f61b524e1e9dfd55636bf471f1f4bb0045 | 2020-12-15T04:19:58.000Z | [
"pytorch",
"tapas",
"table-question-answering",
"transformers"
] | table-question-answering | false | lysandre | null | lysandre/tiny-tapas-random-wtq | 9,078 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
TurkuNLP/eccobert-base-cased-v1 | 800ade528925e578acfbec3668da3d3ad2dfaee1 | 2022-04-13T16:57:18.000Z | [
"pytorch",
"bert",
"pretraining",
"en",
"transformers"
] | null | false | TurkuNLP | null | TurkuNLP/eccobert-base-cased-v1 | 9,071 | null | transformers | ---
language: en
---
# ECCO-BERT base model (cased)
A pretrained BERT model trained exclusively on the ECCO (Eighteenth Century Collections Online) dataset of digitized documents published during the 18th century in the United Kingdom. The model is equivalent in size to [bert-base-cased](https://huggingface.co/bert-b... | [
-0.10352429002523422,
-0.018954241648316383,
0.028460972011089325,
-0.025235461071133614,
0.016557637602090836,
0.04320986568927765,
-0.05902103707194328,
0.07830803841352463,
0.017404230311512947,
-0.012726498767733574,
0.03842492029070854,
0.05259536951780319,
0.039429906755685806,
0.017... |
abjbpi/Dwight_Schrute | 451aab582fe08f5210a58859f9ec1c79278e341b | 2021-06-04T11:43:31.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | abjbpi | null | abjbpi/Dwight_Schrute | 9,070 | 2 | transformers | ---
tags:
- conversational
---
# My Awesome Model | [
-0.05090106651186943,
-0.031366977840662,
0.03581665828824043,
-0.0054056984372437,
0.08076965808868408,
-0.04372682794928551,
0.1371273398399353,
0.04686865955591202,
0.07841186225414276,
-0.04006827622652054,
0.029963944107294083,
-0.012565406039357185,
0.036244455724954605,
0.0099639333... |
DeepChem/ChemBERTa-77M-MLM | ed8a5374f2024ec8da53760af91a33fb8f6a15ff | 2022-01-20T18:02:38.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | DeepChem | null | DeepChem/ChemBERTa-77M-MLM | 9,026 | 1 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
zenham/khemx_m_e4_16h | 08ed457ad68559c2c845dbb6112e84e6cdb00e6f | 2022-03-08T02:50:45.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | zenham | null | zenham/khemx_m_e4_16h | 9,015 | null | transformers | ---
tags:
- conversational
---
#khemx m e4 16h 0k DialoGPT Model | [
0.0002064208674710244,
0.02158665470778942,
0.029150791466236115,
-0.03783119469881058,
-0.03258444741368294,
-0.03732001781463623,
0.09779731184244156,
0.010959630832076073,
0.05743959918618202,
-0.007529596332460642,
0.017540758475661278,
-0.08732490241527557,
0.0003585227532312274,
0.01... |
kha-white/manga-ocr-base | aa6573bd10b0d446cbf622e29c3e084914df9741 | 2022-06-22T15:34:05.000Z | [
"pytorch",
"vision-encoder-decoder",
"ja",
"dataset:manga109s",
"transformers",
"image-to-text",
"license:apache-2.0"
] | image-to-text | false | kha-white | null | kha-white/manga-ocr-base | 8,969 | 5 | transformers | ---
language: ja
tags:
- image-to-text
license: apache-2.0
datasets:
- manga109s
---
# Manga OCR
Optical character recognition for Japanese text, with the main focus being Japanese manga.
It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) framework.
Manga OCR... | [
-0.06438907980918884,
-0.009474285878241062,
-0.044761981815099716,
-0.06921251863241196,
0.04511384665966034,
-0.0027452672366052866,
-0.010576562955975533,
0.048744235187768936,
-0.022976083680987358,
-0.029715849086642265,
0.08420126140117645,
0.048457033932209015,
0.07192529737949371,
... |
Zixtrauce/BDBot4Epoch | 77357067c689ccb8c19220a32137eb8646bf87e5 | 2022-01-01T23:46:44.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Zixtrauce | null | Zixtrauce/BDBot4Epoch | 8,905 | null | transformers | ---
tags:
- conversational
---
#BrandonBot4Epochs | [
-0.017745163291692734,
0.02695123851299286,
0.023420484736561775,
-0.05541969835758209,
0.06651896983385086,
-0.08757458627223969,
0.14189128577709198,
-0.040621206164360046,
0.04028312489390373,
-0.023804957047104836,
-0.020040513947606087,
-0.029352432116866112,
0.004563924390822649,
-0.... |
google/t5-base-lm-adapt | 82aa560c46d415609fa3403f4e94d2c1a90923af | 2021-11-01T14:01:15.000Z | [
"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-base-lm-adapt | 8,874 | 6 | transformers | ---
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... | [
-0.06374464929103851,
-0.04896508902311325,
0.04971892386674881,
-0.01899581588804722,
0.09040919691324234,
-0.0002084529842250049,
-0.03380180895328522,
-0.04972998425364494,
-0.04591972753405571,
-0.0742875337600708,
0.07705316692590714,
0.029367027804255486,
0.009775844402611256,
-0.072... |
princeton-nlp/unsup-simcse-roberta-base | db28710348cf9f33a2be25c505f98f0fbbbfe768 | 2021-06-16T12:12:10.000Z | [
"pytorch",
"jax",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | princeton-nlp | null | princeton-nlp/unsup-simcse-roberta-base | 8,866 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
sberbank-ai/mGPT | 9f49a85776d5ec166120ea81719987fe0f643574 | 2022-04-21T18:06:50.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"az",
"sw",
"af",
"ar",
"ba",
"be",
"bxr",
"bg",
"bn",
"cv",
"hy",
"da",
"de",
"el",
"es",
"eu",
"fa",
"fi",
"fr",
"he",
"hi",
"hu",
"kk",
"id",
"it",
"ja",
"ka",
"ky",
"ko",
"lt",
"lv",
"mn",
"ml",
... | text-generation | false | sberbank-ai | null | sberbank-ai/mGPT | 8,865 | 56 | transformers | ---
license: apache-2.0
language:
- en
- az
- sw
- af
- ar
- ba
- be
- bxr
- bg
- bn
- cv
- hy
- da
- de
- el
- es
- eu
- fa
- fi
- fr
- he
- hi
- hu
- kk
- id
- it
- ja
- ka
- ky
- ko
- lt
- lv
- mn
- ml
- os
- mr
- ms
- my
- nl
- ro
- pl
- pt
- sah
- ru
- tg
- sv
- ta
- te
- tk
- th
- tr
- tl
- tt
- tyv
- uk
- en
- u... | [
-0.13837048411369324,
-0.11026205122470856,
-0.0366678312420845,
0.02246154099702835,
0.04343828558921814,
-0.046915553510189056,
-0.011059295386075974,
0.028004180639982224,
-0.004300341941416264,
-0.05740875378251076,
-0.005342238582670689,
-0.07862739264965057,
0.030558360740542412,
0.0... |
mrm8488/codeBERTaJS | 2d18abf10b01f62f4fe089ef79973541ec534674 | 2021-05-20T18:17:36.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"code",
"arxiv:1909.09436",
"transformers",
"javascript",
"autotrain_compatible"
] | fill-mask | false | mrm8488 | null | mrm8488/codeBERTaJS | 8,801 | 2 | transformers | ---
language: code
thumbnail:
tags:
- javascript
- code
widget:
- text: "async function createUser(req, <mask>) { if (!validUser(req.body.user)) { return res.status(400); } user = userService.createUser(req.body.user); return res.json(user); }"
---
# CodeBERTaJS
CodeBERTaJS is a RoBERTa-like model trained on the [C... | [
-0.17827105522155762,
-0.06724216789007187,
-0.04825394228100777,
0.056332677602767944,
0.015083003789186478,
-0.020152753219008446,
0.03469913452863693,
-0.008838150650262833,
-0.0033321822993457317,
-0.061544667929410934,
0.014825032092630863,
0.014167258515954018,
0.023232456296682358,
... |
pvl/labse_bert | 64aecfed3a09108bbdc9fcfcba7447f36a2a34c7 | 2021-09-22T09:35:24.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"en",
"transformers",
"embeddings",
"license:apache-2.0"
] | null | false | pvl | null | pvl/labse_bert | 8,800 | null | transformers | ---
language: en
thumbnail:
tags:
- bert
- embeddings
license: apache-2.0
---
# LABSE BERT
## Model description
Model for "Language-agnostic BERT Sentence Embedding" paper from Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, Wei Wang. Model available in [TensorFlow Hub](https://tfhub.dev/google/LaBSE/1... | [
-0.08481653779745102,
-0.06526661664247513,
0.051753636449575424,
0.04113209620118141,
0.03532683849334717,
0.05674731358885765,
0.02298211120069027,
0.07193111628293991,
0.051606904715299606,
-0.06789924949407578,
-0.008494184352457523,
-0.07778545469045639,
-0.0009455272229388356,
0.0812... |
dbmdz/bert-base-turkish-uncased | 0582a4e05fd7ec5aa6b265d4bc4c81438d951593 | 2021-05-19T15:15:54.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"tr",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/bert-base-turkish-uncased | 8,784 | 5 | transformers | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources an uncased model for Turkish 🎉
# 🇹🇷 BERTurk
BERTurk is a community-driven uncased BERT model for Turkish.
