modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
class | author stringlengths 2 38 ⌀ | config null | id stringlengths 4 112 | downloads float64 0 36.8M ⌀ | likes float64 0 712 ⌀ | library_name stringclasses 17
values | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
NbAiLab/nb-bert-base | 82b194c0b3ea1fcad65f1eceee04adb26f9f71ac | 2021-11-26T12:02:27.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"no",
"transformers",
"norwegian",
"license:cc-by-4.0",
"fill-mask"
] | fill-mask | false | NbAiLab | null | NbAiLab/nb-bert-base | 13,659 | 13 | transformers | 600 | ---
language: no
license: cc-by-4.0
tags:
- norwegian
- bert
pipeline_tag: fill-mask
widget:
- text: På biblioteket kan du [MASK] en bok.
- text: Dette er et [MASK] eksempel.
- text: Av og til kan en språkmodell gi et [MASK] resultat.
- text: Som ansat får du [MASK] for at bidrage til borgernes adgang til dansk kultur... |
monsoon-nlp/hindi-bert | 35f95927136cc5ea05db2fb2af1fb1455f5b310e | 2020-08-26T22:14:33.000Z | [
"pytorch",
"tf",
"electra",
"feature-extraction",
"hi",
"transformers"
] | feature-extraction | false | monsoon-nlp | null | monsoon-nlp/hindi-bert | 13,569 | 4 | transformers | 601 | ---
language: hi
---
# Releasing Hindi ELECTRA model
This is a first attempt at a Hindi language model trained with Google Research's [ELECTRA](https://github.com/google-research/electra).
**Consider using this newer, larger model: https://huggingface.co/monsoon-nlp/hindi-tpu-electra**
<a href="https://colab.resear... |
facebook/deit-base-distilled-patch16-224 | a0fc9b37fdb63c112e76104f669208784ecfe4ea | 2022-07-13T11:39:38.000Z | [
"pytorch",
"tf",
"deit",
"image-classification",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/deit-base-distilled-patch16-224 | 13,553 | 11 | transformers | 602 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
---
# Distilled Data-efficient Image Transformer (base-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first... |
sshleifer/distilbart-xsum-1-1 | 891968fcbb0e421075cc2c3dfc8da8d4b24d54a4 | 2021-06-14T07:53:57.000Z | [
"pytorch",
"tf",
"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-1-1 | 13,547 | null | transformers | 603 | ---
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... |
pysentimiento/robertuito-hate-speech | 272493f45c85fd9b6590716d0206443f2ce79731 | 2021-12-02T21:50:23.000Z | [
"pytorch",
"roberta",
"text-classification",
"es",
"arxiv:2106.09462",
"arxiv:2111.09453",
"transformers",
"twitter",
"hate-speech"
] | text-classification | false | pysentimiento | null | pysentimiento/robertuito-hate-speech | 13,537 | 3 | transformers | 604 | ---
language:
- es
tags:
- twitter
- hate-speech
---
# Hate Speech detection in Spanish
## robertuito-hate-speech
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speec... |
xlnet-large-cased | 09792d2c42dfb606155f1bea8873260b23887edd | 2021-09-16T09:45:20.000Z | [
"pytorch",
"tf",
"xlnet",
"text-generation",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1906.08237",
"transformers",
"license:mit"
] | text-generation | false | null | null | xlnet-large-cased | 13,523 | 1 | transformers | 605 | ---
language: en
license: mit
datasets:
- bookcorpus
- wikipedia
---
# XLNet (large-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released i... |
j-hartmann/sentiment-roberta-large-english-3-classes | f995433eb6d79d26702ab9335bfde472a9933ee4 | 2022-02-06T12:26:00.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"transformers",
"sentiment",
"twitter"
] | text-classification | false | j-hartmann | null | j-hartmann/sentiment-roberta-large-english-3-classes | 13,452 | 3 | transformers | 606 | ---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "Oh no. This is bad.."
- text: "To be or not to be."
- text: "Oh Happy Day"
---
This RoBERTa-based model can classify the sentiment of English language text in 3 classes:
- positive 😀
- neutral 😐
- negative 🙁
The model was fine-tuned on 5,... |
sultan/BioM-ELECTRA-Large-Discriminator | dac81e9477f7f7e00b2dc79574500fb3072e59c7 | 2021-10-12T21:25:07.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | sultan | null | sultan/BioM-ELECTRA-Large-Discriminator | 13,387 | 1 | transformers | 607 | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer m... |
nghuyong/ernie-2.0-large-en | 4770fb35e20abf0e2ed2ba0a70faec4fc55b5d2b | 2021-05-20T01:45:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"arxiv:1907.12412",
"transformers"
] | null | false | nghuyong | null | nghuyong/ernie-2.0-large-en | 13,381 | 3 | transformers | 608 | # ERNIE-2.0-large
## Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019,
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks o... |
PlanTL-GOB-ES/roberta-large-bne | e30d1acb98f8cd8339e31464ffdb35413ac5a003 | 2022-04-06T14:41:03.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/roberta-large-bne | 13,312 | 5 | transformers | 609 | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
widget:
- text: "Este año las campanadas de La Sexta las <mask> Pedroche y Chicote."
- text: "El artista Antonio Orozco es un colaborador de La <mask>."
