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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
lewtun/minilm-finetuned-emotion | 2e1ecc37e5edd7eb71dec436923ad199f57825c6 | 2021-11-11T20:44:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | lewtun | null | lewtun/minilm-finetuned-emotion | 50 | null | transformers | 6,000 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: minilm-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: F1
... |
mrm8488/flaubert-small-finetuned-movie-review-sentiment-analysis | 1765cad0225ce82b945e6a2ce6e6d5ea8e42173e | 2021-06-21T10:04:37.000Z | [
"pytorch",
"flaubert",
"text-classification",
"transformers"
] | text-classification | false | mrm8488 | null | mrm8488/flaubert-small-finetuned-movie-review-sentiment-analysis | 50 | null | transformers | 6,001 | Entry not found |
pmthangk09/bert-base-uncased-glue-sst2 | 2bf098c8b26580282044f6e9a0917731456f1fbb | 2021-05-20T02:48:36.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | pmthangk09 | null | pmthangk09/bert-base-uncased-glue-sst2 | 50 | null | transformers | 6,002 | Entry not found |
raynardj/ner-chemical-bionlp-bc5cdr-pubmed | 30dd3edf4b0e1a052259b6e11308e3ac86d0c503 | 2021-11-16T03:19:53.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"dataset:bionlp",
"dataset:bc4cdr",
"transformers",
"ner",
"chemical",
"bionlp",
"bc4cdr",
"bioinfomatics",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | raynardj | null | raynardj/ner-chemical-bionlp-bc5cdr-pubmed | 50 | 2 | transformers | 6,003 | ---
language:
- en
tags:
- ner
- chemical
- bionlp
- bc4cdr
- bioinfomatics
license: apache-2.0
datasets:
- bionlp
- bc4cdr
widget:
- text: "Serotonin receptor 2A (HTR2A) gene polymorphism predicts treatment response to venlafaxine XR in generalized anxiety disorder."
---
# NER to find Gene & Gene products
> The mode... |
rifkat/uztext-3Gb-BPE-Roberta | 0c8749478ad426e25029837134ddca4f0cad4ba7 | 2022-05-06T10:48:06.000Z | [
"pytorch",
"roberta",
"fill-mask",
"uz",
"transformers",
"mit",
"robert",
"uzrobert",
"uzbek",
"cyrillic",
"latin",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | rifkat | null | rifkat/uztext-3Gb-BPE-Roberta | 50 | 1 | transformers | 6,004 |
---
language:
- uz
tags:
- transformers
- mit
- robert
- uzrobert
- uzbek
- cyrillic
- latin
license: apache-2.0
widget:
- text: "Kuchli yomg‘irlar tufayli bir qator <mask> kuchli sel oqishi kuzatildi."
example_title: "Latin script"
- text: "Алишер Навоий – улуғ ўзбек ва бошқа туркий халқларнинг <mask>, мутафаккири ... |
smallbenchnlp/bert-small | 5796c8eed06465f91a3d9fae1dc3cda4d716d69c | 2021-10-14T10:38:23.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | smallbenchnlp | null | smallbenchnlp/bert-small | 50 | null | transformers | 6,005 | Small-Bench NLP is a benchmark for small efficient neural language models trained on a single GPU.
|
taeminlee/kodialogpt2-base | d123f95f7fea6865022d6f047708635c263011f5 | 2021-05-23T13:03:30.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | taeminlee | null | taeminlee/kodialogpt2-base | 50 | null | transformers | 6,006 | Entry not found |
aihijo/gpt2-zh-21k | f570cd42dcb161e0936bcdfceab005ecbfcef217 | 2022-03-27T14:59:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"license:cc-by-nc-sa-4.0"
] | text-generation | false | aihijo | null | aihijo/gpt2-zh-21k | 50 | null | transformers | 6,007 | ---
license: cc-by-nc-sa-4.0
---
|
peterhsu/distilbert-base-uncased-finetuned-squad-d5716d28 | 7a6bc409aa7e56502ca7542baa0630d4a67f72f4 | 2022-03-30T12:22:49.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | peterhsu | null | peterhsu/distilbert-base-uncased-finetuned-squad-d5716d28 | 50 | null | transformers | 6,008 | ---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of... |
mismayil/kogito-rc-swem | 61c6156c43a0fa5cd530784f9d2e07d98a84fdfa | 2022-04-28T13:51:17.000Z | [
"pytorch",
"transformers",
"license:mit"
] | null | false | mismayil | null | mismayil/kogito-rc-swem | 50 | null | transformers | 6,009 | ---
license: mit
---
|
TweebankNLP/bertweet-tb2_ewt-pos-tagging | 1616a205d6f1953421a242105d38f58d67c82f8a | 2022-05-05T00:23:51.000Z | [
"pytorch",
"roberta",
"token-classification",
"arxiv:2201.07281",
"transformers",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | token-classification | false | TweebankNLP | null | TweebankNLP/bertweet-tb2_ewt-pos-tagging | 50 | 2 | transformers | 6,010 | ---
license: cc-by-nc-4.0
---
## Model Specification
- This is the **state-of-the-art Twitter POS tagging model (with 95.38\% Accuracy)** on Tweebank V2's NER benchmark (also called `Tweebank-NER`), trained on the corpus combining both Tweebank-NER and English-EWT training data.