Some datasets used for pretraining and... | [
-0.11216237396001816,
-0.10918832570314407,
0.029416779056191444,
0.00532143609598279,
-0.011443842202425003,
0.059406477957963943,
-0.042436037212610245,
0.004199334420263767,
0.057248227298259735,
-0.005149402655661106,
-0.02086455002427101,
0.006235199514776468,
-0.0026740452740341425,
... |
sentence-transformers/all-roberta-large-v1 | 42d37b9d8c9929c64dce4a2b25f6eaa0f59eaf99 | 2021-08-31T09:33:26.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-roberta-large-v1 | 8,748 | 5 | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-roberta-large-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be ... | [
-0.05484281852841377,
-0.04288968816399574,
-0.01240472961217165,
0.04543238878250122,
0.023772334679961205,
0.06621138006448746,
-0.046433333307504654,
0.01735966093838215,
0.02730361372232437,
-0.05484095960855484,
0.046245649456977844,
0.0008387292036786675,
0.04026908054947853,
0.05353... |
nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large | d828558d1a570cbbb5e62a8dbf85c8f18bf7982a | 2021-06-20T19:03:16.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nreimers | null | nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large | 8,688 | 4 | transformers | # Multilingual MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | [
-0.024727946147322655,
-0.0026722648181021214,
-0.06438318639993668,
0.013568010181188583,
0.0008515773224644363,
0.04484379291534424,
-0.05825844779610634,
-0.024744702503085136,
0.025539156049489975,
-0.017522145062685013,
0.05681233108043671,
-0.04648791253566742,
0.0028828149661421776,
... |
TahaDouaji/detr-doc-table-detection | a3e4b9a10c65eeaaf6d0579e4c591ace8dc2ef77 | 2022-03-12T12:09:38.000Z | [
"pytorch",
"detr",
"object-detection",
"transformers"
] | object-detection | false | TahaDouaji | null | TahaDouaji/detr-doc-table-detection | 8,646 | 3 | transformers | ---
tags:
- object-detection
---
## Model description
detr-doc-table-detection is a model trained to detect both **Bordered** and **Borderless** tables in documents, based on [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)
## Training data
The model was trained on ICDAR2019 Table Dataset
#... | [
-0.06218251585960388,
-0.008407077752053738,
-0.03495689108967781,
0.0048962002620100975,
0.10590530931949615,
-0.06351599842309952,
0.00984638649970293,
0.018533121794462204,
-0.07450778037309647,
-0.05048777908086777,
0.06866391003131866,
-0.04253290593624115,
0.07265004515647888,
0.0551... |
finiteautomata/bertweet-base-emotion-analysis | 64046df9cc41eab40e1ecde7d2b7fb42b971be5b | 2021-12-10T13:28:56.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2106.09462",
"transformers",
"emotion-analysis"
] | text-classification | false | finiteautomata | null | finiteautomata/bertweet-base-emotion-analysis | 8,619 | 4 | transformers | ---
language:
- en
tags:
- emotion-analysis
---
# Emotion Analysis in English
## bertweet-base-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with EmoEvent corpus for Emotion detection in English. Base model is [B... | [
-0.04230852425098419,
-0.036132849752902985,
0.023534459993243217,
0.022955985739827156,
0.06478442251682281,
0.04100355505943298,
0.0015926144551485777,
0.04074859246611595,
0.061550695449113846,
-0.021101970225572586,
0.02811291255056858,
-0.06487465649843216,
0.036414261907339096,
0.030... |
epwalsh/bert-xsmall-dummy | d36cc494a54ac76cac8c237866fe8ce540c879a6 | 2021-05-19T16:30:53.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | epwalsh | null | epwalsh/bert-xsmall-dummy | 8,538 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
kamalkraj/BioELECTRA-PICO | 70e29e17b3546be81de3723e7cedf3409d7f234f | 2021-11-27T11:16:12.000Z | [
"pytorch",
"electra",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | kamalkraj | null | kamalkraj/BioELECTRA-PICO | 8,538 | 1 | transformers | ---
widget:
- text: "Those in the aspirin group experienced reduced duration of headache compared to those in the placebo arm (P<0.05)"
---
BioELECTRA-PICO | [
-0.07137191295623779,
0.06053144857287407,
-0.05212065950036049,
0.10194957256317139,
-0.011128793470561504,
-0.022962598130106926,
0.07258734107017517,
0.09566255658864975,
0.07747144997119904,
-0.07816179096698761,
0.053230512887239456,
-0.04413013905286789,
-0.03414393961429596,
-0.0112... |
allenai/unifiedqa-t5-large | 3fc39b105a75526eb2de2271744d48a4202857aa | 2021-06-23T12:00:07.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/unifiedqa-t5-large | 8,513 | 2 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
flaubert/flaubert_base_uncased | 56ea0bf6e54b59c192f99f2397e932a9915cae4c | 2021-10-18T08:14:52.000Z | [
"pytorch",
"flaubert",
"fill-mask",
"fr",
"dataset:flaubert",
"transformers",
"bert",
"language-model",
"flue",
"french",
"flaubert-base",
"uncased",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | flaubert | null | flaubert/flaubert_base_uncased | 8,481 | null | transformers | ---
language: fr
license: mit
datasets:
- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- flaubert-base
- uncased
---
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of d... | [
-0.11780469864606857,
-0.11926593631505966,
-0.00001596406764292624,
-0.004828803241252899,
0.0356057733297348,
0.0245139729231596,
-0.02043706551194191,
0.11056417226791382,
0.026260891929268837,
-0.01041898038238287,
-0.026366498321294785,
0.01390332542359829,
0.026412973180413246,
0.026... |
aliosm/ComVE-distilgpt2 | 95db37f0c7b4bef1ec214a0a5d8cd457d1f55ece | 2021-05-21T13:07:30.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:ComVE",
"transformers",
"exbert",
"commonsense",
"semeval2020",
"comve",
"license:mit"
] | text-generation | false | aliosm | null | aliosm/ComVE-distilgpt2 | 8,429 | null | transformers | ---
language: "en"
tags:
- exbert
- commonsense
- semeval2020
- comve
license: "mit"
datasets:
- ComVE
metrics:
- bleu
widget:
- text: "Chicken can swim in water. <|continue|>"
---
# ComVE-distilgpt2
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval... | [
-0.02141518145799637,
-0.027090368792414665,
0.03960300236940384,
-0.030968794599175453,
0.11888387054204941,
0.02362515963613987,
0.04183113947510719,
0.013932593166828156,
-0.0158697459846735,
-0.012545762583613396,
-0.02950819954276085,
-0.18094928562641144,
0.09920281171798706,
0.02795... |
chkla/roberta-argument | d5480352a5ad33b0135cc1193a62be24396e557a | 2021-05-20T15:19:04.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"english",
"transformers"
] | text-classification | false | chkla | null | chkla/roberta-argument | 8,424 | 3 | transformers | ---
language: english
widget:
- text: "It has been determined that the amount of greenhouse gases have decreased by almost half because of the prevalence in the utilization of nuclear power."