- text: "Gracias a los datos de la... |
naver/splade-cocondenser-ensembledistil | 25178a62708a3ab1b5c4b5eb30764d65bfddcfbb | 2022-05-11T08:05:37.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:ms_marco",
"arxiv:2205.04733",
"transformers",
"splade",
"query-expansion",
"document-expansion",
"bag-of-words",
"passage-retrieval",
"knowledge-distillation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | naver | null | naver/splade-cocondenser-ensembledistil | 13,295 | 6 | transformers | 610 | ---
license: cc-by-nc-sa-4.0
language: "en"
tags:
- splade
- query-expansion
- document-expansion
- bag-of-words
- passage-retrieval
- knowledge-distillation
datasets:
- ms_marco
---
## SPLADE CoCondenser EnsembleDistil
SPLADE model for passage retrieval. For additional details, please visit:
* paper: https://arxiv.o... |
microsoft/beit-base-patch16-224-pt22k-ft22k | e10357ec82ca7a0de830c20ffc715b6c33cde963 | 2022-01-28T10:17:47.000Z | [
"pytorch",
"jax",
"beit",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/beit-base-patch16-224-pt22k-ft22k | 13,243 | 6 | transformers | 611 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-21k
---
# BEiT (base-sized model, fine-tuned on ImageNet-22k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fin... |
KoboldAI/fairseq-dense-13B-Nerys-v2 | 45d7cf7f2a4f285bbe36a3421f2497c925f86ef4 | 2022-06-25T11:07:32.000Z | [
"pytorch",
"xglm",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-13B-Nerys-v2 | 13,190 | null | transformers | 612 | ---
language: en
license: mit
---
# Fairseq-dense 13B - Nerys
## Model Description
Fairseq-dense 13B-Nerys is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels"... |
redewiedergabe/bert-base-historical-german-rw-cased | 011b11dc5bf01e1ac3423bfb1593ce58d9938d37 | 2021-05-20T04:11:23.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"arxiv:1508.01991",
"transformers",
"autotrain_compatible"
] | fill-mask | false | redewiedergabe | null | redewiedergabe/bert-base-historical-german-rw-cased | 13,183 | 1 | transformers | 613 | ---
language: de
---
# Model description
## Dataset
Trained on fictional and non-fictional German texts written between 1840 and 1920:
* Narrative texts from Digitale Bibliothek (https://textgrid.de/digitale-bibliothek)
* Fairy tales and sagas from Grimm Korpus (https://www1.ids-mannheim.de/kl/projekte/korpora/archiv/... |
textattack/roberta-base-CoLA | 3ccf3a400f2fa75ff257eac171047603ffbe84f1 | 2021-05-20T22:05:35.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-CoLA | 13,151 | 1 | transformers | 614 | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... |
nreimers/MiniLM-L6-H384-uncased | 3276f0fac9d818781d7a1327b3ff818fc4e643c0 | 2021-08-30T20:05:29.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | nreimers | null | nreimers/MiniLM-L6-H384-uncased | 13,124 | 8 | transformers | 615 | ---
license: mit
---
## MiniLM: 6 Layer Version
This is a 6 layer version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased/) by keeping only every second layer. |
dandelin/vilt-b32-mlm | 9507e9c3da12076e10f272e942569dc5190edc1c | 2022-07-06T12:18:37.000Z | [
"pytorch",
"vilt",
"fill-mask",
"arxiv:2102.03334",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | dandelin | null | dandelin/vilt-b32-mlm | 13,056 | 2 | transformers | 616 | ---
license: apache-2.0
---
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.0... |
sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens | 9c71022cc69f395526255204c7af8bea1cc252d8 | 2022-06-15T20:21:25.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens | 13,041 | null | sentence-transformers | 617 | ---
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... |
nlpaueb/legal-bert-small-uncased | 0e23f7a9a39f59768ea7e09766d8ee308580fb17 | 2022-04-28T14:43:32.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"transformers",
"legal",
"license:cc-by-sa-4.0",
"fill-mask"
] | fill-mask | false | nlpaueb | null | nlpaueb/legal-bert-small-uncased | 12,944 | 5 | transformers | 618 | ---
language: en
pipeline_tag: fill-mask
license: cc-by-sa-4.0
thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png
tags:
- legal
widget:
- text: "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police."
---
# LEGAL-BERT: The Mupp... |
sberbank-ai/ruT5-base | 8940daf014ad31b5b619cb07429dd50c884882f1 | 2021-09-21T19:41:58.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"transformers",
"PyTorch",
"Transformers",
"autotrain_compatible"
] | text2text-generation | false | sberbank-ai | null | sberbank-ai/ruT5-base | 12,891 | 2 | transformers | 619 | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/model-zoo"
---
# ruT5-base
Model was trained by [SberDevices](https://sberdevices.ru/) team.
* Task: `text2text generation`
* Type: `encoder-decoder`
* Tokenizer: `bpe`
* Dict size: `32 101`
* Num Parameters: `222 M`
* Train... |
skt/kobert-base-v1 | a9f5849fce18fb088f0cd0f9b29ec3f756958464 | 2021-07-01T07:16:05.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | skt | null | skt/kobert-base-v1 | 12,864 | 5 | transformers | 620 | Please refer here. https://github.com/SKTBrain/KoBERT |
google/bert_uncased_L-4_H-256_A-4 | 387825ce42dbb39b87911cdf8e383ee3b25184f8 | 2021-05-19T17:30:27.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-4_H-256_A-4 | 12,826 | null | transformers | 621 | ---
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... |
cointegrated/LaBSE-en-ru | 9e6d1e5fa79584f5346a5262b69bb6dc64b46f98 | 2022-06-23T12:43:04.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"ru",
"en",
"arxiv:2007.01852",
"transformers",
"feature-extraction",
"embeddings",
"sentence-similarity"
] | feature-extraction | false | cointegrated | null | cointegrated/LaBSE-en-ru | 12,813 | 10 | transformers | 622 | ---
language: ["ru", "en"]
tags:
- feature-extraction
- embeddings
- sentence-similarity
---
# LaBSE for English and Russian
This is a truncated version of [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is, in turn, a port of [LaBSE](https://tfhub.dev/google/LaBSE/1) by Google.... |
Maltehb/danish-bert-botxo | 565d9bd5ca0872bec0b2d7af7887607a96416c2f | 2021-11-12T08:34:29.000Z | [
"pytorch",
"jax",
"bert",
"pretraining",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"dataset:dindebat.dk",
"dataset:hestenettet.dk",
"dataset:danish OpenSubtitles",
"transformers",
"danish",
"masked-lm",
"Certainly",
"license:cc-by-4.0",
"fill-mask"
] | fill-mask | false | Maltehb | null | Maltehb/danish-bert-botxo | 12,759 | 2 | transformers | 623 | ---
language: da
tags:
- danish
- bert
- masked-lm
- Certainly
license: cc-by-4.0
datasets:
- common_crawl
- wikipedia
- dindebat.dk
- hestenettet.dk
- danish OpenSubtitles
pipeline_tag: fill-mask
widget:
- text: "København er [MASK] i Danmark."
