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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sileod/roberta-base-mnli | 86d5eb9545d2276806ce7290e670134a65e95e84 | 2022-05-31T10:08:10.000Z | [
"pytorch",
"roberta",
"text-classification",
"dataset:multi_nli",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | sileod | null | sileod/roberta-base-mnli | 516 | 1 | transformers | ---
license: mit
tags:
- generated_from_trainer
datasets:
- multi_nli
metrics:
- accuracy
model-index:
- name: roberta-base-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: multi_nli
type: multi_nli
args: default
metrics:
- name: Accu... | [
-0.09037130326032639,
-0.07013512402772903,
-0.03151529282331467,
0.027212219312787056,
-0.00531049957498908,
0.027758922427892685,
-0.005905041005462408,
0.003558073192834854,
-0.03066815808415413,
-0.07203687727451324,
0.0008921112166717649,
-0.11552464216947556,
0.0241527259349823,
-0.0... |
nvidia/mit-b2 | 44acc700d01cdfdac6f5c236e69da847985eaac3 | 2022-07-29T13:15:51.000Z | [
"pytorch",
"tf",
"segformer",
"image-classification",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | nvidia | null | nvidia/mit-b2 | 515 | null | transformers | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet_1k
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_val_00000002.jpg
exampl... | [
-0.07976414263248444,
0.00992726068943739,
0.039033371955156326,
-0.024457991123199463,
0.07042194902896881,
-0.13668008148670197,
-0.012205728329718113,
-0.027800124138593674,
-0.08618300408124924,
-0.09576070308685303,
0.02076590433716774,
-0.06689205765724182,
0.008827334269881248,
0.00... |
prajjwal1/bert-mini-mnli | 2793a188a2d6f995f9e6a5f73d9dd8b7a3a3aaa6 | 2021-10-05T17:57:20.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers"
] | text-classification | false | prajjwal1 | null | prajjwal1/bert-mini-mnli | 515 | null | transformers | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... | [
-0.09788774698972702,
-0.06514044851064682,
0.04305513948202133,
0.053854115307331085,
0.026423117145895958,
0.050810474902391434,
-0.0007797605358064175,
0.016668150201439857,
-0.04501102864742279,
-0.012287932448089123,
0.006685130298137665,
0.038580089807510376,
0.021358292549848557,
0.... |
Biasface/DDDC | 4481ffe566e96900e4b4e4df6ebc815524295bbf | 2021-11-30T17:30:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Biasface | null | Biasface/DDDC | 513 | null | transformers | ---
tags:
- conversational
---
#hi | [
-0.05905907228589058,
0.048782773315906525,
0.04151437059044838,
-0.01576641947031021,
0.04149341583251953,
-0.07731932401657104,
0.17356204986572266,
-0.009053856134414673,
0.04095043987035751,
-0.03962145373225212,
-0.021752478554844856,
0.010714013129472733,
-0.0019252103520557284,
-0.0... |
studio-ousia/mluke-base | 0f3c9dc42873eaf0e807bd2736bc4cfbe73de3b2 | 2022-03-11T02:58:43.000Z | [
"pytorch",
"luke",
"fill-mask",
"multilingual",
"transformers",
"named entity recognition",
"relation classification",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | studio-ousia | null | studio-ousia/mluke-base | 513 | 3 | transformers | ---
language: multilingual
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- relation classification
- question answering
license: apache-2.0
---
## mLUKE
**mLUKE** (multilingual LUKE) is a multilingual extension of LUKE.
Please check... | [
-0.07457901537418365,
-0.12971004843711853,
0.003333415137603879,
-0.03782671317458153,
-0.00697191059589386,
0.028948567807674408,
-0.06497997790575027,
0.008090963587164879,
0.05358700826764107,
-0.022625990211963654,
0.04569784179329872,
-0.06592000275850296,
0.10006295889616013,
-0.028... |
DeepPavlov/distilrubert-small-cased-conversational | e348066b4a7279b97138038299bddc6580a9169a | 2022-06-28T17:19:09.000Z | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | false | DeepPavlov | null | DeepPavlov/distilrubert-small-cased-conversational | 513 | null | transformers | ---
language:
- ru
---
# distilrubert-small-cased-conversational
Conversational DistilRuBERT-small \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversatio... | [
-0.05675916746258736,
-0.04455617815256119,
0.06875159591436386,
-0.02032424323260784,
0.006696554832160473,
0.0012442867737263441,
0.07231743633747101,
0.017815973609685898,
0.06355373561382294,
-0.07124082744121552,
0.006907591130584478,
-0.05098576471209526,
0.009283230639994144,
0.0323... |
tner/xlm-roberta-large-uncased-wnut2017 | d2f13491ebb59b477fa61dc0224d88daf851513f | 2021-02-13T00:12:33.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/xlm-roberta-large-uncased-wnut2017 | 512 | null | transformers | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-large-uncased-wnut2017")
mo... | [
-0.14381316304206848,
0.0026198197156190872,
-0.052381113171577454,
-0.024082478135824203,
-0.05509265139698982,
0.049844320863485336,
-0.007269842550158501,
0.09843369573354721,
-0.03553507849574089,
-0.015976563096046448,
0.035285934805870056,
-0.02778131701052189,
0.04897482320666313,
0... |
huggingface/distilbert-base-uncased-finetuned-mnli | 0fadb1fe60cd119b3af82e2bf9cb98a59336d7bc | 2021-02-25T20:27:07.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | huggingface | null | huggingface/distilbert-base-uncased-finetuned-mnli | 512 | 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.... |
SIKU-BERT/sikuroberta | bb25260d5c321924fe4fb353c09191c0aaf5c5c6 | 2021-09-22T00:22:36.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"roberta",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | SIKU-BERT | null | SIKU-BERT/sikuroberta | 511 | 2 | transformers | ---
language:
- "zh"
thumbnail: "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "roberta"
- "pytorch"
inference: false
license: "apache-2.0"
---
# SikuBERT
## Model description
![SikuBERT](htt... | [
-0.09843884408473969,
0.0034977104514837265,
0.011986086145043373,
-0.020155439153313637,
0.021541854366660118,
0.03045913577079773,
0.007536630611866713,
0.020337073132395744,
0.012610376812517643,
0.058354489505290985,
0.0647602379322052,
-0.0037606870755553246,
-0.01179917249828577,
0.0... |
Helsinki-NLP/opus-mt-ru-fr | 55c73236818495c7a6dd5a98e3529de3481bc3ae | 2021-09-10T14:02:31.000Z | [
"pytorch",
"jax",
"marian",
"text2text-generation",
"ru",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ru-fr | 510 | null | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ru-fr
* source languages: ru
* target languages: fr
* OPUS readme: [ru-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.0556146577000618,
-0.033930592238903046,
0.012328037992119789,
-0.0008930727490223944,
0.01464043278247118,
0.10105009377002716,
-0.04525613784790039,
0.040750328451395035,
0.0316774882376194,
-0.01470259390771389,
-0.00027723939274437726,
-0.0356462225317955,
-0.06492092460393906,
-0.0... |
KoichiYasuoka/chinese-roberta-base-upos | 2fcc4e89732370e30451b65e5a7227c78811f0d4 | 2022-02-11T06:28:59.000Z | [
"pytorch",
"bert",
"token-classification",
"zh",
"dataset:universal_dependencies",
"transformers",
"chinese",
"pos",
"wikipedia",
"dependency-parsing",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/chinese-roberta-base-upos | 510 | 2 | transformers | ---
language:
- "zh"
tags:
- "chinese"
- "token-classification"
- "pos"
- "wikipedia"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
---
# chinese-roberta-base-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts... | [
-0.14319512248039246,
-0.020101333037018776,
-0.00716145196929574,
-0.01455288752913475,
-0.006295858882367611,
-0.0054800608195364475,
0.06915377825498581,
0.041105203330516815,
-0.030071714892983437,
-0.014979204162955284,
0.052228692919015884,
-0.03892308473587036,
0.005476171616464853,
... |
anton-l/wav2vec2-base-superb-sv | 0a1a74d00d5e44dbd7344b65c9847a1eb625c73b | 2021-12-14T12:49:10.000Z | [
"pytorch",
"wav2vec2",
"audio-xvector",
"transformers"
] | null | false | anton-l | null | anton-l/wav2vec2-base-superb-sv | 510 | 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.... |
Davlan/bert-base-multilingual-cased-finetuned-yoruba | 000f80b4509f73bca9a33f9db0573d6f67396a12 | 2022-06-27T11:50:30.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"yo",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Davlan | null | Davlan/bert-base-multilingual-cased-finetuned-yoruba | 509 | null | transformers | Hugging Face's logo
---
language: yo
datasets:
---
# bert-base-multilingual-cased-finetuned-yoruba
## Model description
**bert-base-multilingual-cased-finetuned-yoruba** is a **Yoruba BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Yorùbá language texts. It provides **better performance... | [
-0.10520040988922119,
-0.035281725227832794,
0.04643205925822258,
-0.037845510989427567,
0.007347824051976204,
-0.03750757873058319,
-0.014329048804938793,
0.05323905497789383,
0.025369666516780853,
-0.047418929636478424,
-0.03624262660741806,
-0.07989465445280075,
-0.005046479869633913,
0... |
facebook/wav2vec2-xls-r-1b | 6d8fad78d7d9c252adfdf48da029590b21f47414 | 2021-11-18T16:32:35.000Z | [
"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-1b | 509 | 10 | transformers | ---
language: multilingual
datasets:
- common_voice
- multilingual_librispeech
tags:
- speech
- xls_r
- xls_r_pretrained
license: apache-2.0
---
# Wav2Vec2-XLS-R-1B
[Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) counting **1 billion** parameters.