---
### Welcome to RoBERTArg!
🤖 **Model description**
This model was trained on ~25k heterogeneous manually annotated senten... | [
-0.024078674614429474,
0.03438662365078926,
-0.030870012938976288,
0.06134043261408806,
0.13429562747478485,
0.0821409597992897,
0.03655914217233658,
0.017233895137906075,
0.05575022101402283,
-0.001776103163138032,
0.041875772178173065,
-0.014216921292245388,
0.041393451392650604,
0.04012... |
flair/ner-multi | b4f9c84fc84d2b1a687bf3f38d15218129e1d202 | 2021-03-02T22:17:41.000Z | [
"pytorch",
"en",
"de",
"nl",
"es",
"multilingual",
"dataset:conll2003",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-multi | 8,414 | 4 | flair | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language:
- en
- de
- nl
- es
- multilingual
datasets:
- conll2003
widget:
- text: "George Washington ging nach Washington"
---
## 4-Language NER in Flair (English, German, Dutch and Spanish)
This is the standard 4-class NER model for 4 CoNLL-03 lan... | [
-0.015656553208827972,
-0.006676780991256237,
-0.0052027348428964615,
-0.052068501710891724,
0.02968950755894184,
0.03607534244656563,
-0.019655423238873482,
0.014392981305718422,
0.025164538994431496,
-0.03614146634936333,
-0.004936967045068741,
-0.10041820257902145,
-0.02246933802962303,
... |
facebook/detr-resnet-101 | 1a655091c08729eecf4fc5063c27fa5ea82aeaa3 | 2022-06-27T08:30:19.000Z | [
"pytorch",
"detr",
"object-detection",
"dataset:coco",
"arxiv:2005.12872",
"transformers",
"vision",
"license:apache-2.0"
] | object-detection | false | facebook | null | facebook/detr-resnet-101 | 8,397 | 1 | transformers | ---
license: apache-2.0
tags:
- object-detection
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
example_title: Savanna
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- s... | [
-0.10318499803543091,
-0.010600433684885502,
0.02613512985408306,
-0.022755276411771774,
0.13274496793746948,
-0.05331333726644516,
-0.015138912945985794,
0.014471438713371754,
0.0007851815316826105,
-0.013555631041526794,
0.045525334775447845,
-0.042567282915115356,
-0.030034275725483894,
... |
deepset/gelectra-large-germanquad | 1b7c5a7fe58943f9df30968460128f2766315f81 | 2022-07-19T14:39:31.000Z | [
"pytorch",
"tf",
"electra",
"question-answering",
"de",
"dataset:deepset/germanquad",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/gelectra-large-germanquad | 8,353 | 9 | transformers | ---
language: de
datasets:
- deepset/germanquad
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
... | [
-0.07481022924184799,
-0.027764294296503067,
0.006425043102353811,
0.07286721467971802,
0.006982266902923584,
0.028809329494833946,
-0.011222079396247864,
0.0503423847258091,
-0.03125924989581108,
-0.0571051724255085,
0.027536509558558464,
-0.09209080040454865,
-0.005787962581962347,
0.070... |
human-centered-summarization/financial-summarization-pegasus | a720f829427cb196a5618a0416473b8597cd106e | 2022-06-29T06:25:30.000Z | [
"pytorch",
"tf",
"pegasus",
"text2text-generation",
"en",
"dataset:xsum",
"arxiv:1912.08777",
"transformers",
"summarization",
"model-index",
"autotrain_compatible"
] | summarization | false | human-centered-summarization | null | human-centered-summarization/financial-summarization-pegasus | 8,315 | 22 | transformers | ---
language:
- en
tags: summarization
datasets:
- xsum
metrics:
- rouge
widget:
- text: "National Commercial Bank (NCB), Saudi Arabia\u2019s largest lender by assets,\
\ agreed to buy rival Samba Financial Group for $15 billion in the biggest banking\
\ takeover this year.NCB will pay 28.45 riyals ($7.58) for ... | [
0.03525776043534279,
-0.07703676074743271,
-0.051842935383319855,
0.004821832291781902,
-0.0242728590965271,
-0.03655292093753815,
0.03452764078974724,
0.024865152314305305,
0.0679158866405487,
-0.061176445335149765,
0.013533301651477814,
-0.020910147577524185,
0.07630247622728348,
-0.0514... |
sshleifer/tiny-xlnet-base-cased | 275d2c323ddd18dad60cd585934383c29027878b | 2020-05-08T15:35:32.000Z | [
"pytorch",
"xlnet",
"text-generation",
"transformers"
] | text-generation | false | sshleifer | null | sshleifer/tiny-xlnet-base-cased | 8,259 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
microsoft/unixcoder-base-nine | 1e114832924596b75dcd2e0bdde218c0f7ee039f | 2022-04-02T05:45:58.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | microsoft | null | microsoft/unixcoder-base-nine | 8,245 | 2 | transformers | ---
license: apache-2.0
---
| [
0.04086383432149887,
0.04840587452054024,
-0.01111048087477684,
-0.0822305753827095,
0.03046034276485443,
-0.024620788171887398,
-0.00873124971985817,
-0.032080959528684616,
-0.009516960941255093,
0.014524046331644058,
0.06244279816746712,
-0.03306293115019798,
-0.057087719440460205,
-0.02... |
julien-c/dummy-diff-tokenizer | 8b54c50bfd24739488683452f24d4471f5d75a21 | 2021-05-20T17:30:11.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | julien-c | null | julien-c/dummy-diff-tokenizer | 8,149 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
textattack/bert-base-uncased-MRPC | d421614df8fbeb22d6826a24d6397809fdc1e3ff | 2021-05-20T07:32:52.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-MRPC | 8,135 | null | transformers | ## 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 16, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a cla... | [
-0.11459482461214066,
-0.020215561613440514,
0.049413081258535385,
-0.008184502832591534,
-0.025021186098456383,
0.07342878729104996,
-0.030838053673505783,
0.04016721993684769,
0.014555455185472965,
-0.05663282796740532,
-0.0029834264423698187,
0.008984590880572796,
-0.01817805878818035,
... |
deepset/bert-small-mm_retrieval-passage_encoder | c764744512975bd3823f689601ab0e388a29c366 | 2021-10-19T16:14:29.000Z | [
"pytorch",
"dpr",
"transformers"
] | null | false | deepset | null | deepset/bert-small-mm_retrieval-passage_encoder | 8,119 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
sshleifer/distilbart-xsum-12-6 | 5b2e376c845c201ddc34ec0e55fd1ad9890ba5ee | 2021-06-14T07:58:25.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distilbart-xsum-12-6 | 8,112 | 2 | transformers | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
-0.10242787003517151,
-0.08055665343999863,
0.058801449835300446,
0.001729630515910685,
-0.01020839437842369,
-0.020107010379433632,
-0.09146951138973236,
0.055804893374443054,
-0.03275766223669052,
-0.0814155638217926,
0.047091152518987656,
-0.024555958807468414,
0.02256167307496071,
-0.0... |
GanjinZero/UMLSBert_ENG | 1e4841546c6384cefa47192146a7bd368d509849 | 2022-04-27T08:18:37.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"transformers",
"biomedical",
"license:apache-2.0"
] | feature-extraction | false | GanjinZero | null | GanjinZero/UMLSBert_ENG | 8,109 | 3 | transformers | ---
language:
- en
license: apache-2.0
tags:
- bert
- biomedical
---
CODER: Knowledge infused cross-lingual medical term embedding for term normalization.