---
# Danish BERT (version 2, uncased) by [Certainly](https://certainly.i... |
Helsinki-NLP/opus-mt-roa-en | 7d547ac627a5bee2cbeb5e614f9752927eb52aab | 2020-08-21T14:42:49.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"it",
"ca",
"rm",
"es",
"ro",
"gl",
"co",
"wa",
"pt",
"oc",
"an",
"id",
"fr",
"ht",
"roa",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-roa-en | 12,727 | 1 | transformers | 624 | ---
language:
- it
- ca
- rm
- es
- ro
- gl
- co
- wa
- pt
- oc
- an
- id
- fr
- ht
- roa
- en
tags:
- translation
license: apache-2.0
---
### roa-eng
* source group: Romance languages
* target group: English
* OPUS readme: [roa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/roa-eng/R... |
thatdramebaazguy/roberta-base-squad | a7a26fd500148b760ca89a87edcd5b5605daab09 | 2022-07-01T19:12:50.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"question-answering",
"English",
"dataset:SQuAD",
"transformers",
"roberta-base",
"qa",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | thatdramebaazguy | null | thatdramebaazguy/roberta-base-squad | 12,654 | 1 | transformers | 625 | ---
datasets:
- SQuAD
language:
- English
thumbnail:
tags:
- roberta
- roberta-base
- question-answering
- qa
license: cc-by-4.0
---
# roberta-base + SQuAD QA
Objective:
This is Roberta Base trained to do the SQuAD Task. This makes a QA model capable of answering questions.
```
model_name = "thatdramebaaz... |
shahrukhx01/question-vs-statement-classifier | 38767ed252cb888c5c1507ea7933150537500893 | 2021-08-25T08:17:39.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"neural-search-query-classification",
"neural-search"
] | text-classification | false | shahrukhx01 | null | shahrukhx01/question-vs-statement-classifier | 12,639 | 6 | transformers | 626 | ---
language: "en"
tags:
- neural-search-query-classification
- neural-search
widget:
- text: "what did you eat in lunch?"
---
# KEYWORD STATEMENT VS QUESTION CLASSIFIER FOR NEURAL SEARCH
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(... |
Zixtrauce/JohnBot | 3831946b0c973685e937c4db612ed7cce2733129 | 2022-01-02T06:43:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Zixtrauce | null | Zixtrauce/JohnBot | 12,482 | null | transformers | 627 | ---
tags:
- conversational
---
#JohnBot |
Helsinki-NLP/opus-mt-en-nl | cd888e7c566d69f91f675d7a12d26613d1c4d826 | 2021-09-09T21:38:05.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"en",
"nl",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-nl | 12,407 | null | transformers | 628 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-nl
* source languages: en
* target languages: nl
* OPUS readme: [en-nl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-nl/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
haisongzhang/roberta-tiny-cased | 2e8caf4404987b8cb20fa4b22955f56940b2ebc6 | 2021-05-19T17:53:53.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | haisongzhang | null | haisongzhang/roberta-tiny-cased | 12,316 | null | transformers | 629 | Github: https://github.com/haisongzhang/roberta-tiny-cased
|
deepset/gbert-base-germandpr-ctx_encoder | 12ac6df80a8a3a3301464a306cd412d01f43c082 | 2021-10-21T12:17:10.000Z | [
"pytorch",
"dpr",
"de",
"dataset:deepset/germandpr",
"transformers",
"exbert",
"license:mit"
] | null | false | deepset | null | deepset/gbert-base-germandpr-ctx_encoder | 12,308 | 5 | transformers | 630 | ---
language: de
datasets:
- deepset/germandpr
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
... |
Maltehb/aelaectra-danish-electra-small-cased | 106e95e10eef7b40441db59d1ac98bef3d78dd0a | 2021-11-23T06:39:44.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"da",
"dataset:DAGW",
"arxiv:2003.10555",
"arxiv:1810.04805",
"arxiv:2005.03521",
"transformers",
"ælæctra",
"danish",
"ELECTRA-Small",
"replaced token detection",
"license:mit",
"co2_eq_emissions"
] | null | false | Maltehb | null | Maltehb/aelaectra-danish-electra-small-cased | 12,267 | 1 | transformers | 631 | ---
language: "da"
co2_eq_emissions: 4009.5
tags:
- ælæctra
- pytorch
- danish
- ELECTRA-Small
- replaced token detection
license: "mit"
datasets:
- DAGW
metrics:
- f1
---
# Ælæctra - A Step Towards More Efficient Danish Natural Language Processing
**Ælæctra** is a Danish Transformer-based language model created to e... |
microsoft/Multilingual-MiniLM-L12-H384 | f8a8e5023cbd4f94f1debed2578c65964dd4846b | 2021-06-01T14:33:36.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"arxiv:2002.10957",
"arxiv:1809.05053",
"arxiv:1911.02116",
"arxiv:1910.07475",
"transformers",
"text-classification",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/Multilingual-MiniLM-L12-H384 | 12,245 | 14 | transformers | 632 | ---
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression o... |
KoboldAI/GPT-Neo-2.7B-Horni | 6024c30cd1984b12fa35c50e1490c73e42cf4823 | 2021-12-30T11:43:31.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-Neo-2.7B-Horni | 12,012 | null | transformers | 633 | Entry not found |
hf-internal-testing/tiny-random-wav2vec2 | 44e60a7cad2409b873242c874476c0c8ce8e98b0 | 2021-10-06T10:02:54.000Z | [
"pytorch",
"tf",
"wav2vec2",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-wav2vec2 | 12,005 | null | transformers | 634 | Entry not found |
mrm8488/t5-base-finetuned-span-sentiment-extraction | 04a3fe1f7373c1f33b82e9fb06d2b2635e0fc5a0 | 2021-08-23T21:29:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:1910.10683",
"transformers",
"sentiment",
"extracion",
"passage",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-span-sentiment-extraction | 11,961 | 8 | transformers | 635 | ---
language: en
tags:
- sentiment
- extracion
- passage
widget:
- text: "question: positive context: On the monday, so i wont be able to be with you! i love you"
---
# T5-base fine-tuned for Sentiment Span Extraction
All credits to [Lorenzo Ampil](https://twitter.com/AND__SO)
[Google's T5](https://ai.googleblog.co... |
allenai/tk-instruct-11b-def | 60a86546f3005d9b3685fde089d67e61c281211d | 2022-05-27T06:29:34.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:natural instructions v2.0",
"arxiv:1910.10683",
"arxiv:2204.07705",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/tk-instruct-11b-def | 11,959 | 2 | transformers | 636 | ---
language: en
license: apache-2.0
datasets:
- natural instructions v2.0
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built... |
benjamin/gerpt2-large | d0ec9b299d7a96e24d03303de120ffb81769f366 | 2022-05-11T09:16:51.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"de",
"transformers",
"license:mit"
] | text-generation | false | benjamin | null | benjamin/gerpt2-large | 11,920 | 6 | transformers | 637 | ---
language: de
widget:
- text: "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einhörner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."