!... | [
-0.03314581140875816,
-0.08428647369146347,
-0.03974233195185661,
-0.035290710628032684,
0.0025936767924576998,
0.004015838261693716,
-0.06183074042201042,
-0.02033969759941101,
0.025888223201036453,
-0.036623790860176086,
-0.01788884587585926,
-0.10040286928415298,
0.0011741240741685033,
... |
oliverguhr/spelling-correction-english-base | a30d76e2e7de0b0b350304c8e17cef99da8eb8e7 | 2022-06-13T12:09:01.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"en",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | oliverguhr | null | oliverguhr/spelling-correction-english-base | 509 | 2 | transformers | ---
language:
- en
license: mit
widget:
- text: "lets do a comparsion"
example_title: "1"
- text: "Their going to be here so0n"
example_title: "2"
- text: "ze shop is cloed due to covid 19"
example_title: "3"
metrics:
- cer
---
This is an experimental model that should fix your typos and punctuation.
If you like... | [
-0.08815503865480423,
-0.0013567091664299369,
0.07396401464939117,
-0.009503952227532864,
-0.10363280028104782,
0.01203787513077259,
0.024313826113939285,
0.03570683300495148,
-0.01175147108733654,
-0.05308513343334198,
0.05255596712231636,
-0.04645258188247681,
0.08839897811412811,
-0.027... |
SEBIS/code_trans_t5_base_code_documentation_generation_python | f42aaecddfc35f12575e9c887ee79cf3d6cdb97d | 2021-06-23T04:43:22.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_code_documentation_generation_python | 508 | null | transformers | ---
tags:
- summarization
widget:
- text: "def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )"
---
# CodeTrans model for code documentation generation python
Pretrained model on programming language python using the t5 base model architect... | [
-0.09217101335525513,
0.008948364295065403,
-0.002516689710319042,
0.03962847590446472,
0.024057749658823013,
-0.05637236684560776,
-0.008672524243593216,
0.020761728286743164,
0.015833068639039993,
-0.044282227754592896,
-0.006656281184405088,
-0.0072817872278392315,
0.0659097209572792,
0... |
indobenchmark/indobert-large-p2 | 4b280c3bfcc1ed2d6b4589be5c876076b7d73568 | 2021-05-19T20:28:22.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"transformers",
"indobert",
"indobenchmark",
"indonlu",
"license:mit"
] | feature-extraction | false | indobenchmark | null | indobenchmark/indobert-large-p2 | 508 | null | transformers | ---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT Large Model (phase2 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a m... | [
-0.05553301423788071,
-0.07608026266098022,
0.04833946377038956,
-0.004947134293615818,
-0.037574402987957,
0.11484593898057938,
0.0025740531273186207,
-0.0064101372845470905,
0.051521532237529755,
0.016718853265047073,
0.04813005402684212,
-0.08794326335191727,
-0.04469331353902817,
0.002... |
kamalkraj/bioelectra-base-discriminator-pubmed-pmc-lt | d807405696fdace62f42841dc06289d2354e1158 | 2021-06-10T14:22:08.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | kamalkraj | null | kamalkraj/bioelectra-base-discriminator-pubmed-pmc-lt | 508 | 2 | transformers | ## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that ada... | [
-0.048747170716524124,
-0.04893111437559128,
-0.015514216385781765,
-0.03868996724486351,
-0.004656575154513121,
0.015299046412110329,
-0.031510304659605026,
0.006487579550594091,
0.013422035612165928,
-0.015993250533938408,
-0.0350366085767746,
-0.01311967521905899,
-0.03418135643005371,
... |
facebook/regnet-y-040 | 40577f588ce4b8b3a306e59b93b117047e0a6625 | 2022-06-30T18:56:14.000Z | [
"pytorch",
"tf",
"regnet",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2003.13678",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/regnet-y-040 | 508 | null | transformers | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: htt... | [
-0.09716660529375076,
0.007682012394070625,
0.014121271669864655,
0.023276086896657944,
0.06860209256410599,
-0.05333618447184563,
-0.014848208986222744,
-0.01589207723736763,
-0.011465679854154587,
-0.030643023550510406,
0.06083591654896736,
-0.012407905422151089,
0.05800802633166313,
0.0... |
cross-encoder/quora-roberta-base | 195493c8767e7155c449e9ff7e64890d116d432d | 2021-08-05T08:41:36.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/quora-roberta-base | 507 | 1 | 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 [Quora Duplicate Questi... | [
-0.06531592458486557,
-0.09562886506319046,
-0.08294804394245148,
0.003444195259362459,
-0.07722270488739014,
0.039460305124521255,
-0.019080625846982002,
-0.00024401306291110814,
-0.0006181865464895964,
-0.08193150162696838,
0.0601973682641983,
-0.07962110638618469,
0.06383474916219711,
-... |
valurank/distilroberta-news-small | dad826d1ce6732850428d4673ff50835c8f7f59b | 2022-06-08T20:45:50.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:valurank/news-small",
"transformers",
"license:other"
] | text-classification | false | valurank | null | valurank/distilroberta-news-small | 507 | null | transformers | ---
license: other
language: en
datasets:
- valurank/news-small
---
# DistilROBERTA fine-tuned for news classification
This model is based on [distilroberta-base](https://huggingface.co/distilroberta-base) pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medi... | [
0.028654946014285088,
-0.043840158730745316,
-0.0007216497324407101,
0.03276117146015167,
-0.018651634454727173,
0.026930686086416245,
-0.020677220076322556,
-0.008208226412534714,
-0.01945236697793007,
-0.06835045665502548,
-0.04772130772471428,
0.02958497405052185,
0.03375142440199852,
-... |
Milos/slovak-gpt-j-1.4B | 1ca9a664fba18d050377579e43b92897efca62d4 | 2022-02-17T14:29:47.000Z | [
"pytorch",
"gptj",
"text-generation",
"sk",
"arxiv:2104.09864",
"transformers",
"Slovak GPT-J",
"causal-lm",
"license:gpl-3.0"
] | text-generation | false | Milos | null | Milos/slovak-gpt-j-1.4B | 506 | null | transformers | ---
language:
- sk
tags:
- Slovak GPT-J
- pytorch
- causal-lm
license: gpl-3.0
---
# Slovak GPT-J-1.4B
Slovak GPT-J-1.4B with the whopping `1,415,283,792` parameters is the latest and the largest model released in Slovak GPT-J series. Smaller variants, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M... | [
-0.08281024545431137,
-0.01994326524436474,
-0.062316905707120895,
-0.01194529328495264,
0.05133694037795067,
-0.028919586911797523,
-0.027500825002789497,
0.11036471277475357,
-0.015410039573907852,
-0.07324817776679993,
0.018006818369030952,
0.02508479170501232,
-0.03810977190732956,
0.0... |
SIKU-BERT/sikubert | fc656de2d6bde33919102dd3abe31c843f42226a | 2021-09-13T13:34:40.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"roberta",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | SIKU-BERT | null | SIKU-BERT/sikubert | 506 | 2 | transformers | ---
language:
- "zh"
thumbnail: "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "roberta"
- "pytorch"
inference: false
license: "apache-2.0"
---
# SikuBERT
## Model description
![SikuBERT](htt... | [
-0.09843884408473969,
0.0034977104514837265,
0.011986086145043373,
-0.020155439153313637,
0.021541854366660118,
0.03045913577079773,
0.007536630611866713,
0.020337073132395744,
0.012610376812517643,
0.058354489505290985,
0.0647602379322052,
-0.0037606870755553246,
-0.01179917249828577,
0.0... |
google/owlvit-base-patch32 | 4641e344cbbe8e25e0f2ab4e7e53372091ef9cfd | 2022-07-21T10:49:01.000Z | [
"pytorch",
"owlvit",
"transformers"
] | null | false | google | null | google/owlvit-base-patch32 | 506 | 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.... |
boychaboy/SNLI_distilbert-base-cased | fabefe1f7390d5aecf5d152e13da5998eee2e84d | 2021-05-10T17:08:47.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | boychaboy | null | boychaboy/SNLI_distilbert-base-cased | 505 | 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/unispeech-sat-base-plus-sv | a492b4bf41b1bd2fa6e6d07c6eae573b3f711b66 | 2021-12-17T13:56:17.000Z | [
"pytorch",
"unispeech-sat",
"audio-xvector",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"transformers",
"speech"
] | null | false | microsoft | null | microsoft/unispeech-sat-base-plus-sv | 505 | null | transformers | ---
language:
- en
tags:
- speech
---
# UniSpeech-SAT-Base for Speaker Verification
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and... | [
-0.05743613466620445,
-0.0897461548447609,
-0.04606340453028679,
0.0000587551730859559,
-0.02397315949201584,
0.04164471849799156,
-0.04461126774549484,
-0.0756264477968216,
-0.06988972425460815,
-0.020344486460089684,
-0.055797673761844635,
-0.028410715982317924,
-0.0027440532576292753,
0... |
nkoh01/MSRoberta | 3ff20e811ea95572470d3538cad29e816f05d7f4 | 2021-05-20T18:51:20.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nkoh01 | null | nkoh01/MSRoberta | 505 | null | transformers | # MSRoBERTa
Fine-tuned RoBERTa MLM model for [`Miscrosoft Sentence Completion Challenge`](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR_SCCD.pdf). This model case-sensitive following the `Roberta-base` model.