English Version. Old name. This model is not UMLSBert!!!
```
@article{YUAN2022103983,
title = {CODER: Knowledge-infused cross-lingual medical term embedding fo... | [
-0.01304174680262804,
-0.05261708050966263,
0.006109150592237711,
0.0032456577755510807,
-0.006242305040359497,
0.037726495414972305,
-0.06559067964553833,
0.09016583114862442,
-0.049748968333005905,
0.041397109627723694,
0.0516948401927948,
-0.01406204141676426,
0.024410801008343697,
0.09... |
bigscience/bigscience-small-testing | 5fc95662beefe9606b9f9f3b9eefdd87cdf4b51a | 2022-07-11T10:04:17.000Z | [
"pytorch",
"bloom",
"feature-extraction",
"eng",
"transformers",
"integration",
"text-generation"
] | text-generation | false | bigscience | null | bigscience/bigscience-small-testing | 8,081 | null | transformers | ---
language:
- eng
tags:
- integration
pipeline_tag: text-generation
---
# BigScience - testing model
This model aims to test the conversion between Megatron-LM and transformers. It is a small ```GPT-2```-like model that has been used to debug the script. Use it only for integration tests | [
-0.08087308704853058,
0.01658627949655056,
-0.026207296177744865,
0.012760224752128124,
0.049331896007061005,
-0.11245300620794296,
-0.043065812438726425,
0.10059072077274323,
-0.10919713973999023,
-0.026045437902212143,
0.031146999448537827,
-0.0563659742474556,
0.049019262194633484,
-0.0... |
lvwerra/distilbert-imdb | dc2e91fb7046e0ede2359fd54e667446daf267a3 | 2022-04-30T11:21:06.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | lvwerra | null | lvwerra/distilbert-imdb | 8,073 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
... | [
-0.10599382221698761,
-0.0434105359017849,
-0.012441826984286308,
0.02246803045272827,
0.013310329988598824,
0.0038301663007587194,
0.025629501789808273,
0.04090632498264313,
-0.038466550409793854,
-0.14717997610569,
0.03168392553925514,
-0.060193855315446854,
0.03140324726700783,
-0.02486... |
uer/gpt2-chinese-lyric | c835964d9427bf1b4d01adf867454c9a85d4e385 | 2022-07-15T08:25:43.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"transformers"
] | text-generation | false | uer | null | uer/gpt2-chinese-lyric | 8,060 | 8 | transformers | ---
language: zh
widget:
- text: "最美的不是下雨天，是曾与你躲过雨的屋檐"
---
# Chinese GPT2 Lyric Model
## Model description
The model is used to generate Chinese lyrics. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese... | [
-0.08998191356658936,
-0.032523803412914276,
0.02321925014257431,
0.010561428032815456,
-0.0019674666691571474,
0.0245301965624094,
0.026444820687174797,
0.024702711030840874,
0.0374826081097126,
-0.0925922691822052,
0.03440621867775917,
-0.08156166970729828,
0.04279472306370735,
-0.045385... |
facebook/opt-66b | 8ea7547215f0999c2f648c8c034869bad974273e | 2022-06-25T15:31:09.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-66b | 8,059 | 31 | transformers | ---
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... | [
-0.09430353343486786,
0.010255655273795128,
-0.006349516566842794,
0.079442597925663,
0.024165261536836624,
0.013372854329645634,
-0.011628345586359501,
0.022513514384627342,
0.048179179430007935,
0.007294895127415657,
0.06878720223903656,
0.0030618805903941393,
0.010361358523368835,
0.052... |
hfl/chinese-xlnet-base | 34b827684078f956411389834966eb55588f5254 | 2021-03-03T01:44:59.000Z | [
"pytorch",
"tf",
"xlnet",
"text-generation",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | text-generation | false | hfl | null | hfl/chinese-xlnet-base | 8,033 | 13 | transformers | ---
language:
- zh
license: "apache-2.0"
---
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and ... | [
-0.11881551146507263,
-0.026175836101174355,
0.04676695168018341,
0.005689638201147318,
0.009067604318261147,
0.0467078760266304,
-0.02178468182682991,
-0.018991971388459206,
-0.019644850865006447,
-0.015515758655965328,
0.04460512474179268,
-0.0133119598031044,
0.014103207737207413,
-0.01... |
TheGoldenToaster/DialoGPT-medium-Bot | b9e2e669356dfda8108ccdf76d4db16cef38f227 | 2022-04-04T21:58:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | TheGoldenToaster | null | TheGoldenToaster/DialoGPT-medium-Bot | 7,888 | 1 | transformers | ---
tags:
- conversational
---
#Bot Chat | [
-0.07530517876148224,
0.0014849177096039057,
0.038555122911930084,
0.004969943314790726,
0.059507906436920166,
-0.08957493305206299,
0.13391733169555664,
0.011565092951059341,
0.04197738692164421,
-0.024990994483232498,
0.002872777171432972,
-0.029718788340687752,
-0.009749740362167358,
0.... |
ctl/wav2vec2-large-xlsr-cantonese | 6a6119ab39ec2a0c8d16edfbf91db45334540315 | 2021-07-06T01:16:38.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"zh-HK",
"yue",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ctl | null | ctl/wav2vec2-large-xlsr-cantonese | 7,858 | 1 | transformers | ---
language:
- zh-HK
- yue
datasets:
- common_voice
metrics:
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-large-xlsr-cantonese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
da... | [
-0.049578189849853516,
-0.01092930231243372,
0.0008256161236204207,
-0.062087658792734146,
-0.035977501422166824,
0.02412949688732624,
-0.03991124778985977,
-0.06375236064195633,
-0.042716655880212784,
-0.08002656698226929,
0.04797324538230896,
-0.17869171500205994,
-0.028254473581910133,
... |
pucpr/clinicalnerpt-disorder | 6a6597b35c51aeabfeedf828dff89de7a25f2b69 | 2021-10-13T09:32:51.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr | null | pucpr/clinicalnerpt-disorder | 7,858 | 4 | transformers | ---
language: "pt"
widget:
- text: "PACIENTE DE 69 ANOS COM ICC DE ETIOLOGIA ISQUÊMICA "
- text: "Paciente com Sepse pulmonar em D8 tazocin (paciente não recebeu por 2 dias Atb)."