license: mit
---
# GerPT2
German large and small versions of GPT2:
- https://huggingface.co/benjamin/gerpt2
- https://huggingface... |
Helsinki-NLP/opus-mt-uk-ru | 0dbf7d9872d43f0599f41259b927e012f84b87fe | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"uk",
"ru",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-uk-ru | 11,691 | null | transformers | 638 | ---
language:
- uk
- ru
tags:
- translation
license: apache-2.0
---
### ukr-rus
* source group: Ukrainian
* target group: Russian
* OPUS readme: [ukr-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-rus/README.md)
* model: transformer-align
* source language(s): ukr
* target langu... |
hf-internal-testing/tiny-bert | 8a4db81d7e7ef2296d71ceb206e048c5734ce42f | 2021-07-08T18:23:09.000Z | [
"pytorch",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-bert | 11,632 | null | transformers | 639 | This is a copy of: https://huggingface.co/prajjwal1/bert-tiny
|
allenai/led-large-16384 | 04472a9a5d3af2efe700dda11da6063c68cd27a4 | 2021-01-11T14:51:13.000Z | [
"pytorch",
"tf",
"led",
"text2text-generation",
"en",
"arxiv:2004.05150",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/led-large-16384 | 11,624 | 2 | transformers | 640 | ---
language: en
license: apache-2.0
---
## Introduction
[Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer).
As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-large-16384* w... |
ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt | cf511f7e00de089e67e80fbedd5fbfb8e76ea067 | 2021-04-01T14:09:29.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"zh",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | ydshieh | null | ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt | 11,617 | 9 | transformers | 641 | ---
language: zh
datasets:
- common_voice
metrics:
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53 - Chinese (zh-CN), by Yih-Dar SHIEH
results:
- task:
name: Speech Recognition
type: automatic-speech-rec... |
sentence-transformers/multi-qa-MiniLM-L6-dot-v1 | cffdcf0082a1156dce96de91b456a8859adc67b2 | 2022-06-15T22:45:19.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/multi-qa-MiniLM-L6-dot-v1 | 11,558 | 4 | sentence-transformers | 642 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa-MiniLM-L6-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**.... |
deepset/roberta-base-squad2-distilled | 13d15d60b8fb45dc19c73a13bbd9a523811bca59 | 2022-07-22T16:29:09.000Z | [
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"exbert",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/roberta-base-squad2-distilled | 11,514 | 2 | transformers | 643 | ---
language: en
datasets:
- squad_v2
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
model-index:
- name: deepset/roberta-base-squad2-distilled
results:
- task:
type: question-answering
name: Question Answ... |
Helsinki-NLP/opus-mt-bat-en | 760a39c69d5158182c320e47986306e24e96fa1f | 2021-01-18T07:48:50.000Z | [
"pytorch",
"marian",
"text2text-generation",
"lt",
"lv",
"bat",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-bat-en | 11,478 | null | transformers | 644 | ---
language:
- lt
- lv
- bat
- en
tags:
- translation
license: apache-2.0
---
### bat-eng
* source group: Baltic languages
* target group: English
* OPUS readme: [bat-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bat-eng/README.md)
* model: transformer
* source language(s): lav li... |
zenham/wail_m_e4_16h_2k | 5ac007e21763708150470814fc800ab1e72c9582 | 2022-03-10T03:06:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | zenham | null | zenham/wail_m_e4_16h_2k | 11,459 | null | transformers | 645 | ---
tags:
- conversational
---
#wail m e4 16h 2k DialoGPT Model |
clips/mfaq | a49f0160e4d170a3d12e90ace3990fd6a50e35aa | 2021-10-15T06:21:13.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"cs",
"da",
"de",
"en",
"es",
"fi",
"fr",
"he",
"hr",
"hu",
"id",
"it",
"nl",
"no",
"pl",
"pt",
"ro",
"ru",
"sv",
"tr",
"vi",
"dataset:clips/mfaq",
"arxiv:2109.12870",
"sentence-transformers",
"sentence-sim... | sentence-similarity | false | clips | null | clips/mfaq | 11,437 | 11 | sentence-transformers | 646 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
language:
- cs
- da
- de
- en
- es
- fi
- fr
- he
- hr
- hu
- id
- it
- nl
- 'no'
- pl
- pt
- ro
- ru
- sv
- tr
- vi
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- clips/mfaq
widget:
source_sentence: "<Q>How... |
indolem/indobert-base-uncased | b6663c19a819c04798e7a93d681f9bc34ed57b4a | 2021-09-17T04:06:54.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"id",
"dataset:220M words (IndoWiki, IndoWC, News)]",
"arxiv:2011.00677",
"transformers",
"indobert",
"indolem",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | indolem | null | indolem/indobert-base-uncased | 11,412 | 6 | transformers | 647 | ---
language: id
tags:
- indobert
- indolem
license: mit
inference: False
datasets:
- 220M words (IndoWiki, IndoWC, News)]
---
## About
[IndoBERT](https://arxiv.org/pdf/2011.00677.pdf) is the Indonesian version of BERT model. We train the model using over 220M words, aggregated from three main sources:
* Indonesian ... |
beomi/kcbert-base | 99fc27ea7d643d8377ade8912c6c445a5e3861be | 2022-07-20T01:56:19.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"ko",
"arxiv:1810.04805",
"transformers",
"korean",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | beomi | null | beomi/kcbert-base | 11,349 | null | transformers | 648 | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KcBERT: Korean comments BERT
** Updates on 2021.04.07 **
- KcELECTRA가 릴리즈 되었습니다!🤗
- KcELECTRA는 보다 더 많은 데이터셋, 그리고 더 큰 General vocab을 통해 KcBERT 대비 **모든 태스크에서 더 높은 성능**을 보입니다.