# Model description (taken from: [here](https://huggingface.co/roberta-base))
RoBE... | [
-0.05911831185221672,
-0.09170519560575485,
0.00720212934538722,
0.09317266196012497,
0.019343744963407516,
0.0704764649271965,
-0.009064627811312675,
-0.02024911344051361,
0.06894007325172424,
-0.014651037752628326,
0.007605264429003,
0.0003830238420050591,
0.06960557401180267,
0.03321130... |
fmikaelian/camembert-base-fquad | 341bf4683d9388a0a4022ce4062283255dc9246c | 2020-12-11T21:40:08.000Z | [
"pytorch",
"camembert",
"question-answering",
"fr",
"transformers",
"autotrain_compatible"
] | question-answering | false | fmikaelian | null | fmikaelian/camembert-base-fquad | 504 | 1 | transformers | ---
language: fr
---
# camembert-base-fquad
## Description
A baseline model for question-answering in french ([CamemBERT](https://camembert-model.fr/) model fine-tuned on [FQuAD](https://fquad.illuin.tech/))
## Training hyperparameters
```shell
python3 ./examples/question-answering/run_squad.py \
--m... | [
-0.039192166179418564,
-0.05508130043745041,
-0.046833012253046036,
0.03363977000117302,
0.028535712510347366,
0.0010800410527735949,
-0.047614142298698425,
0.11151213943958282,
-0.06330958753824234,
-0.08621683716773987,
0.029097702354192734,
-0.08578243106603622,
0.02208751067519188,
-0.... |
lanwuwei/GigaBERT-v3-Arabic-and-English | ee5c781756946364d989e0102b91b4a15390f6ac | 2021-05-19T00:17:42.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"ar",
"dataset:gigaword",
"dataset:oscar",
"dataset:wikipedia",
"transformers"
] | feature-extraction | false | lanwuwei | null | lanwuwei/GigaBERT-v3-Arabic-and-English | 504 | null | transformers | ---
language:
- en
- ar
datasets:
- gigaword
- oscar
- wikipedia
---
## GigaBERT-v3
GigaBERT-v3 is a customized bilingual BERT for English and Arabic. It was pre-trained in a large-scale corpus (Gigaword+Oscar+Wikipedia) with ~10B tokens, showing state-of-the-art zero-shot transfer performance from English to Arabic o... | [
-0.10906994342803955,
-0.010139446705579758,
0.04155832901597023,
0.006737437564879656,
-0.015256146900355816,
0.022861745208501816,
0.04719800502061844,
-0.058694418519735336,
0.01469008345156908,
-0.008443709462881088,
0.012695878744125366,
-0.011923509649932384,
0.05699107050895691,
-0.... |
orai-nlp/ElhBERTeu | 8d4de0a5d8c49f260010d5ea239afe77de31cfe2 | 2022-07-06T10:21:53.000Z | [
"pytorch",
"bert",
"feature-extraction",
"eu",
"transformers",
"basque",
"euskara",
"license:cc-by-4.0"
] | feature-extraction | false | orai-nlp | null | orai-nlp/ElhBERTeu | 502 | 0 | transformers | ---
license: cc-by-4.0
language: eu
tags:
- bert
- basque
- euskara
---
# ElhBERTeu
This is a BERT model for Basque introduced in [BasqueGLUE: A Natural Language Understanding Benchmark for Basque]().
To train ElhBERTeu, we collected different corpora sources from several domains: updated (2021) national and local ... | [
0.015789227560162544,
-0.06720083951950073,
0.03678123280405998,
0.011217808350920677,
0.036854881793260574,
0.03929118067026138,
-0.027999140322208405,
0.05031074583530426,
0.06369110196828842,
0.013771479949355125,
0.030120527371764183,
-0.01533704437315464,
0.035800401121377945,
0.02700... |
dhtocks/Named-Entity-Recognition | c9eb2cb284b0b69709132d19eeac3816ceb89c5b | 2022-01-15T11:22:33.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dhtocks | null | dhtocks/Named-Entity-Recognition | 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.... |
anas-awadalla/splinter-large-finetuned-squad | 36015d000da8055edcfbbf0a14c6f5d31a2e837c | 2022-05-15T10:51:43.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-finetuned-squad | 500 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this co... | [
-0.04910530149936676,
-0.04931975156068802,
-0.025724811479449272,
0.034278079867362976,
0.0036073727533221245,
0.025438200682401657,
-0.033705368638038635,
0.006786467973142862,
-0.07663004100322723,
-0.05717922002077103,
0.09333457052707672,
-0.03618541359901428,
0.021905414760112762,
0.... |
STAM/agricore | b6dfd05bfdcb097a78e563599517f8441452b404 | 2022-06-01T14:24:16.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | STAM | null | STAM/agricore | 500 | null | transformers | ---
license: mit
---
| [
-0.09818281978368759,
-0.010856573469936848,
0.052169445902109146,
-0.08761013299226761,
0.051318615674972534,
0.008416811004281044,
0.0449553020298481,
-0.011573160998523235,
0.020761393010616302,
-0.014396079815924168,
0.019734712317585945,
-0.01053137332201004,
-0.008089784532785416,
-0... |
TofuBoy/DialoGPT-medium-boon | cd59807e12d63621addb6c915273fe8621ba6145 | 2022-01-23T05:46:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | TofuBoy | null | TofuBoy/DialoGPT-medium-boon | 499 | null | transformers | ---
tags:
- conversational
---
# Boon Bot DialoGPT Model | [
-0.11100736260414124,
-0.03655783459544182,
0.03126463666558266,
-0.014288733713328838,
0.03909830003976822,
-0.0721946656703949,
0.09115498512983322,
0.05078687518835068,
0.03297612443566322,
0.0071451314724981785,
0.012549221515655518,
-0.051621101796627045,
0.030468245968222618,
-0.0014... |
Recognai/zeroshot_selectra_medium | 6c3ff31c3c1acb96375d7913f90a19707af33b9a | 2022-03-27T09:30:04.000Z | [
"pytorch",
"electra",
"text-classification",
"es",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"nli",
"license:apache-2.0"
] | zero-shot-classification | false | Recognai | null | Recognai/zeroshot_selectra_medium | 498 | 3 | transformers | ---
language: es
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- xnli
pipeline_tag: zero-shot-classification
license: apache-2.0
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"
---
# Z... | [
-0.06607873737812042,
0.023454107344150543,
-0.016811909154057503,
-0.024203801527619362,
0.03585889935493469,
-0.018713578581809998,
0.013925444334745407,
-0.02274782769382,
-0.00922352820634842,
-0.05966750532388687,
0.06967376917600632,
-0.1020708680152893,
-0.038567300885915756,
-0.005... |
castorini/doc2query-t5-large-msmarco | e607227b4d07161391f3a61a7ccd9efcf875ea14 | 2021-11-24T19:16:08.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/doc2query-t5-large-msmarco | 497 | null | transformers | For more information, check [doc2query.ai](http://doc2query.ai) | [
-0.01664639078080654,
0.042538564652204514,
-0.020739879459142685,
0.04152047261595726,
-0.007000063546001911,
-0.029250264167785645,
-0.03182143718004227,
0.09250056743621826,
0.049610573798418045,
0.012089862488210201,
0.029003707692027092,
0.005808307323604822,
0.06598371267318726,
-0.0... |
j-hartmann/purchase-intention-english-roberta-large | e26a7d11ced410a78f1fe9a710e61ca14a2a0014 | 2022-02-06T12:22:55.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"transformers",
"sentiment",
"twitter"
] | text-classification | false | j-hartmann | null | j-hartmann/purchase-intention-english-roberta-large | 497 | 1 | transformers | ---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "This looks tasty. Where can I buy it??"
- text: "Now I want this, too."
- text: "You look great today!"
- text: "I just love spring and sunshine!"
---
This RoBERTa-based model can classify *expressed purchase intentions* in English language te... | [
-0.09872577339410782,
-0.0210016630589962,
-0.016322437673807144,
0.015896648168563843,
0.014374596998095512,
0.0028340602293610573,
0.026491178199648857,
0.05030149966478348,
-0.012779596261680126,
-0.0795096606016159,
-0.04066513478755951,
0.030178312212228775,
0.04208267107605934,
-0.02... |
naver/efficient-splade-V-large-query | eb23fdf72c344e26d37d63a86cf536b3a6e11118 | 2022-07-08T13:12:08.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:ms_marco",
"transformers",
"splade",
"query-expansion",
"document-expansion",
"bag-of-words",
"passage-retrieval",
"knowledge-distillation",
"document encoder",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | naver | null | naver/efficient-splade-V-large-query | 497 | null | transformers | ---
license: cc-by-nc-sa-4.0
language: "en"
tags:
- splade
- query-expansion
- document-expansion
- bag-of-words
- passage-retrieval
- knowledge-distillation
- document encoder
datasets:
- ms_marco
---
## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for quer... | [
-0.0365375280380249,
-0.0052169933915138245,
-0.04493996128439903,
0.02278798073530197,
-0.023053521290421486,
0.006130758672952652,
-0.06290934979915619,
0.027982421219348907,
-0.03152460604906082,
0.001991125289350748,
0.061385612934827805,
0.0003424505703151226,
0.012578875757753849,
0.... |
canwenxu/BERT-of-Theseus-MNLI | ee82a9e7c3fec19661f93a2291295ea62e8acee1 | 2021-05-19T13:58:30.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"dataset:multi_nli",
"arxiv:2002.02925",
"arxiv:2005.00628",
"transformers"
] | feature-extraction | false | canwenxu | null | canwenxu/BERT-of-Theseus-MNLI | 496 | null | transformers | ---
thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
datasets:
- multi_nli
---
# BERT-of-Theseus
See our paper ["BERT-of-Theseus: Compressing BERT by Progressive Module Replacing"](http://arxiv.org/abs/2002.02925).
BERT-of-Theseus is a new compressed BERT by progressiv... | [
-0.14293628931045532,
-0.037599097937345505,
0.07479608803987503,
0.05985765904188156,
0.007039262913167477,
0.03824728727340698,
-0.04509834572672844,
0.013635183684527874,
0.033281587064266205,
-0.0086225476115942,
-0.005419918801635504,
0.03509693220257759,
-0.008079776540398598,
0.0636... |
readerbench/RoBERT-base | 42fa3f7ca1731b66401081554a36ef072279402a | 2021-05-20T04:05:43.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"ro",
"transformers"
] | null | false | readerbench | null | readerbench/RoBERT-base | 496 | null | transformers | Model card for RoBERT-base
---
language:
- ro
---
# RoBERT-base
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.581... | [
-0.06448285281658173,
-0.06794307380914688,
-0.014083046466112137,
0.028884606435894966,
-0.013742855750024319,
0.14084839820861816,
-0.039669256657361984,
0.053051020950078964,
0.015603196807205677,
-0.02033010497689247,
-0.009584218263626099,
-0.07699316740036011,
0.008353515528142452,
0... |
GroNLP/gpt2-small-dutch | a4d770e17c7b3b2aa3ff29c6e52c7c8284974fb9 | 2021-05-21T09:55:47.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"nl",
"arxiv:2012.05628",
"transformers",
"adaption",
"recycled",
"gpt2-small"
] | text-generation | false | GroNLP | null | GroNLP/gpt2-small-dutch | 495 | null | transformers | ---
language: nl
tags:
- adaption
- recycled
- gpt2-small
pipeline_tag: text-generation
---
# GPT-2 recycled for Dutch (small)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This mod... | [
-0.09735878556966782,
-0.025663580745458603,
-0.0159592442214489,
0.025443661957979202,
0.08134505152702332,
-0.06880450248718262,
-0.0393754318356514,
0.0597071573138237,
-0.003977623302489519,
-0.06150631606578827,
0.004668361973017454,
-0.012051801197230816,
-0.0657159611582756,
0.04205... |
textattack/roberta-base-MRPC | c8e94968c57c5d825bf0476261d3fb0602c1e0ac | 2021-05-20T22:07:47.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-MRPC | 495 | null | transformers | ## TextAttack Model Card
This `roberta-base` 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 3e-05, and a maximum sequence length of 256.