datasets:
- SemClinBr
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"
---
<img sr... | [
-0.09277816861867905,
-0.0460968054831028,
-0.033515773713588715,
-0.009988026693463326,
-0.001158019294962287,
-0.02695516310632229,
-0.04033723846077919,
0.08332420885562897,
0.07562761008739471,
-0.0391087643802166,
0.1227356567978859,
-0.07242214679718018,
0.02125549130141735,
0.082908... |
rsvp-ai/bertserini-bert-base-squad | 1c93f9f29544f8ce8d6ee99133f91e5bd4dfed36 | 2022-06-23T14:13:40.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | rsvp-ai | null | rsvp-ai/bertserini-bert-base-squad | 7,828 | 2 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
vblagoje/bert-english-uncased-finetuned-pos | 46ec120264b121e8d92bef19b45c107d06d2cb99 | 2021-05-20T08:51:26.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | vblagoje | null | vblagoje/bert-english-uncased-finetuned-pos | 7,819 | 2 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
facebook/hubert-base-ls960 | dba3bb02fda4248b6e082697eee756de8fe8aa8a | 2021-11-05T12:43:12.000Z | [
"pytorch",
"tf",
"hubert",
"feature-extraction",
"en",
"dataset:librispeech_asr",
"arxiv:2106.07447",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/hubert-base-ls960 | 7,814 | 4 | transformers | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Hubert-Base
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The base model pretrained on 16kHz sampled speech audio. When using the... | [
-0.04352585971355438,
-0.09407538175582886,
-0.03377638757228851,
-0.04447558894753456,
-0.06588684022426605,
0.0702386423945427,
0.0009422205621376634,
-0.041706908494234085,
-0.06457708775997162,
-0.09763643145561218,
-0.02604658342897892,
-0.07805618643760681,
-0.02540084347128868,
-0.0... |
sahri/indonesiasentiment | 99f38e6c1b34109bbf4a6d7c6556c56f5d2eef6a | 2022-01-17T04:50:03.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"id",
"dataset:indonlu",
"arxiv:1907.11692",
"transformers",
"indonesian-roberta-base-sentiment-classifier",
"license:mit"
] | text-classification | false | sahri | null | sahri/indonesiasentiment | 7,791 | null | transformers | ---
language: id
tags:
- indonesian-roberta-base-sentiment-classifier
license: mit
datasets:
- indonlu
widget:
- text: "tidak jelek tapi keren"
---
## Indonesian RoBERTa Base Sentiment Classifier
Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [RoB... | [
-0.14286072552204132,
-0.05692875385284424,
-0.044992443174123764,
0.020817043259739876,
-0.018705595284700394,
0.056229449808597565,
-0.013110709376633167,
0.01167660765349865,
0.04041985049843788,
0.013523361645638943,
0.059962619096040726,
-0.018433749675750732,
0.007898712530732155,
0.... |
google/long-t5-local-base | e040d65029c54fb38eaefa4019bc3e2e31ba3c62 | 2022-06-22T09:04:55.000Z | [
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/long-t5-local-base | 7,756 | 5 | transformers | ---
license: apache-2.0
language: en
---
# LongT5 (local attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT... | [
-0.14853155612945557,
-0.08370652794837952,
0.03366756811738014,
-0.005874714348465204,
0.02386505715548992,
-0.0014904160052537918,
-0.13787443935871124,
0.020675497129559517,
-0.01738435961306095,
-0.06558583676815033,
0.035798314958810806,
0.06598906219005585,
-0.016261259093880653,
0.0... |
sbcBI/sentiment_analysis | 2e9e3afe68478a6168a11adb6c6f1b741e00ae83 | 2022-04-22T06:42:07.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:Confidential",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0"
] | text-classification | false | sbcBI | null | sbcBI/sentiment_analysis | 7,739 | null | transformers | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- Confidential
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.... | [
-0.0985998660326004,
-0.08899679780006409,
0.05186743289232254,
0.035263340920209885,
0.05233854427933693,
0.03494033217430115,
0.020050641149282455,
-0.005732312332838774,
0.045652635395526886,
-0.02304491214454174,
0.03469158709049225,
-0.03239385783672333,
0.07250905781984329,
0.0354376... |
MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli | 35cdaef56ac000802c965e584bb2facaede17c4a | 2022-07-28T16:23:53.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:anli",
"dataset:fever",
"arxiv:2006.03654",
"transformers",
"zero-shot-classification",
"license:mit"
] | zero-shot-classification | false | MoritzLaurer | null | MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli | 7,723 | 10 | transformers | ---
language:
- en
license: mit
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
datasets:
- multi_nli
- anli
- fever
pipeline_tag: zero-shot-classification
---
# DeBERTa-v3-base-mnli-fever-anli
## Model description
This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI)... | [
-0.049313612282276154,
-0.08974435180425644,
-0.019857410341501236,
0.00819606613367796,
0.05424134060740471,
-0.005011414177715778,
-0.055922020226716995,
0.04681232199072838,
0.018013034015893936,
-0.00929945893585682,
0.03942127898335457,
-0.05421833321452141,
-0.02055106870830059,
0.00... |
google/muril-base-cased | afd9f36c7923d54e97903922ff1b260d091d202f | 2022-06-10T13:33:04.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"arxiv:2103.10730",
"arxiv:1810.04805",
"arxiv:1911.02116",
"arxiv:2003.11080",
"arxiv:2009.05166",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | google | null | google/muril-base-cased | 7,640 | 9 | transformers | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
MuRIL: Multilingual Representations for Indian Languages
===
MuRIL is a BERT model pre-trained on 17 Indian languages and their transliterated counterparts. We have released the pre-trained model (with the MLM layer intact, enabli... | [
-0.1419440656900406,
-0.10690672695636749,
0.046251293271780014,
-0.001899879309348762,
-0.015651531517505646,
0.025571860373020172,
-0.03302827849984169,
-0.015630898997187614,
0.015291876159608364,
-0.04051760211586952,
-0.013755560852587223,
-0.1081051453948021,
0.053103066980838776,
0.... |
r3dhummingbird/DialoGPT-medium-joshua | ff22e98bcb70ae1e082f54640c5c3bafd3950125 | 2021-07-19T23:18:30.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
] | conversational | false | r3dhummingbird | null | r3dhummingbird/DialoGPT-medium-joshua | 7,633 | 12 | transformers | ---
thumbnail: https://raw.githubusercontent.com/RuolinZheng08/twewy-discord-chatbot/main/gif-demo/icon.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on ... | [
-0.07909061759710312,
-0.07169482856988907,
0.014899887144565582,
-0.028310146182775497,
0.02456045150756836,
-0.05610654503107071,
0.05983433872461319,
-0.003468582406640053,
0.04228248819708824,
-0.02380676008760929,
0.023029156029224396,
-0.07842514663934708,
-0.020067140460014343,
0.00... |
valhalla/distilbart-mnli-12-9 | 66a037d826920a2f84a9d83edcbeb23a0951ed2e | 2021-06-14T10:34:58.000Z | [
"pytorch",
"jax",
"bart",
"text-classification",
"dataset:mnli",
"transformers",
"distilbart",
"distilbart-mnli",
"zero-shot-classification"
] | zero-shot-classification | false | valhalla | null | valhalla/distilbart-mnli-12-9 | 7,612 | null | transformers | ---
datasets:
- mnli
tags:
- distilbart
- distilbart-mnli
pipeline_tag: zero-shot-classification
---
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingfa... | [
-0.0767815113067627,
-0.005045149940997362,
0.10137208551168442,
-0.0044491710141301155,
-0.08543753623962402,
-0.054864756762981415,
-0.06274072825908661,
0.012675730511546135,
-0.03593569993972778,
-0.08640600740909576,
0.03498023375868797,
-0.07564754039049149,
0.0690762847661972,
-0.06... |
sentence-transformers/roberta-large-nli-stsb-mean-tokens | 768fca01ac32ae924414f7128af28ea1d9dfcada | 2022-06-15T20:56:01.000Z | [
"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/roberta-large-nli-stsb-mean-tokens | 7,575 | 1 | sentence-transformers | ---
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... | [
-0.05386015400290489,
-0.06863757222890854,
-0.010540490970015526,
0.04522981494665146,
0.009709744714200497,
0.08176402002573013,
-0.027924794703722,
0.06319715082645416,
0.022827759385108948,
-0.06955612450838089,
0.037037938833236694,
0.02703072875738144,
0.0585457980632782,
0.071992367... |
charsiu/g2p_multilingual_byT5_small | 834df67c125a811e1a60fbf9f0f39503115437ea | 2022-05-19T05:02:14.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | charsiu | null | charsiu/g2p_multilingual_byT5_small | 7,545 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
microsoft/unixcoder-base | 02583b53b9290e674a43b6b74e89f98a71b2d22a | 2022-03-23T06:05:18.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | microsoft | null | microsoft/unixcoder-base | 7,437 | 4 | transformers | ---
license: apache-2.0
---
| [
0.04086383432149887,
0.04840587452054024,
-0.01111048087477684,
-0.0822305753827095,
0.03046034276485443,
-0.024620788171887398,
-0.00873124971985817,
-0.032080959528684616,
-0.009516960941255093,
0.014524046331644058,
0.06244279816746712,
-0.03306293115019798,
-0.057087719440460205,
-0.02... |
allenai/macaw-large | 57fd83e05c764b04c36650fac1458e9816f2d355 | 2021-09-21T15:59:44.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/macaw-large | 7,429 | 8 | transformers | ---
language: en
widget:
- text: $answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?