- 아래 깃헙 링크에서 직접 사용해보세요!
- https://github.com/Beomi/KcELECTRA
** Updates on 2021.03... |
allenai/biomed_roberta_base | 6209646a5f79bbd383f8193d70e88ab00ae779f8 | 2021-05-20T13:00:31.000Z | [
"pytorch",
"jax",
"roberta",
"transformers"
] | null | false | allenai | null | allenai/biomed_roberta_base | 11,329 | 7 | transformers | 649 | ---
thumbnail: https://huggingface.co/front/thumbnails/allenai.png
---
# BioMed-RoBERTa-base
BioMed-RoBERTa-base is a language model based on the RoBERTa-base (Liu et. al, 2019) architecture. We adapt RoBERTa-base to 2.68 million scientific papers from the [Semantic Scholar](https://www.semanticscholar.org) corpus vi... |
Narasimha/hinglish-distilbert | e58ddfc465f1027aa1c54092992247bd310d97c7 | 2022-05-05T08:45:20.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"license:mit"
] | text-classification | false | Narasimha | null | Narasimha/hinglish-distilbert | 11,266 | null | transformers | 650 | ---
license: mit
---
|
csarron/bert-base-uncased-squad-v1 | 31129bdd485e06a6bec8fd2e045256369db34b0b | 2021-05-19T14:32:38.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad",
"transformers",
"bert-base",
"license:mit",
"autotrain_compatible"
] | question-answering | false | csarron | null | csarron/bert-base-uncased-squad-v1 | 11,230 | null | transformers | 651 | ---
language: en
thumbnail:
license: mit
tags:
- question-answering
- bert
- bert-base
datasets:
- squad
metrics:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amaz... |
Babelscape/rebel-large | d24237e8ab9c1ad2cbdf53fd54b0d7cda1da8018 | 2022-05-27T10:20:40.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:Babelscape/rebel-dataset",
"transformers",
"seq2seq",
"relation-extraction",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Babelscape | null | Babelscape/rebel-large | 11,194 | 22 | transformers | 652 | ---
language:
- en
widget:
- text: "Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic"
tags:
- seq2seq
- relation-extraction
datasets:
- Babelscape/rebel-dataset
model-index:
- name: REBEL
results:
- task:
name: Relation E... |
rinna/japanese-roberta-base | 1559edfaaadefcb1661c016455990b0f6f68b20d | 2021-09-13T00:46:53.000Z | [
"pytorch",
"tf",
"roberta",
"fill-mask",
"ja",
"dataset:cc100",
"dataset:wikipedia",
"transformers",
"japanese",
"masked-lm",
"nlp",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | rinna | null | rinna/japanese-roberta-base | 11,187 | 13 | transformers | 653 | ---
language: ja
thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png
tags:
- ja
- japanese
- roberta
- masked-lm
- nlp
license: mit
datasets:
- cc100
- wikipedia
mask_token: "[MASK]"
widget:
- text: "[CLS]4年に1度[MASK]は開かれる。"
---
# japanese-roberta-base
This repository ... |
microsoft/prophetnet-large-uncased | f8218576b32128d7623ad24f3f25dce10f3d1b01 | 2021-03-04T20:24:09.000Z | [
"pytorch",
"rust",
"prophetnet",
"text2text-generation",
"en",
"arxiv:2001.04063",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | microsoft | null | microsoft/prophetnet-large-uncased | 11,179 | null | transformers | 654 | ---
language: en
---
## prophetnet-large-uncased
Pretrained weights for [ProphetNet](https://arxiv.org/abs/2001.04063).
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNet is able to predict more future ... |
Zixtrauce/BaekBot | 8dd7cbc7e2f1f6d711330aabb4a165d87a8dc7f5 | 2021-12-31T08:30:32.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Zixtrauce | null | Zixtrauce/BaekBot | 11,165 | null | transformers | 655 | ---
tags:
- conversational
---
#BaekBot |
Salesforce/codet5-base | 4078456db09ba972a3532827a0b5df4da172323c | 2021-11-23T09:53:41.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:code_search_net",
"arxiv:2109.00859",
"arxiv:1909.09436",
"transformers",
"codet5",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | Salesforce | null | Salesforce/codet5-base | 11,146 | 27 | transformers | 656 | ---
license: apache-2.0
tags:
- codet5
datasets:
- code_search_net
inference: false
---
# CodeT5 (base-sized model)
Pre-trained CodeT5 model. It was introduced in the paper [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
for Code Understanding and Generation](https://arxiv.org/abs/2109.00859) by... |
aliosm/sha3bor-footer-51-arabertv02-base | 3538a19ad8c8efa08ab46e4273a9e28a8db097d5 | 2022-05-28T09:34:58.000Z | [
"pytorch",
"bert",
"text-classification",
"ar",
"transformers",
"license:mit"
] | text-classification | false | aliosm | null | aliosm/sha3bor-footer-51-arabertv02-base | 11,140 | null | transformers | 657 | ---
language: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور"
- text: "إذا ما فعلت الخير ضوعف شرهم"
- text: "واحر قلباه ممن قلبه شبم"
---
|
sentence-transformers/sentence-t5-large | 266640df151776ad39f66a2595b81c97ae678195 | 2022-02-09T14:01:09.000Z | [
"pytorch",
"t5",
"en",
"arxiv:2108.08877",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/sentence-t5-large | 11,115 | 2 | sentence-transformers | 658 | ---
pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/sentence-t5-large
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a ... |
ckiplab/bert-base-chinese | efe27bb4a9373384e0120ffe1cf327714ceb61bf | 2022-05-10T03:28:12.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"zh",
"transformers",
"lm-head",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | ckiplab | null | ckiplab/bert-base-chinese | 11,108 | 5 | transformers | 659 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-o... |
aliosm/sha3bor-rhyme-detector-arabertv02-base | 6b25d49b178fc51b74a7f7ad0461c6fee7a3105b | 2022-05-28T09:34:23.000Z | [
"pytorch",
"bert",
"text-classification",
"ar",
"transformers",
"license:mit"
] | text-classification | false | aliosm | null | aliosm/sha3bor-rhyme-detector-arabertv02-base | 11,047 | null | transformers | 660 | ---
language: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور [شطر] قتلننا ثم لم يحيين قتلانا"