Since this was a classifi... | [
-0.09013377875089645,
-0.050823453813791275,
-0.05688469111919403,
-0.006750625558197498,
-0.05234414339065552,
0.07756023854017258,
-0.04574105516076088,
0.043804831802845,
0.028769860044121742,
-0.03543238341808319,
-0.015045338310301304,
0.035710494965314865,
-0.0007155675557442009,
0.0... |
thanathorn/mt5-cpe-kmutt-thai-sentence-sum | cc479312c558c62618d794a961d994be2a12d0fc | 2022-05-13T18:20:03.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"th",
"transformers",
"summarization",
"mT5",
"autotrain_compatible"
] | summarization | false | thanathorn | null | thanathorn/mt5-cpe-kmutt-thai-sentence-sum | 495 | 1 | transformers | ---
tags:
- summarization
- mT5
language:
- th
widget:
- text: "simplify: ถ้าพูดถึงขนมหวานในตำนานที่ชื่นใจที่สุดแล้วละก็ต้องไม่พ้น น้ำแข็งใส แน่เพราะว่าเป็นอะไรที่ชื่นใจสุด"
---
# mt5-cpe-kmutt-thai-sentence-sum
This repository contains the finetuned mT5-base model for Thai sentence summarization. The architecture of... | [
-0.10741720348596573,
0.010382728651165962,
0.06266243755817413,
0.02563062123954296,
0.05336781218647957,
0.01932690106332302,
-0.055251434445381165,
0.008649271912872791,
0.058634962886571884,
-0.038857121020555496,
0.03398098424077034,
-0.10331521183252335,
0.05127353221178055,
0.013825... |
Harshveer/autonlp-formality_scoring_2-32597818 | 0683aa8fe9feb6b9824e38a256f6258aaaf79f34 | 2021-11-14T06:46:39.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:Harshveer/autonlp-data-formality_scoring_2",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | Harshveer | null | Harshveer/autonlp-formality_scoring_2-32597818 | 494 | null | transformers | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Harshveer/autonlp-data-formality_scoring_2
co2_eq_emissions: 8.655894631203154
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 32597818
- CO2 Emissions (in grams): 8.655894631203154
## Validation Met... | [
-0.07858423888683319,
0.058237139135599136,
-0.04054221883416176,
0.02948416955769062,
0.07153751701116562,
0.020713942125439644,
0.030200686305761337,
0.06089668348431587,
0.027965551242232323,
-0.04372565820813179,
-0.011305119842290878,
-0.13811607658863068,
-0.05347006767988205,
0.0271... |
bayartsogt/albert-mongolian | 33be497e1f7f561b0b1d58880d523be723830771 | 2021-03-17T19:01:07.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"mn",
"arxiv:1904.00962",
"transformers",
"autotrain_compatible"
] | fill-mask | false | bayartsogt | null | bayartsogt/albert-mongolian | 494 | 2 | transformers | ---
language: mn
---
# ALBERT-Mongolian
[pretraining repo link](https://github.com/bayartsogt-ya/albert-mongolian)
## Model description
Here we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News ... | [
0.010642632842063904,
-0.006300316192209721,
0.009903010912239552,
0.04985344037413597,
0.01015252061188221,
0.05447586998343468,
-0.0660846009850502,
0.0557810477912426,
-0.010033766739070415,
-0.047897256910800934,
0.02119837887585163,
-0.029736407101154327,
0.051937226206064224,
-0.0147... |
bigjoedata/rockbot355M | c43da88f2a0221ca19bdc99d81cbcc05d65474eb | 2021-05-21T14:17:25.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | bigjoedata | null | bigjoedata/rockbot355M | 494 | null | transformers |
# 🎸 🥁 Rockbot 🎤 🎧
A [GPT-2](https://openai.com/blog/better-language-models/) based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
**Instructions:** Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise... | [
-0.053262002766132355,
-0.04699165001511574,
-0.007151858881115913,
-0.013682748191058636,
0.06134549528360367,
0.007755270227789879,
-0.020549895241856575,
-0.06168321520090103,
-0.003512990428134799,
-0.08141987770795822,
-0.028142930939793587,
-0.06584947556257248,
0.12182558327913284,
... |
superb/wav2vec2-base-superb-sid | 73365f1ed139a3d88fb8a72b98ecac3a38a1fa0e | 2021-11-04T16:03:40.000Z | [
"pytorch",
"wav2vec2",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | audio-classification | false | superb | null | superb/wav2vec2-base-superb-sid | 494 | null | transformers | ---
language: en
datasets:
- superb
tags:
- speech
- audio
- wav2vec2
- audio-classification
widget:
- example_title: VoxCeleb Speaker id10003
src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
- example_title: VoxCeleb Speaker id10004
src: https://cdn-media.huggingface.co/speech_samples/VoxCe... | [
-0.0872616320848465,
-0.06299030035734177,
-0.02740572579205036,
-0.09863390773534775,
0.06444341689348221,
0.038085438311100006,
-0.07422550022602081,
-0.06822848320007324,
0.004329846240580082,
-0.05950727313756943,
0.02541392296552658,
-0.09097390621900558,
-0.014917215332388878,
-0.000... |
crystallyzing/DialoGPT-small-nishikiyama | e2268eaff68c5ac9dc1e475d7b3362f22c5f67ff | 2022-06-21T00:05:00.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | crystallyzing | null | crystallyzing/DialoGPT-small-nishikiyama | 494 | null | transformers | ---
tags:
- conversational
---
# Nishiki Chatbot Model | [
-0.08805780857801437,
-0.005041730590164661,
0.044300202280282974,
0.01086872536689043,
0.02807549014687538,
-0.13015316426753998,
0.10621298104524612,
-0.029013855382800102,
0.0545869842171669,
-0.06118704751133919,
0.012677827849984169,
-0.03421302139759064,
0.0038082124665379524,
0.0413... |
Norod78/hebrew-gpt_neo-tiny | 61d3dddbbf95e3096e6a4249dc5d7fe396de529a | 2022-07-04T07:27:46.000Z | [
"pytorch",
"jax",
"gpt_neo",
"text-generation",
"he",
"transformers",
"license:mit"
] | text-generation | false | Norod78 | null | Norod78/hebrew-gpt_neo-tiny | 493 | null | transformers | ---
language: he
thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
widget:
- text: "עוד בימי קדם"
- text: "קוראים לי דורון ואני מעוניין ל"
- text: "קוראים לי איציק ואני חושב ש"
- text: "החתול שלך מאוד חמוד ו"
license: mit
---
# hebrew-gpt_neo-tiny
Hebrew text generation model based on [EleutherA... | [
-0.0902843102812767,
0.03872276842594147,
-0.014581657946109772,
-0.05650012940168381,
-0.001112707774154842,
0.004159613978117704,
-0.011862082406878471,
-0.020016411319375038,
0.04470624029636383,
0.011448752135038376,
0.06478375196456909,
-0.08461015671491623,
0.009078942239284515,
-0.0... |
asafaya/bert-large-arabic | 980a2eb3a4b8b3eb156b82ae30cc9768ef3794de | 2021-05-19T00:07:46.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"dataset:oscar",
"dataset:wikipedia",
"transformers",
"autotrain_compatible"
] | fill-mask | false | asafaya | null | asafaya/bert-large-arabic | 492 | null | transformers | ---
language: ar
datasets:
- oscar
- wikipedia
---
# Arabic BERT Large Model
Pretrained BERT Large language model for Arabic
_If you use this model in your work, please cite this paper:_
```
@inproceedings{safaya-etal-2020-kuisail,
title = "{KUISAIL} at {S}em{E}val-2020 Task 12: {BERT}-{CNN} for Offensive Spe... | [
-0.0867978185415268,
-0.00981132872402668,
0.04813704267144203,
0.03212527558207512,
0.004550151061266661,
0.05449078977108002,
-0.006575993727892637,
-0.07864397764205933,
0.027794789522886276,
-0.013839654624462128,
-0.0014366256073117256,
-0.023991815745830536,
0.05722324550151825,
-0.0... |
facebook/hubert-xlarge-ls960-ft | 8b565fd5c194610f72ff01f4fecf7ccde17f9638 | 2022-05-24T10:44:12.000Z | [
"pytorch",
"tf",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:libri-light",
"dataset:librispeech_asr",
"arxiv:2106.07447",
"transformers",
"speech",
"audio",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/hubert-xlarge-ls960-ft | 492 | 7 | transformers | ---
language: en
datasets:
- libri-light
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: hubert-large-ls960-ft
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
n... | [
-0.08370207995176315,
-0.09826073050498962,
0.000029868515412090346,
-0.06922198832035065,
-0.024562450125813484,
-0.001877912669442594,
-0.023187462240457535,
-0.018289243802428246,
-0.05474185198545456,
-0.09931867569684982,
0.0021802487317472696,
-0.08613403141498566,
-0.05199350044131279... |
DMetaSoul/sbert-chinese-general-v2-distill | 7f91a6d64ffa5a0031587f9738dd603219abf8c3 | 2022-04-02T09:58:33.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese"
] | sentence-similarity | false | DMetaSoul | null | DMetaSoul/sbert-chinese-general-v2-distill | 492 | null | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-general-v2-distill
此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2)的蒸馏版本（仅4层 BERT），适用于**通用语义匹配**场景，从效果来看该... | [
-0.06304185837507248,
-0.0018807227024808526,
0.04594602808356285,
-0.039733704179525375,
0.008908441290259361,
0.01676112785935402,
-0.029114805161952972,
0.06853490322828293,
0.02674100734293461,
-0.06807053089141846,
0.11109739542007446,
-0.024825895205140114,
0.04943850636482239,
0.018... |
tscholak/1wnr382e | 44847d47b5b59789aadc86c7f88d2574cf1f284c | 2022-01-10T21:50:25.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:spider",