license: apache-2.0
---
# macaw-large
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing ro... | [
-0.08597929775714874,
0.01489941030740738,
0.006830783560872078,
0.031845081597566605,
0.019177068024873734,
-0.011971067637205124,
0.05538540706038475,
-0.01818736456334591,
0.045247260481119156,
-0.035805828869342804,
-0.008018245920538902,
-0.09603692591190338,
0.06987427920103073,
-0.0... |
microsoft/wavlm-large | c1423ed94bb01d80a3f5ce5bc39f6026a0f4828c | 2022-02-02T21:21:50.000Z | [
"pytorch",
"wavlm",
"feature-extraction",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.13900",
"transformers",
"speech"
] | feature-extraction | false | microsoft | null | microsoft/wavlm-large | 7,408 | 6 | transformers | ---
language:
- en
tags:
- speech
inference: false
---
# WavLM-Large
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model does not hav... | [
-0.03570942580699921,
-0.0924510806798935,
-0.026479028165340424,
-0.013604486361145973,
-0.04561544954776764,
-0.0009414275409653783,
-0.006420349236577749,
-0.05708598718047142,
-0.06575790792703629,
-0.0591592974960804,
-0.010695355013012886,
-0.040921926498413086,
-0.018516870215535164,
... |
cross-encoder/stsb-distilroberta-base | 2a387f03597b030ff3dadcef7d73456ce23e3bb7 | 2021-08-05T08:41:53.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/stsb-distilroberta-base | 7,400 | null | transformers | ---
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 [STS benchmark dataset]... | [
-0.05867493897676468,
-0.10378813743591309,
-0.054619889706373215,
0.01839994080364704,
-0.024363433942198753,
0.05517164617776871,
-0.018026946112513542,
-0.010603397153317928,
0.005246789660304785,
-0.09420166909694672,
0.07769262790679932,
-0.08185577392578125,
0.055262017995119095,
0.0... |
microsoft/BiomedVLP-CXR-BERT-general | 93af83cefc6d3f7d0ef9a0b78b0d579452c6a546 | 2022-07-11T14:52:52.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"arxiv:2204.09817",
"arxiv:2103.00020",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/BiomedVLP-CXR-BERT-general | 7,374 | 5 | transformers | ---
language: en
tags:
- exbert
license: mit
widget:
- text: "Left pleural effusion with adjacent [MASK]."
example_title: "Radiology 1"
- text: "Heart size normal and lungs are [MASK]."
example_title: "Radiology 2"
- text: "[MASK] is a tumor suppressor gene."
example_title: "Biomedical"
- text: "The patient was o... | [
-0.0701243132352829,
-0.0383235402405262,
0.05087171494960785,
0.0055551836267113686,
0.017142485827207565,
0.06022603437304497,
0.044871166348457336,
0.05579875782132149,
0.023235656321048737,
-0.05574702471494675,
-0.021382983773946762,
-0.05434630438685417,
0.04933801293373108,
0.106309... |
kykim/electra-kor-base | 8599418d72f5dcb21ae3972ba2405f88c819b195 | 2021-01-22T00:28:50.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"ko",
"transformers"
] | null | false | kykim | null | kykim/electra-kor-base | 7,372 | 1 | transformers | ---
language: ko
---
# Electra 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 ElectraTokenizerFast, ElectraModel
tokenize... | [
-0.07287156581878662,
0.0035638681147247553,
0.004675906151533127,
0.008773989044129848,
0.014719270169734955,
-0.019980445504188538,
0.015259988605976105,
-0.02824963629245758,
-0.021806946024298668,
-0.03495234623551369,
0.08799552172422409,
-0.046033311635255814,
0.06217537447810173,
-0... |
google/bert_uncased_L-6_H-768_A-12 | c132ecc85d3d73b460b741cc50aa9ed18446c335 | 2021-05-19T17:34:36.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-6_H-768_A-12 | 7,350 | null | transformers | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
-0.02777470275759697,
-0.02693094126880169,
0.07438826560974121,
0.03228488564491272,
-0.0023304771166294813,
0.018128493800759315,
-0.06253628432750702,
0.0994548574090004,
-0.014644814655184746,
0.018868697807192802,
-0.015814494341611862,
0.03585591912269592,
0.03645862638950348,
0.0455... |
allenai/unifiedqa-t5-base | 85413ec7c7b86263cade67192224aa5fc95838ac | 2021-06-23T11:17:21.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/unifiedqa-t5-base | 7,312 | 2 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
facebook/wmt19-en-de | b33976783993b11baabc19313275865ee87931e3 | 2020-12-11T21:39:55.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"en",
"de",
"dataset:wmt19",
"arxiv:1907.06616",
"transformers",
"translation",
"wmt19",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | facebook | null | facebook/wmt19-en-de | 7,310 | null | transformers | ---
language:
- en
- de
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/... | [
-0.08782295882701874,
-0.016733264550566673,
-0.03767985850572586,
-0.011975746601819992,
0.036806050688028336,
-0.027734335511922836,
0.011203650385141373,
0.06087851524353027,
-0.03000461682677269,
-0.039158303290605545,
0.08723749965429306,
-0.09838131070137024,
0.02246216870844364,
0.0... |
google/bigbird-base-trivia-itc | 29c5c29e0297ad7eb9b90ef69fecba71508f5ca4 | 2021-06-02T14:53:34.000Z | [
"pytorch",
"jax",
"big_bird",
"question-answering",
"en",
"dataset:trivia_qa",
"arxiv:2007.14062",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | google | null | google/bigbird-base-trivia-itc | 7,286 | 1 | transformers | ---
language: en
license: apache-2.0
datasets:
- trivia_qa
---
# BigBird base trivia-itc
This model is a fine-tune checkpoint of `bigbird-roberta-base`, fine-tuned on `trivia_qa` with `BigBirdForQuestionAnsweringHead` on its top.