- text: "إذا ما فعلت الخير ضوعف شرهم [شطر] وكل إناء بالذي فيه ينضح"
- text: "واحر قلباه ممن قلبه شبم [شطر] ومن بجسمي وحالي عنده سقم"
---
|
aliosm/sha3bor-metre-detector-arabertv02-base | 5cc8cfa4c8ed69b642cd3527941d13830c8c1945 | 2022-05-28T09:34:12.000Z | [
"pytorch",
"bert",
"text-classification",
"ar",
"transformers",
"license:mit"
] | text-classification | false | aliosm | null | aliosm/sha3bor-metre-detector-arabertv02-base | 11,030 | null | transformers | 661 | ---
language: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور [شطر] قتلننا ثم لم يحيين قتلانا"
- text: "إذا ما فعلت الخير ضوعف شرهم [شطر] وكل إناء بالذي فيه ينضح"
- text: "واحر قلباه ممن قلبه شبم [شطر] ومن بجسمي وحالي عنده سقم"
---
|
sentence-transformers/msmarco-distilbert-base-tas-b | 1de916fb3ec96493f57c9f0349a0d9e338ed64c2 | 2022-06-15T21:37:05.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-distilbert-base-tas-b | 10,954 | 4 | sentence-transformers | 662 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilbert-base-tas-b
This is a port of the [DistilBert TAS-B Model](https://huggingface.co/sebastian-hofstaetter/distilbert-dot-tas_b-b... |
rycont/biblify | 4bd8b40b9570dc2ad5389b22a8f78a69b0a21389 | 2022-07-14T01:52:55.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | rycont | null | rycont/biblify | 10,945 | null | transformers | 663 | Entry not found |
facebook/vit-mae-base | 87dd4faac12498cde93a176406329112584c0413 | 2022-03-29T16:18:27.000Z | [
"pytorch",
"tf",
"vit_mae",
"pretraining",
"dataset:imagenet-1k",
"arxiv:2111.06377",
"transformers",
"vision",
"license:apache-2.0"
] | null | false | facebook | null | facebook/vit-mae-base | 10,939 | 2 | transformers | 664 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-1k
---
# Vision Transformer (base-sized model) pre-trained with MAE
Vision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaimin... |
aliosm/sha3bor-poetry-diacritizer-canine-s | ac91eec76844bfcd9159c6063c2828bdc61e65d4 | 2022-05-28T09:41:28.000Z | [
"pytorch",
"canine",
"token-classification",
"ar",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | aliosm | null | aliosm/sha3bor-poetry-diacritizer-canine-s | 10,913 | null | transformers | 665 | ---
language: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور [شطر] قتلننا ثم لم يحيين قتلانا"
- text: "إذا ما فعلت الخير ضوعف شرهم [شطر] وكل إناء بالذي فيه ينضح"
- text: "واحر قلباه ممن قلبه شبم [شطر] ومن بجسمي وحالي عنده سقم"
---
|
dbmdz/german-gpt2 | f0edef6d975b1338bae533502e1dae74974cb2d2 | 2021-10-22T08:58:57.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"de",
"transformers",
"license:mit"
] | text-generation | false | dbmdz | null | dbmdz/german-gpt2 | 10,875 | 6 | transformers | 666 | ---
language: de
widget:
- text: "Heute ist sehr schönes Wetter in"
license: mit
---
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good o... |
albert-xxlarge-v1 | 431c690c9f508b1cbcba8f475974833ac646d41c | 2021-01-13T15:32:02.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | albert-xxlarge-v1 | 10,863 | 1 | transformers | 667 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XXLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-r... |
cointegrated/rubert-tiny2 | a53f0afc4b34c94012191a043c52e2271fa23f27 | 2022-06-30T14:26:53.000Z | [
"pytorch",
"bert",
"pretraining",
"ru",
"transformers",
"russian",
"fill-mask",
"embeddings",
"masked-lm",
"tiny",
"feature-extraction",
"sentence-similarity",
"license:mit"
] | feature-extraction | false | cointegrated | null | cointegrated/rubert-tiny2 | 10,824 | 7 | transformers | 668 | ---
language: ["ru"]
tags:
- russian
- fill-mask
- pretraining
- embeddings
- masked-lm
- tiny
- feature-extraction
- sentence-similarity
license: mit
widget:
- text: "Миниатюрная модель для [MASK] разных задач."
---
This is an updated version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-ti... |
deepset/electra-base-squad2 | 3f5fb835e980f8bcb97cfbdf33afec8acfb9f45e | 2022-07-26T11:04:45.000Z | [
"pytorch",
"electra",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/electra-base-squad2 | 10,788 | 6 | transformers | 669 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/electra-base-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- name: Exac... |
sberbank-ai/ruclip-vit-base-patch32-224 | 95a185fc2b0b1b7fa54c651bcb1fab292b4a3faf | 2022-01-09T21:34:27.000Z | [
"pytorch",
"transformers"
] | null | false | sberbank-ai | null | sberbank-ai/ruclip-vit-base-patch32-224 | 10,770 | null | transformers | 670 | # ruclip-vit-base-patch32-224
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processi... |
Helsinki-NLP/opus-mt-hi-en | 48acb8543e96768bc9fea5b3813d0e46fe1a45f3 | 2021-09-09T22:09:54.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"hi",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-hi-en | 10,743 | 2 | transformers | 671 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-hi-en
* source languages: hi
* target languages: en
* OPUS readme: [hi-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hi-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
cl-tohoku/bert-large-japanese | 0f5fc4dcd523bed677d208cfa7279c80b1e8e8dc | 2021-09-23T13:45:41.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | cl-tohoku | null | cl-tohoku/bert-large-japanese | 10,714 | null | transformers | 672 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東北大学で[MASK]の研究をしています。
---
# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model p... |
dbmdz/bert-base-turkish-128k-cased | ee962e2ecfaafa8cf708fa961f5cbc4346b1c367 | 2021-05-19T15:10:48.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"tr",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/bert-base-turkish-128k-cased | 10,655 | 7 | transformers | 673 | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased model for Turkish 🎉
# 🇹🇷 BERTurk
BERTurk is a community-driven cased BERT model for Turkish.