"arxiv:2109.05093",
"transformers",
"text2sql",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | tscholak | null | tscholak/1wnr382e | 490 | null | transformers | ---
language:
- en
thumbnail: "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab"
tags:
- text2sql
widget:
- "How many singers do we have? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : singer_id, name, country, song... | [
-0.0009505408816039562,
-0.03852904587984085,
-0.030543982982635498,
0.010516415350139141,
0.0210415031760931,
0.03547271341085434,
0.04190151020884514,
-0.02051895670592785,
-0.01922018639743328,
-0.02962866611778736,
0.018675539642572403,
-0.11727866530418396,
0.05493256077170372,
0.0107... |
codeparrot/codeparrot-small-multi | 7753edbe82562bf23c6ff15ad46ce6f0f2307139 | 2022-07-15T10:56:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"code",
"dataset:codeparrot/github-code-clean",
"dataset:openai_humaneval",
"transformers",
"generation",
"license:apache-2.0"
] | text-generation | false | codeparrot | null | codeparrot/codeparrot-small-multi | 490 | null | transformers | ---
language:
- code
license: apache-2.0
tags:
- code
- gpt2
- generation
datasets:
- "codeparrot/github-code-clean"
- "openai_humaneval"
metrics:
- "evaluate-metric/code_eval"
---
# CodeParrot-Multi 🦜 (small)
CodeParrot-Multi 🦜 is a GPT-2 model (110M parameters) trained to generate code in 9 programming languag... | [
-0.09567297250032425,
-0.08774632960557938,
-0.08397429436445236,
0.02111663483083248,
0.017826490104198456,
-0.045471109449863434,
-0.028125716373324394,
0.06555846333503723,
-0.07048789411783218,
-0.059973448514938354,
0.0194704569876194,
-0.06979633867740631,
0.011625556275248528,
-0.04... |
HooshvareLab/gpt2-fa | 9c1fa5edb93f30ca93df0d1f1abcc44bcc73e5d1 | 2021-05-21T10:51:23.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"transformers",
"license:apache-2.0"
] | text-generation | false | HooshvareLab | null | HooshvareLab/gpt2-fa | 489 | null | transformers | ---
language: fa
license: apache-2.0
widget:
- text: "در یک اتفاق شگفت انگیز، پژوهشگران"
- text: "گرفتگی بینی در کودکان و بهخصوص نوزادان باعث میشود"
- text: "امیدواریم نوروز امسال سالی"
---
# ParsGPT2
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ParsGPT2,
... | [
-0.0621785894036293,
0.07048328965902328,
-0.009135381318628788,
-0.0059806955978274345,
0.0037166301626712084,
-0.06961491703987122,
0.01141546480357647,
-0.02258879318833351,
0.013217786327004433,
-0.015189587138593197,
0.02521759830415249,
0.03939911723136902,
0.05884047970175743,
-0.03... |
IMSyPP/hate_speech_en | ffe54334b9df65e704492d2d660610dd848658d6 | 2022-05-16T06:13:38.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"license:mit"
] | text-classification | false | IMSyPP | null | IMSyPP/hate_speech_en | 489 | 1 | transformers | ---
widget:
- text: "My name is Mark and I live in London. I am a postgraduate student at Queen Mary University."
language:
- en
license: mit
---
# Hate Speech Classifier for Social Media Content in English Language
A monolingual model for hate speech classification of social media content in English language. Th... | [
-0.04767749458551407,
-0.04758436977863312,
0.0748697966337204,
-0.006920006591826677,
0.05371137708425522,
0.0589689239859581,
0.05959739536046982,
-0.0011432872852310538,
0.051125336438417435,
-0.03869824483990669,
0.01587556302547455,
-0.0690636858344078,
0.09123872220516205,
0.01464459... |
dbmdz/bert-base-german-europeana-uncased | f703f5a27791d5c8e083eab510563083fb7ed18d | 2021-05-19T14:55:07.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"de",
"transformers",
"historic german",
"license:mit"
] | null | false | dbmdz | null | dbmdz/bert-base-german-europeana-uncased | 489 | null | transformers | ---
language: de
license: mit
tags:
- "historic german"
---
# 🤗 + 📚 dbmdz BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models 🎉
# German Europeana BERT
We use the open source [Europeana newspapers](http://www.europeana-news... | [
-0.14619633555412292,
-0.10724250227212906,
-0.01246640644967556,
0.017872899770736694,
-0.039598289877176285,
0.05608343705534935,
-0.028806161135435104,
0.0779457539319992,
-0.04386018589138985,
-0.04420127347111702,
-0.03908848389983177,
-0.01847052201628685,
-0.025506433099508286,
0.02... |
naver-clova-ocr/bros-large-uncased | a644113dc6c2b6dd53f99f94feb7ed4a5e3fdf71 | 2022-04-05T13:57:07.000Z | [
"pytorch",
"bros",
"arxiv:2108.04539",
"transformers"
] | null | false | naver-clova-ocr | null | naver-clova-ocr/bros-large-uncased | 489 | 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... |
oigele/Fb_improved_zeroshot | d68aaffe80f68f2a820944c59a92b2e285741725 | 2021-11-29T11:51:49.000Z | [
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"arxiv:1909.00161",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | oigele | null | oigele/Fb_improved_zeroshot | 488 | 4 | transformers | ---
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
widget:
- text: "natural language processing"
candidate_labels: "Location & Address, Employment, Organizational, Name, Service, Studies, Science"
hypothesis_template: "This is {}."
---
# Fb_improved_zeroshot
Zero-Shot Model designed to classify aca... | [
-0.03528502583503723,
0.027242939919233322,
0.023314666002988815,
-0.010736946016550064,
0.06809545308351517,
0.0009531466057524085,
-0.04522845894098282,
-0.019717765972018242,
0.002836934756487608,
-0.06471379846334457,
0.024271022528409958,
-0.06163706257939339,
0.007482814136892557,
0.... |
imxly/sentence_roberta_wwm_ext | 28b1082b623326456cdec17ee4b521e21e823434 | 2021-05-19T20:20:32.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | imxly | null | imxly/sentence_roberta_wwm_ext | 487 | 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.... |
KBLab/bert-base-swedish-cased-pos | eae7acf6c32812794b8edd93a944c6b1bd1e402a | 2021-05-18T21:20:59.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | KBLab | null | KBLab/bert-base-swedish-cased-pos | 486 | 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.... |
lirondos/anglicisms-spanish-mbert | 11e819e8161f1162b2b09d253dde4a927a9dc3e0 | 2022-05-16T14:03:29.000Z | [
"pytorch",
"bert",
"token-classification",
"es",
"dataset:coalas",
"transformers",
"anglicisms",
"loanwords",
"borrowing",
"codeswitching",
"arxiv:2203.16169",
"license:cc-by-4.0",
"autotrain_compatible"
] | token-classification | false | lirondos | null | lirondos/anglicisms-spanish-mbert | 486 | null | transformers | ---
language:
- es
license: cc-by-4.0
tags:
- anglicisms # Example: audio
- loanwords # Example: automatic-speech-recognition
- borrowing # Example: speech
- codeswitching # Example to specify a library: allennlp
- arxiv:2203.16169
datasets:
- coalas # Example: common_voice. Use dataset id from https://hf.co/datas... | [
-0.061395399272441864,
-0.07765623182058334,
-0.004751102067530155,
-0.026392338797450066,
0.050411079078912735,
0.04140030965209007,
0.024222323670983315,
-0.07237081229686737,
0.04191143065690994,
-0.04538198933005333,
0.052615221589803696,
-0.06415441632270813,
-0.0143975131213665,
-0.0... |
FangLee/DialoGPT-small-Kirito | b367d8ac8cbfabbaeb96bfd98a3f4550687daa99 | 2021-09-04T14:25:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | FangLee | null | FangLee/DialoGPT-small-Kirito | 485 | null | transformers | ---
tags:
- conversational
---
@Kirito DialoGPT Small Model | [
-0.030213700607419014,
0.004708520136773586,
0.025042733177542686,
0.005150844808667898,
0.050808392465114594,
-0.060302939265966415,
0.09553280472755432,
-0.013839706778526306,
0.07512351870536804,
-0.026071082800626755,
0.03847123309969902,
-0.07936635613441467,
0.006472201086580753,
0.0... |
filco306/gpt2-base-style-paraphraser | e320d414ae5ef9a893c4a6bc3604117f9e436c53 | 2021-08-28T19:27:41.000Z | [
"pytorch",
"text-generation",
"arxiv:2010.05700",
"transformers"
] | text-generation | false | filco306 | null | filco306/gpt2-base-style-paraphraser | 485 | 2 | transformers | # GPT2 base style transfer paraphraser
This is the trained base-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by th... | [
-0.1133081242442131,
0.009696620516479015,
0.06329228729009628,
0.07285311818122864,
0.09128259122371674,
0.0007881597266532481,
-0.052948251366615295,
-0.03992582485079765,
-0.034054726362228394,
0.016542209312319756,
-0.0029739399906247854,
-0.005598101299256086,
0.08719001710414886,
-0.... |
Helsinki-NLP/opus-mt-th-fr | b9e7a1b2d0a2aa9c1cc4123c37dcef4b13d41c15 | 2021-09-11T10:48:01.000Z | [
"pytorch",
"marian",
"text2text-generation",
"th",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-th-fr | 484 | null | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-th-fr
* source languages: th
* target languages: fr
* OPUS readme: [th-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/th-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.06143813580274582,