Check out [this](https://colab.research.google.com/drive/1DVOm1VHjW0eKCayFq1N2GpY6GR9M4... | [
-0.07812393456697464,
-0.03843126818537712,
-0.04080774635076523,
0.006781372707337141,
-0.0025240425020456314,
-0.008470644243061543,
0.027732327580451965,
0.00809802021831274,
-0.07931428402662277,
-0.045450177043676376,
-0.036874476820230484,
-0.0020581819117069244,
-0.01221038494259119,
... |
Harveenchadha/vakyansh-wav2vec2-hindi-him-4200 | e2568c3f7868d8aa3aaabcf28fa100d10d54c170 | 2022-01-29T06:03:43.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hi",
"arxiv:2107.07402",
"transformers",
"audio",
"speech",
"license:mit",
"model-index"
] | automatic-speech-recognition | false | Harveenchadha | null | Harveenchadha/vakyansh-wav2vec2-hindi-him-4200 | 7,235 | 0 | transformers | ---
language: hi
#datasets:
#- Interspeech 2021
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: mit
model-index:
- name: Wav2Vec2 Vakyansh Hindi Model by Harveen Chadha
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Com... | [
-0.10541097819805145,
-0.08041687309741974,
-0.006981979124248028,
-0.04557544365525246,
-0.016843710094690323,
-0.004770498722791672,
0.014415543526411057,
-0.06555183231830597,
-0.0005171556840650737,
-0.04524217173457146,
0.05830507352948189,
-0.1093701720237732,
-0.010401604697108269,
... |
moussaKam/frugalscore_tiny_bert-base_bert-score | a487e5a875e63ef1f9cf6015a3a11be2d80aa550 | 2022-02-01T10:50:21.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"transformers"
] | text-classification | false | moussaKam | null | moussaKam/frugalscore_tiny_bert-base_bert-score | 7,234 | null | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
-0.042448315769433975,
-0.03151621297001839,
-0.04763283580541611,
0.07655563205480576,
0.02725154161453247,
-0.0008655411656945944,
-0.01697634719312191,
0.11361992359161377,
-0.013541421853005886,
0.03203555569052696,
0.038515813648700714,
0.010016776621341705,
0.025363873690366745,
-0.0... |
digitalepidemiologylab/covid-twitter-bert-v2 | b113bc3c2590d7b32ed62603fe1ebe32e1e5beee | 2021-09-22T08:20:06.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"transformers",
"Twitter",
"COVID-19",
"license:mit"
] | null | false | digitalepidemiologylab | null | digitalepidemiologylab/covid-twitter-bert-v2 | 7,203 | 2 | transformers | ---
language: en
thumbnail: https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png
tags:
- Twitter
- COVID-19
license: mit
---
# COVID-Twitter-BERT v2
## Model description
BERT-large-uncased model, pretrained on a corpus of messages from Twitter about C... | [
-0.07665276527404785,
-0.06651245057582855,
0.02727682515978813,
0.010750196874141693,
0.057945188134908676,
0.013190199621021748,
-0.006109526846557856,
0.06689181178808212,
0.05522686243057251,
-0.0054182554595172405,
0.000586212903726846,
0.011817611753940582,
0.0006026944029144943,
0.0... |
vinai/bertweet-large | 67477168d449ccc8abb725e2123a0d6e44f27f4b | 2022-06-08T04:43:57.000Z | [
"pytorch",
"tf",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vinai | null | vinai/bertweet-large | 7,183 | 2 | transformers | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... | [
-0.06569212675094604,
-0.08119648694992065,
0.05748256295919418,
0.04152612015604973,
0.0379173643887043,
0.05656646192073822,
-0.01779285818338394,
0.11296234279870987,
0.08408951014280319,
0.024929603561758995,
0.013933742418885231,
0.029724139720201492,
0.01992405392229557,
0.0383222699... |
ai4bharat/indic-bert | 97ae2d6440dbd1a2698540223dc00b43075c69c9 | 2021-04-12T09:06:47.000Z | [
"pytorch",
"albert",
"en",
"dataset:AI4Bharat IndicNLP Corpora",
"transformers",
"license:mit"
] | null | false | ai4bharat | null | ai4bharat/indic-bert | 7,147 | 12 | transformers | ---
language: en
license: mit
datasets:
- AI4Bharat IndicNLP Corpora
---
# IndicBERT
IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBER... | [
-0.04687028005719185,
-0.11748872697353363,
0.0018463223241269588,
-0.06609062850475311,
-0.04719531908631325,
0.07390031218528748,
-0.05439862608909607,
0.03235790878534317,
0.018545880913734436,
-0.025129908695816994,
0.01932387240231037,
-0.05290902033448219,
-0.00007568294677184895,
-0... |
svalabs/twitter-xlm-roberta-bitcoin-sentiment | 34915a8cf74b0ad061a6f383eded7aecd293f3e5 | 2022-05-12T09:28:14.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | false | svalabs | null | svalabs/twitter-xlm-roberta-bitcoin-sentiment | 7,139 | null | transformers | This model is mainly focussed on extracting the sentiment on tweets regarding bitcoin. The model was trained on manually on labeled data with rubrix (https://www.rubrix.ml/). The training set approximately contained 500 samples and 500 test samples. The cardiffnlp/twitter-xlm-roberta-base-sentiment (https://huggingface... | [
-0.05423995479941368,
-0.09167225658893585,
-0.08425907790660858,
0.04418861120939255,
0.040418367832899094,
0.05595405399799347,
-0.0268994327634573,
0.04370150342583656,
0.015512010082602501,
0.003817172721028328,
0.0047922320663928986,
0.04018882289528847,
0.060718975961208344,
-0.04440... |
jonatasgrosman/wav2vec2-large-xlsr-53-german | 934c45f3e6939b6b6d261b4c71ed2755810e7fe6 | 2022-07-27T23:37:37.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"transformers",
"audio",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"lice... | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-xlsr-53-german | 7,115 | 5 | transformers | ---
language: de
license: apache-2.0
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- de
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- robust-speech-event
- speech
- xlsr-fine-tuning-week
model-index:
- name: XLSR Wav2Vec2 ... | [
-0.08388983458280563,
-0.07693295180797577,
-0.04375056177377701,
-0.0904199481010437,
0.012585571967065334,
0.02896086685359478,
-0.01367014367133379,
0.0063398354686796665,
-0.019788114354014397,
-0.0690489113330841,
-0.009659511968493462,
-0.16389170289039612,
-0.021237393841147423,
-0.... |
deepset/bert-small-mm_retrieval-question_encoder | a34edf571667cc1ba38cec55c56f2905f13336a2 | 2021-10-19T15:51:37.000Z | [
"pytorch",
"dpr",
"feature-extraction",
"transformers"
] | feature-extraction | false | deepset | null | deepset/bert-small-mm_retrieval-question_encoder | 7,099 | null | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large | 160deb78aca30f63754e512a93337ce8013a32ca | 2021-06-20T19:03:02.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nreimers | null | nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large | 7,093 | 6 | transformers | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) | [
-0.04895520955324173,
0.02276579663157463,
-0.07000173628330231,
0.036097876727581024,
0.042695432901382446,
0.02520260028541088,
-0.0600503534078598,
-0.0007676688255742192,
0.0047691743820905685,
0.015759311616420746,
0.06056235358119011,
0.00046843758900649846,
0.00011801968503277749,
0... |
nvidia/segformer-b0-finetuned-ade-512-512 | 677af011c308b27a94d3ec6098c86c31c4fb6e7d | 2022-07-20T09:52:37.000Z | [
"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-b0-finetuned-ade-512-512 | 7,091 | 7 | transformers | ---
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_... | [
-0.07330463081598282,
0.03187301754951477,
0.05381572246551514,
-0.03394179046154022,
0.04922076314687729,
-0.16568075120449066,
-0.016254644840955734,
0.03506134822964668,
-0.08561974763870239,
-0.08930863440036774,
0.020988473668694496,
-0.06830696761608124,
-0.0075541045516729355,
-0.00... |
flaubert/flaubert_small_cased | 21a2d6f46294ad07a0b692d96af443990c07f790 | 2021-05-19T16:56:07.000Z | [
"pytorch",
"flaubert",
"fill-mask",
"fr",
"dataset:flaubert",
"transformers",
"bert",
"language-model",
"flue",
"french",
"flaubert-small",
"cased",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | flaubert | null | flaubert/flaubert_small_cased | 7,078 | 1 | transformers | ---
language: fr
license: mit
datasets:
- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- flaubert-small
- cased
---
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. Models of di... | [
-0.11573060601949692,
-0.11729912459850311,
-0.002388942753896117,
-0.0051825628615915775,
0.035848814994096756,
0.025654267519712448,
-0.02048722468316555,
0.11406595259904861,
0.027327295392751694,
-0.007826564833521843,
-0.023103196173906326,
0.016238121315836906,
0.026047026738524437,
... |
esiebomajeremiah/autonlp-email-classification-657119381 | 484ba1babc3906d77331d95c1587aea7f3683637 | 2022-03-22T13:57:29.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:esiebomajeremiah/autonlp-data-email-classification",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | esiebomajeremiah | null | esiebomajeremiah/autonlp-email-classification-657119381 | 7,026 | null | transformers | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- esiebomajeremiah/autonlp-data-email-classification
co2_eq_emissions: 3.516233232503715
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 657119381
- CO2 Emissions (in grams): 3.516233232503715
## Validati... | [
-0.10065767914056778,
0.04212992638349533,
-0.023528600111603737,
-0.006394938565790653,
0.06634734570980072,
0.023258021101355553,
0.044618431478738785,
0.011422238312661648,
0.010949777439236641,
-0.03354537859559059,
-0.019860055297613144,
-0.1500413864850998,
-0.03853297606110573,
0.00... |
HooshvareLab/bert-fa-base-uncased | a04aa40c97bcdde570ae11986a534542c2995a62 | 2021-05-18T21:02:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"fa",
"arxiv:2005.12515",
"transformers",
"bert-fa",
"bert-persian",
"persian-lm",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | HooshvareLab | null | HooshvareLab/bert-fa-base-uncased | 7,008 | 2 | transformers | ---
language: fa
tags:
- bert-fa
- bert-persian
- persian-lm
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT i... | [
-0.1138458251953125,
-0.10975673049688339,
0.037766698747873306,
-0.01900535263121128,
-0.01892690919339657,
-0.01897917315363884,
-0.020216507837176323,
0.048232510685920715,
-0.009319653734564781,
-0.023134777322411537,
0.031493332237005234,
0.04040331393480301,
0.04310895502567291,
0.00... |
cross-encoder/nli-roberta-base | 1c9dadfb1d7bcaac49176fd3a5de914f6ae2bd42 | 2021-08-05T08:41:05.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"transformers",
"roberta-base",
"license:apache-2.0",
"zero-shot-classification"
] | zero-shot-classification | false | cross-encoder | null | cross-encoder/nli-roberta-base | 6,989 | 3 | transformers | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- roberta-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/examples/appl... | [
-0.05067457631230354,
-0.10063932836055756,
-0.04871303215622902,
-0.03833722323179245,
0.057528261095285416,
0.09458030015230179,
-0.05522534251213074,
0.0060033309273421764,
-0.017189867794513702,
-0.07683423161506653,
0.014681501314043999,
-0.09541838616132736,
-0.008427686989307404,
-0... |
klue/roberta-base | 67dd433d36ebc66a42c9aaa85abcf8d2620e41d9 | 2021-10-20T16:10:25.000Z | [
"pytorch",
"roberta",
"fill-mask",
"ko",
"arxiv:2105.09680",
"transformers",
"korean",
"klue",
"autotrain_compatible"
] | fill-mask | false | klue | null | klue/roberta-base | 6,986 | null | transformers | ---
language: ko
tags:
- korean
- klue
mask_token: "[MASK]"
widget:
- text: 대한민국의 수도는 [MASK] 입니다.