Some datasets used for pretraining and eval... |
seyonec/PubChem10M_SMILES_BPE_450k | c18fccd09b3326bf2d4633412c256d7db872156d | 2021-05-20T21:02:39.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/PubChem10M_SMILES_BPE_450k | 10,609 | 1 | transformers | 674 | Entry not found |
UWB-AIR/Czert-B-base-cased | 93c1b4ee6603cea828b217e6bb84e369e9b142f0 | 2022-03-16T10:39:50.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"arxiv:2103.13031",
"transformers",
"cs",
"fill-mask"
] | fill-mask | false | UWB-AIR | null | UWB-AIR/Czert-B-base-cased | 10,382 | null | transformers | 675 | ---
tags:
- cs
- fill-mask
---
# CZERT
This repository keeps trained Czert-B model for the paper [Czert – Czech BERT-like Model for Language Representation
](https://arxiv.org/abs/2103.13031)
For more information, see the paper
## Available Models
You can download **MLM & NSP only** pretrained models
~~[CZERT-A-v1](... |
LiYuan/amazon-review-sentiment-analysis | 0aacda6423e43213da4e50a0f30cfcdb42a5c725 | 2022-04-30T22:03:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | LiYuan | null | LiYuan/amazon-review-sentiment-analysis | 10,370 | 0 | transformers | 676 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and ... |
knkarthick/MEETING_SUMMARY | 1d9f2261609ed4970abf4f3659c080783beaf09e | 2022-06-29T07:16:14.000Z | [
"pytorch",
"tf",
"bart",
"text2text-generation",
"en",
"dataset:cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI",
"transformers",
"seq2seq",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | knkarthick | null | knkarthick/MEETING_SUMMARY | 10,311 | 14 | transformers | 677 | ---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI
metrics:
- rouge
widget:
- text: |-
Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the project is. Designing a remo... |
hfl/rbt3 | 0aa0527ff4170f29e1dfd3eb6ef60dc67e1bf75c | 2021-05-19T19:19:45.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | hfl | null | hfl/rbt3 | 10,305 | 4 | transformers | 678 | ---
language:
- zh
tags:
- bert
license: "apache-2.0"
pipeline_tag: "fill-mask"
---
# This is a re-trained 3-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Tra... |
bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12 | a0ce2a0e0feb8f4caf0346b139266f5320b90322 | 2021-09-24T07:45:33.000Z | [
"pytorch",
"en",
"dataset:pubmed",
"transformers",
"bluebert",
"license:cc0-1.0"
] | null | false | bionlp | null | bionlp/bluebert_pubmed_uncased_L-12_H-768_A-12 | 10,301 | 1 | transformers | 679 | ---
language:
- en
tags:
- bluebert
license: cc0-1.0
datasets:
- pubmed
---
# BlueBert-Base, Uncased, PubMed
## Model description
A BERT model pre-trained on PubMed abstracts
## Intended uses & limitations
#### How to use
Please see https://github.com/ncbi-nlp/bluebert
## Training data
We provide [preprocesse... |
howey/bert-base-uncased-sst2 | 4463eee18b0108806303203ebfb9cd91a88a96fa | 2021-05-26T08:29:25.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | howey | null | howey/bert-base-uncased-sst2 | 10,285 | null | transformers | 680 | Entry not found |
facebook/deit-base-patch16-224 | fb2c78a54a5637dec350432794f7b93e31f910c9 | 2022-07-13T11:40:44.000Z | [
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"transformers",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/deit-base-patch16-224 | 10,265 | 5 | transformers | 681 | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet-1k
---
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [... |
ckiplab/bert-base-chinese-ner | 50c5afc0a0131e8ab93f54d9ebf9575af04c22d5 | 2022-05-10T03:28:12.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/bert-base-chinese-ner | 10,198 | 12 | transformers | 682 | ---
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 Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segment... |
rinna/japanese-gpt2-medium | f464b76739c884d8b0479a0a7705b7fa71c3fd5a | 2021-08-23T03:20:17.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"ja",
"dataset:cc100",
"dataset:wikipedia",
"transformers",
"japanese",
"lm",
"nlp",
"license:mit"
] | text-generation | false | rinna | null | rinna/japanese-gpt2-medium | 10,180 | 17 | transformers | 683 | ---
language: ja
thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png
tags:
- ja
- japanese
- gpt2
- text-generation
- lm
- nlp
license: mit
datasets:
- cc100
- wikipedia
widget:
- text: "生命、宇宙、そして万物についての究極の疑問の答えは"
---
# japanese-gpt2-medium
This repository provides a ... |
google/tapas-large-finetuned-wtq | 8ee9ab2e6d2e09fc3c138730f8a144cea6fe5c16 | 2022-07-14T10:13:16.000Z | [
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"transformers",
"license:apache-2.0"
] | table-question-answering | false | google | null | google/tapas-large-finetuned-wtq | 10,178 | 4 | transformers | 684 | ---
language: en
tags:
- tapas
- table-question-answering
license: apache-2.0
datasets:
- wikitablequestions
---
# TAPAS large model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_large_reset` checkpoint o... |
patrickvonplaten/wav2vec2_tiny_random_robust | 358ecf7824b56e9b52b1ffa22ed02680207d3eae | 2021-09-01T14:48:17.000Z | [
"pytorch",
"wav2vec2",
"feature-extraction",
"en",
"dataset:librispeech_asr",
"transformers",
"automatic-speech-recognition",
"license:apache-2.0"
] | feature-extraction | false | patrickvonplaten | null | patrickvonplaten/wav2vec2_tiny_random_robust | 10,152 | null | transformers | 685 | ---
language: en
datasets:
- librispeech_asr
tags:
- automatic-speech-recognition
license: apache-2.0
---
## Test model
To test this model run the following code:
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC
import torchaudio
import torch
ds = load_dataset("patrickvonplaten/li... |
sshleifer/distilbart-cnn-6-6 | d2fde4ca965ba893255479612e4b801aa6500029 | 2021-06-14T07:53:04.000Z | [
"pytorch",
"jax",
"rust",
"bart",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distilbart-cnn-6-6 | 10,144 | 11 | transformers | 686 | ---