-0.023496365174651146,
0.02496808022260666,
-0.017120424658060074,
0.012232628650963306,
0.10213306546211243,
-0.059655915945768356,
0.03695574030280113,
0.026717249304056168,
-0.00871147308498621,
0.0037982268258929253,
-0.039496272802352905,
-0.07447108626365662,
-0... |
deepset/bert-base-german-cased-hatespeech-GermEval18Coarse | 9423036452a34960b227e787d8fd86063c6b87ad | 2021-05-19T15:25:01.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:cc-by-4.0"
] | text-classification | false | deepset | null | deepset/bert-base-german-cased-hatespeech-GermEval18Coarse | 484 | 6 | transformers | ---
license: cc-by-4.0
---
This is a German BERT v1 (https://deepset.ai/german-bert) trained to do hate speech detection on the GermEval18Coarse dataset | [
-0.09257111698389053,
0.02817523293197155,
0.030330678448081017,
0.007327720057219267,
0.07083045691251755,
-0.00007588681182824075,
0.03496389836072922,
-0.024221187457442284,
0.03572508320212364,
-0.08342184126377106,
-0.017490467056632042,
-0.09881039708852768,
-0.017834778875112534,
0.... |
ELiRF/mbart-large-cc25-dacsa-es | c0f9e6d88fc2f865327cb63898186036944d204e | 2022-07-11T17:34:09.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"es",
"arxiv:2001.08210",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | ELiRF | null | ELiRF/mbart-large-cc25-dacsa-es | 484 | null | transformers | ---
language: es
tags:
- summarization
widget:
- text: "La Universitat Politècnica de València (UPV), a través del proyecto Atenea “plataforma de mujeres, arte y tecnología” y en colaboración con las compañías tecnológicas Metric Salad y Zetalab, ha digitalizado y modelado en 3D para la 35ª edición del Festival Dansa... | [
0.020284555852413177,
-0.02396903745830059,
0.02393236942589283,
-0.044318895787000656,
-0.028732333332300186,
0.027888601645827293,
0.07864434272050858,
-0.033373281359672546,
0.061881229281425476,
-0.010080046951770782,
0.06280556321144104,
-0.07512764632701874,
-0.004864920862019062,
0.... |
ckiplab/bert-tiny-chinese-ner | a18df36c7f73ae3329877506be48a86c09599e8d | 2022-05-10T03:28:12.000Z | [
"pytorch",
"bert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/bert-tiny-chinese-ner | 483 | null | transformers | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segment... | [
-0.17626053094863892,
-0.01695609651505947,
0.05264962837100029,
-0.014467387460172176,
0.04539623111486435,
0.017867865040898323,
0.058326490223407745,
0.022501304745674133,
0.02078504115343094,
-0.01768597587943077,
0.039050281047821045,
-0.04356204345822334,
0.006745001766830683,
0.0609... |
navteca/roberta-base-squad2 | 6c7bec0e5e05d24070d598661767d8004c097553 | 2021-04-06T16:27:48.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:mit",
"autotrain_compatible"
] | question-answering | false | navteca | null | navteca/roberta-base-squad2 | 482 | null | transformers | ---
datasets:
- squad_v2
language: en
license: mit
pipeline_tag: question-answering
tags:
- roberta
- question-answering
---
# Roberta base model for QA (SQuAD 2.0)
This model uses [roberta-base](https://huggingface.co/roberta-base).
## Training Data
The models have been trained on the [SQuAD 2.0](https://rajpurkar.g... | [
-0.14098526537418365,
-0.07050324231386185,
-0.04231586307287216,
0.05892234295606613,
-0.003887410741299391,
0.0075106872245669365,
0.0018973341211676598,
0.061997152864933014,
-0.016463419422507286,
-0.016953343525528908,
-0.018335837870836258,
-0.10491099208593369,
0.025394627824425697,
... |
Helsinki-NLP/opus-mt-en-he | 6b58caddd6ee489cafb8dd45d0e76a9c9b61de4c | 2021-09-09T21:35:50.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"en",
"he",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-he | 481 | 1 | transformers | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-he
* source languages: en
* target languages: he
* OPUS readme: [en-he](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-he/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... | [
-0.055364180356264114,
-0.013184436596930027,
0.022574407979846,
-0.01938953809440136,
-0.0011229126248508692,
0.09681642800569534,
-0.057348135858774185,
0.03259827569127083,
0.0099124014377594,
-0.012718183919787407,
0.009502824395895004,
-0.04413411393761635,
-0.07429047673940659,
-0.02... |
speechbrain/asr-crdnn-transformerlm-librispeech | 6c7c0a922755a083805630e0c1bfc2258da3fe4c | 2021-11-30T00:38:21.000Z | [
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Tranformer",
"pytorch",
"license:apache-2.0"
] | automatic-speech-recognition | false | speechbrain | null | speechbrain/asr-crdnn-transformerlm-librispeech | 481 | null | speechbrain | ---
language: "en"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- Tranformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- librispeech
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" framebo... | [
-0.13995307683944702,
-0.16403374075889587,
-0.00897042267024517,
-0.09092943370342255,
-0.016846755519509315,
0.05191715806722641,
-0.002059811493381858,
-0.03545107692480087,
-0.07860442250967026,
-0.06068509817123413,
-0.0041145035065710545,
-0.08329794555902481,
-0.05768254026770592,
0... |
IIC/dpr-spanish-passage_encoder-squades-base | fa963e0a2626fa6ea5553894d5685cd262cc6382 | 2022-04-02T15:08:22.000Z | [
"pytorch",
"bert",
"fill-mask",
"es",
"dataset:squad_es",
"arxiv:2004.04906",
"transformers",
"sentence similarity",
"passage retrieval",
"model-index",
"autotrain_compatible"
] | fill-mask | false | IIC | null | IIC/dpr-spanish-passage_encoder-squades-base | 481 | 3 | transformers | ---
language:
- es
tags:
- sentence similarity # Example: audio
- passage retrieval # Example: automatic-speech-recognition
datasets:
- squad_es
metrics:
- eval_loss: 0.08608942725107592
- eval_accuracy: 0.9925325215819639
- eval_f1: 0.8805402320715237
- average_rank: 0.27430093209054596
model-index:
- name: dpr-spa... | [
-0.09165947884321213,
-0.06601984798908234,
-0.054284628480672836,
-0.054547663778066635,
0.04456948861479759,
0.04046889767050743,
0.0206848606467247,
-0.016014432534575462,
-0.002231176244094968,
-0.09954672306776047,
0.030761564150452614,
-0.12795241177082062,
-0.01933259516954422,
0.03... |
luyaojie/uie-base-en | 966f8b1fc4c74e94ab552081605913ad5133cc41 | 2022-04-15T13:09:21.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | luyaojie | null | luyaojie/uie-base-en | 481 | null | transformers | ---
license: cc-by-nc-sa-4.0
---
| [
-0.04723339527845383,
0.025953227654099464,
-0.09948673844337463,
-0.03253987058997154,
0.05198120325803757,
0.035326000303030014,
0.03477821871638298,
-0.0377240888774395,
-0.015796229243278503,
0.04078708216547966,
0.004578083287924528,
-0.04642094671726227,
0.0043295398354530334,
0.0100... |
zuu/grammar-error-correcter | e6b6507ef6e9308d0e344845c2e7486eaaecca5d | 2022-06-02T18:10:59.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | zuu | null | zuu/grammar-error-correcter | 481 | 0 | transformers | ```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
GED_TOKENIZER = AutoTokenizer.from_pretrained("zuu/grammar-error-correcter")
GED_MODEL = AutoModelForSeq2SeqLM.from_pretrained("zuu/grammar-error-correcter")
# Incorrect text
incorrect_text = 'young children should avoid exposure to contageous di... | [
-0.05740447714924812,
0.03495039418339729,
0.009616155177354813,
0.034644659608602524,
-0.0701906606554985,
-0.008159924298524857,
0.011895932257175446,
0.08807350695133209,
-0.05337664484977722,
-0.018469147384166718,
0.054006122052669525,
-0.04400038719177246,
0.06244435906410217,
0.0349... |
cambridgeltl/simctg_lccc_dialogue | 45b51e1c98f8dc6f0b65a2ade9bdff6d9a128b79 | 2022-06-25T19:21:55.000Z | [
"pytorch",
"gpt2",
"text-generation",
"arxiv:2008.03946",
"arxiv:2202.06417",
"transformers"
] | text-generation | false | cambridgeltl | null | cambridgeltl/simctg_lccc_dialogue | 480 | null | transformers | This model provides a Chinese GPT-2 language model trained with SimCTG on the LCCC benchmark [(Wang et al., 2020)](https://arxiv.org/pdf/2008.03946v2.pdf) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG a... | [
-0.08486119657754898,
-0.07038545608520508,
0.015798237174749374,
0.06885602325201035,
-0.02955619990825653,
0.0008793825400061905,
-0.032661110162734985,
-0.0016880270559340715,
-0.0009335228241980076,
-0.08376596868038177,
0.01622794196009636,
-0.056575074791908264,
0.010277153924107552,
... |
j-hartmann/emotion-english-roberta-large | ab319b8cfc7ca91478e74bce639ed8b8e0927d0c | 2021-08-29T11:48:09.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"transformers",
"sentiment",
"emotion",
"twitter",
"reddit"
] | text-classification | false | j-hartmann | null | j-hartmann/emotion-english-roberta-large | 480 | 1 | transformers | ---
language: "en"
tags:
- roberta
- sentiment
- emotion
- twitter
- reddit
widget:
- text: "Oh wow. I didn't know that."
- text: "This movie always makes me cry.."