---
# KLUE RoBERTa base
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 ... | [
-0.15588511526584625,
-0.023237166926264763,
0.0621400810778141,
0.01157368067651987,
-0.047475218772888184,
0.06339114904403687,
0.023674119263887405,
0.034490447491407394,
0.0009868412744253874,
-0.018254712224006653,
-0.014493593946099281,
-0.06607455760240555,
0.0264532919973135,
0.051... |
facebook/wmt19-de-en | 80d366f635721148ffa2a0a58591cb672c9b4982 | 2020-12-11T21:39:51.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"de",
"en",
"dataset:wmt19",
"arxiv:1907.06616",
"transformers",
"translation",
"wmt19",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | facebook | null | facebook/wmt19-de-en | 6,979 | null | transformers | ---
language:
- de
- 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/... | [
-0.08737433701753616,
-0.014936153776943684,
-0.03332792595028877,
-0.014402315020561218,
0.03474336862564087,
-0.033070676028728485,
0.013921699486672878,
0.06004323810338974,
-0.032337263226509094,
-0.03714103624224663,
0.08474110811948776,
-0.09947549551725388,
0.01866295374929905,
0.00... |
HooshvareLab/bert-fa-zwnj-base | 3880fac085e1a338e9564907cba0adeb9e14bc72 | 2021-05-18T21:05:42.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"fa",
"arxiv:2005.12515",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | HooshvareLab | null | HooshvareLab/bert-fa-zwnj-base | 6,937 | 3 | transformers | ---
language: fa
license: apache-2.0
---
# ParsBERT (v3.0)
A Transformer-based Model for Persian Language Understanding
The new version of BERT v3.0 for Persian is available today and can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new ... | [
-0.11249449849128723,
-0.02549499273300171,
0.014047556556761265,
-0.015234151855111122,
-0.04962414503097534,
-0.022687125951051712,
-0.042220745235681534,
0.04880340397357941,
0.01373323518782854,
-0.041855983436107635,
0.015459089539945126,
0.06377378106117249,
0.03782154247164726,
-0.0... |
gogamza/kobart-base-v2 | d9a1f640896cef8dcfd693b1bc57510a2b09a18f | 2021-11-11T07:43:35.000Z | [
"pytorch",
"bart",
"feature-extraction",
"ko",
"transformers",
"license:mit"
] | feature-extraction | false | gogamza | null | gogamza/kobart-base-v2 | 6,910 | 3 | transformers | ---
language: ko
tags:
- bart
license: mit
---
## KoBART-base-v2
With the addition of chatting data, the model is trained to handle the semantics of sequences longer than KoBART.
```python
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart... | [
-0.11324606090784073,
-0.014094129204750061,
0.02366548217833042,
-0.033351730555295944,
-0.05986827611923218,
-0.022683126851916313,
-0.03376873955130577,
0.04126504063606262,
0.0318809375166893,
-0.07791490107774734,
0.04523751884698868,
-0.04740383103489876,
-0.021599793806672096,
0.006... |
Helsinki-NLP/opus-mt-tr-en | 3252b40d8b9dead8012364425fd00db1a26abf85 | 2021-09-11T10:49:35.000Z | [
"pytorch",
"marian",
"text2text-generation",
"tr",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tr-en | 6,901 | 9 | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-tr-en
* source languages: tr
* target languages: en
* OPUS readme: [tr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tr-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.053958091884851456,
-0.03019360825419426,
0.022831706330180168,
-0.007792068179696798,
0.0034690347965806723,
0.09389012306928635,
-0.04958104342222214,
0.02943223901093006,
0.014684055931866169,
-0.014786768704652786,
0.012111724354326725,
-0.04121154174208641,
-0.07839208841323853,
-0... |
google/bigbird-pegasus-large-bigpatent | 623321f538339e475269fdf79a258a5a7b796f4c | 2021-06-03T18:26:21.000Z | [
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"en",
"dataset:big_patent",
"arxiv:2007.14062",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | google | null | google/bigbird-pegasus-large-bigpatent | 6,873 | 7 | transformers | ---
language: en
license: apache-2.0
datasets:
- big_patent
tags:
- summarization
---
# BigBirdPegasus model (large)
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the ca... | [
-0.10371853411197662,
0.01022668369114399,
-0.00922483205795288,
-0.03300868347287178,
-0.01656779646873474,
0.002154112793505192,
-0.10391688346862793,
0.0386798121035099,
0.03212219849228859,
-0.00821114331483841,
-0.05011751130223274,
0.05463859811425209,
-0.04795999079942703,
-0.004013... |
dmis-lab/biobert-large-cased-v1.1-squad | 2b17f30cda1efcbe0d6ab3b977856c7898f934b1 | 2021-05-19T16:01:47.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | dmis-lab | null | dmis-lab/biobert-large-cased-v1.1-squad | 6,856 | 2 | transformers | Entry not found | [
0.0461147278547287,
-0.038838207721710205,
-0.01049656979739666,
-0.03682169318199158,
0.011261860840022564,
0.013094935566186905,
0.0019101888174191117,
-0.013979103416204453,
0.027092741802334785,
-0.015212527476251125,
0.017284274101257324,
-0.08189476281404495,
0.03817418962717056,
-0.... |
naver-clova-ocr/bros-base-uncased | 0f0e83a58cde75af72e331e6a018cd5bc7ccab31 | 2022-04-05T13:56:46.000Z | [
"pytorch",
"bros",
"arxiv:2108.04539",
"transformers"
] | null | false | naver-clova-ocr | null | naver-clova-ocr/bros-base-uncased | 6,843 | 1 | transformers | # BROS
GitHub: https://github.com/clovaai/bros
## Introduction
BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents.<br>
Given the OCR results of the document image, which are text and bounding box pairs, it can perform var... | [
-0.09032468497753143,
0.06349515914916992,
0.07526274025440216,
-0.0024768831208348274,
0.03415302187204361,
0.04946291446685791,
0.0275509562343359,
0.015108099207282066,
0.0287735927850008,
-0.02269292064011097,
0.010489358566701412,
0.08855713158845901,
0.02995944768190384,
0.0427544601... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.