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... |
microsoft/SportsBERT | 79260fc3773f96187a55fc2d94c3c897a285b3c9 | 2022-05-05T20:38:36.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/SportsBERT | 10,121 | 4 | transformers | 687 | Pretraining large natural language processing models such as BERT, RoBERTa, etc are now state of the art models in natural language understanding and processing tasks. However, these models are trained on a general corpus of articles from the web or from repositories like quora, wikipedia, etc which contain articles of... |
sbcBI/sentiment_analysis_model | a994fec145b4e096961210b871c376a0ba8440ba | 2022-05-16T18:37:13.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:Confidential",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0"
] | text-classification | false | sbcBI | null | sbcBI/sentiment_analysis_model | 10,074 | null | transformers | 688 | ---
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.... |
google/canine-s | 792aaf916e56cb8470fc3162a75f2fa31f96756a | 2021-08-13T08:23:53.000Z | [
"pytorch",
"canine",
"feature-extraction",
"multilingual",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2103.06874",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | google | null | google/canine-s | 10,062 | 1 | transformers | 689 | ---
language: multilingual
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# CANINE-s (CANINE pre-trained with subword loss)
Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper [CANINE: Pre-training an Efficient Tokenization-Free Encode... |
dbmdz/electra-large-discriminator-finetuned-conll03-english | 7c0a483cbc7c8c27759f5fc38fe0261ce6bda31e | 2020-12-09T18:30:05.000Z | [
"pytorch",
"electra",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dbmdz | null | dbmdz/electra-large-discriminator-finetuned-conll03-english | 10,055 | 5 | transformers | 690 | Entry not found |
asafaya/bert-base-arabic | b61c328d130eabc1bb1b3b4d1448410a961da888 | 2021-05-19T00:05:27.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"dataset:oscar",
"dataset:wikipedia",
"transformers",
"autotrain_compatible"
] | fill-mask | false | asafaya | null | asafaya/bert-base-arabic | 10,010 | 6 | transformers | 691 | ---
language: ar
datasets:
- oscar
- wikipedia
---
# Arabic BERT Model
Pretrained BERT base 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 Speech Iden... |
aliosm/sha3bor-generator-aragpt2-medium | 6a63861ac042b61a1ab5bf3e58df42d96cd07365 | 2022-05-28T09:18:07.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ar",
"transformers",
"license:mit"
] | text-generation | false | aliosm | null | aliosm/sha3bor-generator-aragpt2-medium | 9,994 | null | transformers | 692 | ---
language: ar
license: mit
widget:
- text: "حبيبي"
example_title: "حبيبي"
- text: "يا"
example_title: "يا"
- text: "رسول الله"
example_title: "رسول الله"
---
|
seyonec/ChemBERTa_zinc250k_v2_40k | 460f88413c1b572509b46fbd957a4296ea71e19f | 2021-05-20T20:57:42.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/ChemBERTa_zinc250k_v2_40k | 9,917 | null | transformers | 693 | Entry not found |
allenai/tk-instruct-3b-def-pos | c6b8a84d2d7cc60376733fcb5e17ad1bd3516d0b | 2022-05-27T06:27:30.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:natural instructions v2.0",
"arxiv:1910.10683",
"arxiv:2204.07705",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/tk-instruct-3b-def-pos | 9,892 | null | transformers | 694 | ---
language: en
license: apache-2.0
datasets:
- natural instructions v2.0
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built... |
microsoft/deberta-v2-xxlarge | da58c8f337be794a1bb98c218d0d8cc72c324884 | 2022-01-13T20:00:20.000Z | [
"pytorch",
"tf",
"deberta-v2",
"en",
"arxiv:2006.03654",
"transformers",
"deberta",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-v2-xxlarge | 9,813 | 10 | transformers | 695 | ---
language: en
tags: deberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It ou... |
Salesforce/codet5-small | a642dc934e5475185369d09ac07091dfe72a31fc | 2021-11-23T09:45:34.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:code_search_net",
"arxiv:2109.00859",
"arxiv:1909.09436",
"transformers",
"codet5",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | Salesforce | null | Salesforce/codet5-small | 9,809 | 11 | transformers | 696 | ---
license: apache-2.0
tags:
- codet5
datasets:
- code_search_net
inference: false
---
# CodeT5 (small-sized model)
Pre-trained CodeT5 model. It was introduced in the paper [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
for Code Understanding and Generation](https://arxiv.org/abs/2109.00859) b... |
microsoft/deberta-xlarge-mnli | 5b07a9086c1dbb79981ff7b05b4d1ad83b3af51c | 2022-06-27T15:47:33.000Z | [
"pytorch",
"tf",
"deberta",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v1",
"deberta-mnli",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/deberta-xlarge-mnli | 9,771 | 4 | transformers | 697 | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... |
ahotrod/electra_large_discriminator_squad2_512 | fe230d41e74248eceb366a91e4f8572f358f201c | 2020-12-11T21:31:42.000Z | [
"pytorch",
"tf",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | ahotrod | null | ahotrod/electra_large_discriminator_squad2_512 | 9,769 | 4 | transformers | 698 | ## ELECTRA_large_discriminator language model fine-tuned on SQuAD2.0
### with the following results:
```
"exact": 87.09677419354838,
"f1": 89.98343832723452,
"total": 11873,
"HasAns_exact": 84.66599190283401,
"HasAns_f1": 90.44759839056285,
"HasAns_total": 5928,
"NoAns_exact": 89.52060555088309,
"NoAn... |
ALINEAR/albert-japanese-v2 | 102cec3c2b7bc8483cd9281b6a029279861df66d | 2020-05-04T13:20:53.000Z | [
"pytorch",
"albert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ALINEAR | null | ALINEAR/albert-japanese-v2 | 9,729 | null | transformers | 699 | Entry not found |
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