- text: "Oh Happy Day"
---
## Description ℹ
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datase... | [
-0.0519532635807991,
-0.040424175560474396,
0.03750740364193916,
0.03767973929643631,
0.026691783219575882,
0.04425995424389839,
0.028270408511161804,
-0.010654921643435955,
0.08076336234807968,
-0.03586738184094429,
0.038939204066991806,
-0.011356311850249767,
-0.0016697548562660813,
0.01... |
Helsinki-NLP/opus-mt-tc-big-fr-en | df6dfc5e22be93169ad457196ad8472ad749f886 | 2022-06-01T13:01:21.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"fr",
"transformers",
"translation",
"opus-mt-tc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tc-big-fr-en | 480 | 1 | transformers | ---
language:
- en
- fr
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-fr-en
results:
- task:
name: Translation fra-eng
type: translation
args: fra-eng
dataset:
name: flores101-devtest
type: flores_101
args: fra eng devtest
metrics... | [
-0.05384448915719986,
-0.05379650741815567,
0.007851258851587772,
0.0022097742184996605,
0.06942101567983627,
-0.035138290375471115,
0.012336586602032185,
-0.013984194956719875,
0.027137335389852524,
-0.012405267916619778,
0.016138901934027672,
-0.1663447469472885,
-0.01988455466926098,
-0... |
ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive2 | 4e7b2dda5588080784ac6f7482060026296d5cea | 2022-06-22T01:36:28.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ZipperXYZ | null | ZipperXYZ/DialoGPT-medium-TheWorldMachineExpressive2 | 480 | null | transformers | ---
tags:
- conversational
---
# The world machine DialoGPT model | [
-0.031065436080098152,
-0.050446994602680206,
0.024474725127220154,
-0.032328005880117416,
0.042380910366773605,
-0.08751147985458374,
0.07857198268175125,
0.03142630308866501,
0.04671727120876312,
-0.032877106219530106,
-0.020602883771061897,
-0.03311848267912865,
0.02598205767571926,
0.0... |
facebook/hubert-xlarge-ll60k | b0cef767123fe004883915a053f538f1737a1e47 | 2021-10-20T10:20:44.000Z | [
"pytorch",
"tf",
"hubert",
"feature-extraction",
"en",
"dataset:libri-light",
"arxiv:2106.07447",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/hubert-xlarge-ll60k | 479 | 3 | transformers | ---
language: en
datasets:
- libri-light
tags:
- speech
license: apache-2.0
---
# Hubert-Extra-Large
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The extra large model pretrained on 16kHz sampled speech audio. When... | [
-0.04486246407032013,
-0.11916813999414444,
-0.010815639980137348,
-0.011358874849975109,
-0.03390045464038849,
0.07857124507427216,
-0.025996023789048195,
-0.04178137332201004,
-0.03548131510615349,
-0.08658377826213837,
-0.04246630147099495,
-0.01837068237364292,
-0.03742700815200806,
0.... |
mrm8488/roberta-med-small2roberta-med-small-finetuned-cnn_daily_mail-summarization | 3df1c9e04581ca196e80b9ce1e4c22db6431bec7 | 2021-04-06T09:22:39.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | mrm8488 | null | mrm8488/roberta-med-small2roberta-med-small-finetuned-cnn_daily_mail-summarization | 479 | null | transformers | ---
language: en
license: apache-2.0
datasets:
- cnn_dailymail
tags:
- summarization
---
Shared [RoBERTa2RoBERTa (med-small)](https://huggingface.co/nyu-mll/roberta-med-small-1M-1) Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *RoBERTaShared* (med-small) model fine-tuned on the *cn... | [
-0.0052590989507734776,
-0.054838795214891434,
0.03767162561416626,
0.04173806309700012,
0.06353010982275009,
0.0180322527885437,
-0.06515060365200043,
-0.023921065032482147,
0.026325568556785583,
-0.03986195847392082,
0.021836450323462486,
0.02297867275774479,
-0.012008657678961754,
0.056... |
facebook/xglm-7.5B | b4f0ef7d74603a0e63a05695cd38d08260961e3a | 2022-02-14T22:54:52.000Z | [
"pytorch",
"xglm",
"text-generation",
"arxiv:2112.10668",
"transformers",
"license:mit"
] | text-generation | false | facebook | null | facebook/xglm-7.5B | 478 | 5 | transformers | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-7.5B
XGLM-7.5B is a multilingual autoregressive language model (with 7.5 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the... | [
-0.02713983692228794,
-0.08456059545278549,
0.07021740823984146,
0.017778925597667694,
0.034753378480672836,
0.09113920480012894,
0.03935115411877632,
0.0220672395080328,
0.06279164552688599,
0.02407478541135788,
0.013857586309313774,
-0.0959509015083313,
0.035112615674734116,
0.0106664448... |
sadakmed/distiluse-base-multilingual-cased-v2 | d4e9bba5ac7e7bb5a86e3b97e8150e8fc1fbd931 | 2021-09-22T09:37:21.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"multilingual",
"sentence-transformers",
"DistilBert",
"Universal Sentence Encoder",
"sentence-embeddings",
"sentence-similarity",
"license:apache-2.0"
] | feature-extraction | false | sadakmed | null | sadakmed/distiluse-base-multilingual-cased-v2 | 478 | null | sentence-transformers | ---
language: multilingual
tags:
- DistilBert
- Universal Sentence Encoder
- sentence-embeddings
- sentence-transformers
- sentence-similarity
license: apache-2.0
---
While v1 model supports 15 languages, this version supports 50+ languages. However, performance on the 15 languages mentioned in v1 are reported to be a... | [
-0.01893790066242218,
-0.05378040298819542,
-0.006891652010381222,
-0.02512281946837902,
0.033649198710918427,
0.03491845726966858,
-0.09050209820270538,
0.030917389318346977,
0.012744010426104069,
-0.09491518884897232,
0.04222964867949486,
-0.011819485574960709,
-0.029776249080896378,
0.0... |
ethanyt/guwen-quote | a5a28406ac0e3ab13727a3295c15f84f425ac9e8 | 2021-06-17T08:22:56.000Z | [
"pytorch",
"roberta",
"token-classification",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"quotation detection",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | ethanyt | null | ethanyt/guwen-quote | 477 | null | transformers | ---
language:
- "zh"
thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "pytorch"
- "quotation detection"
license: "apache-2.0"
pipeline_tag: "token-classification"
... | [
-0.07923474907875061,
0.0947275310754776,
0.004816481377929449,
-0.09768784046173096,
0.01439759973436594,
-0.0007676238310523331,
0.0218445286154747,
-0.008286704309284687,
0.0043288785964250565,
0.003961291629821062,
0.16669023036956787,
-0.04511483386158943,
0.06290777027606964,
-0.0559... |
google/pegasus-arxiv | 8d68b512ac8f83bd6ecfb651a793a35e71fdc402 | 2020-10-22T16:33:20.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-arxiv | 477 | 1 | transformers | ---
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: [@... | [
-0.009604666382074356,
-0.021501343697309494,
0.045193955302238464,
-0.0028297731187194586,
0.1056499257683754,
-0.02136904187500477,
-0.02256815880537033,
-0.042818013578653336,
0.05765407904982567,
-0.05029257759451866,
0.053568508476018906,
0.0030024012085050344,
-0.032771240919828415,
... |
gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner | aeb8f1a4908c7f21676dd7c1572e303a685056e1 | 2022-05-25T08:55:03.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"de",
"nl",
"es",
"multilingual",
"dataset:conll2003",
"transformers",
"model-index",
"autotrain_compatible"
] | token-classification | false | gunghio | null | gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner | 477 | null | transformers | ---
metrics:
- precision: 0.936
- recall: 0.9458
- f1: 0.9409
- accuracy: 0.9902
datasets:
- conll2003
language:
- en
- de
- nl
- es
- multilingual
model-index:
- name: gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
results:
- task:
type: ner
name: Named Entity Reco... | [
-0.05041155964136124,
-0.03567184880375862,
-0.048738595098257065,
0.01129789836704731,
-0.005803948733955622,
0.00887321401387453,
-0.007266394328325987,
0.05779526010155678,
0.006987350061535835,
-0.10144387930631638,
0.020375119522213936,
-0.108697809278965,
-0.0018696747720241547,
0.02... |
nsi319/legal-pegasus | 54ef2872d33bbff28eb09544bdecbf6699f5b0b8 | 2021-03-11T08:50:52.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"transformers",
"summarization",
"license:mit",
"autotrain_compatible"
] | summarization | false | nsi319 | null | nsi319/legal-pegasus | 477 | null | transformers | ---
language: en
tags: summarization
metrics:
- rouge
- precision
inference: false
license: mit
---
## PEGASUS for legal document summarization
**legal-pegasus** is a finetuned version of ([**google/pegasus-cnn_dailymail**](https://huggingface.co/google/pegasus-cnn_dailymail)) for the **legal domain**, trained to perf... | [
-0.08756554871797562,
-0.013925701379776001,
-0.03616854548454285,
-0.0393950492143631,
-0.017563525587320328,
-0.004509813617914915,
-0.025834176689386368,
0.033391259610652924,
0.015156461857259274,
-0.027127975597977638,
0.03042336367070675,
-0.001012066495604813,
-0.03908894583582878,
... |
hfl/chinese-electra-180g-large-discriminator | d017e219578df8e4885484edbc8969dbdea9cbe0 | 2021-03-03T01:29:12.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | null | false | hfl | null | hfl/chinese-electra-180g-large-discriminator | 476 | 3 | transformers | ---
language:
- zh
license: "apache-2.0"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compa... | [
-0.09022209793329239,
-0.06908009946346283,
0.044281091541051865,
0.05305112898349762,
-0.009731532074511051,
0.055254384875297546,
-0.02344399318099022,
0.0328807607293129,
-0.026894984766840935,
-0.015258828178048134,
0.028422411531209946,
-0.013105235062539577,
-0.019453970715403557,
0.... |
Visual-Attention-Network/van-base | 569d1d8e1323ad5baefa8c00b11d82de0e42cfad | 2022-03-31T12:45:44.000Z | [
"pytorch",
"van",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2202.09741",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | Visual-Attention-Network | null | Visual-Attention-Network/van-base | 476 | null | transformers | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: htt... | [
-0.04665321856737137,
-0.0007814760319888592,
0.015855273231863976,
-0.004480050876736641,
0.11461484432220459,
-0.022039439529180527,
-0.02022823691368103,
-0.011873318813741207,
0.030775051563978195,
-0.021862365305423737,
0.07206636667251587,
-0.041970208287239075,
0.0787147730588913,
0... |
DTAI-KULeuven/robbert-v2-dutch-sentiment | bb4e1466d94f15534e792fc6870040e024000432 | 2022-06-29T13:11:28.000Z | [
"pytorch",
"roberta",
"text-classification",
"nl",
"dataset:dbrd",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"license:mit",
"model-index"
] | text-classification | false | DTAI-KULeuven | null | DTAI-KULeuven/robbert-v2-dutch-sentiment | 476 | null | transformers | ---
language: nl
license: mit
datasets:
- dbrd
model-index:
- name: robbert-v2-dutch-sentiment
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: dbrd
type: sentiment-analysis
split: test
metrics:
- name: Accuracy
type: accuracy
... | [
-0.0679883062839508,
0.054205093532800674,
-0.01622936874628067,
-0.03305955231189728,
0.1170542910695076,
-0.0024667144753038883,
0.04503880813717842,
0.03551375865936279,
0.05338531360030174,
-0.0237885694950819,
0.06191380321979523,
-0.053218353539705276,
0.025152545422315598,
-0.030671... |
facebook/wmt21-dense-24-wide-en-x | ee254716c52331df63a08ac929da96c59e68b057 | 2022-05-26T22:23:33.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"multilingual",
"ha",
"is",
"ja",
"cs",
"ru",
"zh",
"de",
"en",
"arxiv:2108.03265",
"transformers",
"translation",
"wmt21",
"license:mit",
"autotrain_compatible"
] | translation | false | facebook | null | facebook/wmt21-dense-24-wide-en-x | 475 | 9 | transformers | ---
language:
- multilingual
- ha
- is
- ja
- cs
- ru
- zh
- de
- en
license: mit
tags:
- translation
- wmt21
---
# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) an... | [
-0.07725450396537781,
-0.02304929681122303,
-0.05533858761191368,
0.04067360237240791,
-0.012473275884985924,
0.0022177433129400015,
0.031018877401947975,
-0.011003700084984303,
0.008209430612623692,
-0.03689870610833168,
0.02870958298444748,
-0.11327488720417023,
0.0820441022515297,
-0.02... |
alistair7/bbt-diagpt2-model | 2539b4c94eccb5f0ee1d9d86b191f492c70d4fa8 | 2021-06-06T21:49:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | alistair7 | null | alistair7/bbt-diagpt2-model | 474 | null | transformers | ---
tags:
- conversational
---
# A conversational model based on the character of Sheldon Cooper from Big Bang Theory. | [
-0.018701981753110886,
-0.03214837610721588,
0.017944350838661194,
0.032779693603515625,
-0.03569922223687172,
-0.04761292412877083,
0.0774889588356018,
0.04193536564707756,
0.05150457099080086,
-0.04512733966112137,
-0.015245960094034672,
-0.0010128956055268645,
-0.023104310035705566,
-0.... |
impyadav/GPT2-FineTuned-Hinglish-Song-Generation | 7c5694e0b1ec8dab4f17a857b3778911af56609a | 2022-01-03T11:33:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | impyadav | null | impyadav/GPT2-FineTuned-Hinglish-Song-Generation | 474 | 1 | transformers | GPT-2 model fine-tuned on Custom old Hindi songs (Hinglish) for text-generation task (AI Lyricist)
language:
- Hindi
- Hinglish
| [
-0.045941583812236786,
-0.06489919126033783,
0.002479373710229993,
-0.018857454881072044,
-0.03832612931728363,
0.014679105952382088,
0.02510969340801239,
-0.07447414845228195,
-0.0075659723952412605,
-0.02193623036146164,
0.016126342117786407,
0.02368754707276821,
0.028338242322206497,
-0... |
JorisCos/DPRNNTasNet-ks2_Libri1Mix_enhsingle_16k | e37a839cfaa3ce1e0c04d93a0e242d8ec8a694ed | 2021-09-23T15:49:18.000Z | [
"pytorch",
"dataset:Libri1Mix",
"dataset:enh_single",
"asteroid",
"audio",
"DPRNNTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | JorisCos | null | JorisCos/DPRNNTasNet-ks2_Libri1Mix_enhsingle_16k | 471 | null | asteroid | ---
tags:
- asteroid
- audio
- DPRNNTasNet
- audio-to-audio
datasets:
- Libri1Mix
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model `JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/ast... | [
-0.05339193344116211,
-0.0991385206580162,
0.03358631581068039,
-0.03808116167783737,
0.043665286153554916,
-0.06021957844495773,
-0.014813042245805264,
-0.02090379409492016,
-0.08098147064447403,
-0.06403478235006332,
0.02518780343234539,
-0.1054692491889,
-0.02407713606953621,
-0.0507293... |
TransQuest/monotransquest-da-en_zh-wiki | fefd083a71d9be578d7d98191b880d4578898619 | 2021-06-03T19:04:32.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"en-zh",
"transformers",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0"
] | text-classification | false | TransQuest | null | TransQuest/monotransquest-da-en_zh-wiki | 471 | null | transformers | ---
language: en-zh
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE t... | [
-0.09904971718788147,
0.005896366201341152,
-0.016593802720308304,
0.0035537683870643377,
-0.023602940142154694,
-0.05226273089647293,
0.001207570661790669,
0.048936787992715836,
0.0815245658159256,
0.010586651042103767,
-0.061086397618055344,
-0.07322283089160919,
0.07248580455780029,
0.0... |
neuralspace-reverie/indic-transformers-bn-distilbert | 4662cb6d6dd900f8dff05896cd1494a8ed0e1ecf | 2020-12-11T21:57:07.000Z | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"bn",
"transformers",
"MaskedLM",
"Bengali",
"DistilBERT",
"Question-Answering",
"Token Classification",
"Text Classification",
"autotrain_compatible"
] | fill-mask | false | neuralspace-reverie | null | neuralspace-reverie/indic-transformers-bn-distilbert | 471 | null | transformers | ---
language:
- bn
tags:
- MaskedLM
- Bengali
- DistilBERT
- Question-Answering
- Token Classification
- Text Classification
---
# Indic-Transformers Bengali DistilBERT
## Model description
This is a DistilBERT language model pre-trained on ~6 GB of monolingual training corpus. The pre-training data was majorly taken... | [
-0.09034735709428787,
-0.058284372091293335,
0.017194468528032303,
0.06555228680372238,
-0.04776956886053085,
0.06968613713979721,
0.01917179860174656,
0.05167751759290695,
-0.05556442588567734,
-0.08759364485740662,
-0.02474728412926197,
-0.08073034137487411,
-0.0009418310946784914,
0.005... |
HooshvareLab/bert-fa-zwnj-base-ner | 17d4928f28c36fd74864c221a27134da8b6bf9bc | 2021-05-18T21:04:35.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"transformers",
"autotrain_compatible"
] | token-classification | false | HooshvareLab | null | HooshvareLab/bert-fa-zwnj-base-ner | 470 | 3 | transformers | ---
language: fa
---
# BertNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikian... | [
-0.04251851141452789,
-0.036126233637332916,
-0.05064355581998825,
-0.061900828033685684,
0.02239306829869747,
-0.020776644349098206,
0.0358765684068203,
-0.0006631820579059422,
0.05523812025785446,
-0.013330601155757904,
0.04202502593398094,
-0.1213671863079071,
-0.029075173661112785,
0.0... |
KoboldAI/GPT-Neo-2.7B-Janeway | 56b0950204eafb4673c78595669cf8b04e413ab4 | 2022-03-20T12:57:50.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-Neo-2.7B-Janeway | 469 | 2 | transformers | ---
language: en
license: mit
---
# GPT-Neo 2.7B - Janeway
## Model Description
GPT-Neo 2.7B-Janeway is a finetune created using EleutherAI's GPT-Neo 2.7B model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used... | [
-0.08624086529016495,
-0.0032004977110773325,
-0.04396155849099159,
0.0521477572619915,
0.0368407741189003,
-0.02329370751976967,
-0.0003961194888688624,
-0.008174547925591469,
0.0013561947271227837,
-0.0875813290476799,
-0.013440444134175777,
-0.005128555465489626,
0.026656268164515495,
-... |
nvidia/segformer-b3-finetuned-cityscapes-1024-1024 | 74ff1cf1357f4bfa962660c491282dfc3e7c72c2 | 2022-07-20T09:53:50.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:cityscapes",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b3-finetuned-cityscapes-1024-1024 | 469 | null | transformers | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- cityscapes
widget:
- src: https://www.researchgate.net/profile/Anurag-Arnab/publication/315881952/figure/fig5/AS:667673876779033@1536197265755/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.jpg
example_ti... | [
-0.013866015709936619,
0.039189908653497696,
0.07921045273542404,
-0.013933206908404827,
0.06958452612161636,
-0.1330569088459015,
-0.042134255170822144,
0.02053431235253811,
-0.08613268285989761,
-0.07498953491449356,
0.0020826291292905807,
-0.06123873591423035,
0.009738415479660034,
0.03... |
cardiffnlp/bertweet-base-emotion | 89c1f1de95e4ae3979c82155d9a8f00be45c1668 | 2021-05-20T14:45:11.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/bertweet-base-emotion | 468 | null | transformers | [
-0.11883839219808578,
0.04829875007271767,
-0.0025480713229626417,
-0.011011119931936264,
0.05195086821913719,
0.010291781276464462,
0.11543325334787369,
0.0007007101085036993,
-0.08592551946640015,
-0.07065412402153015,
0.0013317831326276064,
-0.03547239303588867,
0.018434111028909683,
-0... | |
ricardo-filho/bert-portuguese-cased-nli-assin-assin-2 | 17efd936dc233255fe5c95474813a51e9c3be9f8 | 2021-08-04T13:24:42.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | ricardo-filho | null | ricardo-filho/bert-portuguese-cased-nli-assin-assin-2 | 468 | 3 | sentence-transformers | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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 for tasks like cluster... | [
-0.05899689346551895,
-0.04668601602315903,
-0.007824195548892021,
0.055522240698337555,
0.021890703588724136,
0.06966360658407211,
-0.03817794471979141,
0.017645347863435745,
0.029430586844682693,
-0.08511728048324585,
0.031796302646398544,
-0.012681740336120129,
0.04747813940048218,
0.06... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.