- For more details about the `TweebankN... |
d0r1h/led-base-ilc | 08a1d521150602fa95f0f4abdaa6f807c22d1988 | 2022-05-06T08:17:46.000Z | [
"pytorch",
"led",
"text2text-generation",
"dataset:ilc",
"arxiv:2004.05150",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | d0r1h | null | d0r1h/led-base-ilc | 50 | null | transformers | 6,011 | ---
license: apache-2.0
datasets: ilc
tags:
- summarization
metrics:
- rouge
widget:
- text: "IN THE HIGH COURT OF JUDICATURE AT PATNA CRIMINAL MISCELLANEOUS No. 229121 Arising Out of PS. Case No. 127 Year 2020 Thana DUMRAON District Buxar 1. Ramlal Goswami aged about 44 years Male S o Late Gauri Shankar 2. Dharmshila ... |
allenai/tk-instruct-11b-def-pos | 655f68f5a6cf3ae9685688e109e714a0596fa380 | 2022-05-27T06:29:13.000Z | [
"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-pos | 50 | null | transformers | 6,012 | ---
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... |
samrawal/medical-sentence-tokenizer | 9e006a3fbed6747fcf36ff6530b8fdbe778243f3 | 2022-05-30T19:12:19.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | samrawal | null | samrawal/medical-sentence-tokenizer | 50 | null | transformers | 6,013 | ---
license: apache-2.0
---
`clinitokenizer` is a sentence tokenizer for clinical text to split unstructured text from clinical text (such as Electronic Medical Records) into individual sentences.
To use this model, see the [clinitokenizer repository](https://github.com/clinisift/clinitokenizer).
General English s... |
smc/PANDA_ConvNeXT_K | 1d5430baeef533b5c3669798c106477298502cad | 2022-05-26T21:33:39.000Z | [
"pytorch",
"convnext",
"image-classification",
"transformers",
"model-index"
] | image-classification | false | smc | null | smc/PANDA_ConvNeXT_K | 50 | 1 | transformers | 6,014 | ---
tags:
- image-classification
- pytorch
metrics:
- accuracy
- Cohen's Kappa
model-index:
- name: PANDA_ConvNeXT_K
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6058823466300964
- name: Quadrati... |
bilalahmed15/Urdu_repo | ace2bdd81b2edbe366ffac7049cef94d3fc02a69 | 2022-06-02T21:01:04.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | bilalahmed15 | null | bilalahmed15/Urdu_repo | 50 | null | transformers | 6,015 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, the... |
Rebreak/bert_news_class | 8bc8f312c13d2f8be961cbd7c9d4f7309511a758 | 2022-06-10T08:07:21.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:mit"
] | text-classification | false | Rebreak | null | Rebreak/bert_news_class | 50 | null | transformers | 6,016 | ---
license: mit
---
Classifier of news affecting the stock price in the next 10 minutes |
huggingtweets/dril-tacticalmaid | f2e2cea517d4a627226a01612fa88a21800dd135 | 2022-07-01T12:50:55.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/dril-tacticalmaid | 50 | null | transformers | 6,017 | ---
language: en
thumbnail: http://www.huggingtweets.com/dril-tacticalmaid/1656679850409/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4p... |
Danitg95/autotrain-kaggle-effective-arguments-1086739296 | 5ad53906ce06a39872b8dce0dd8d812dbf0e89e4 | 2022-07-04T21:53:10.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:Danitg95/autotrain-data-kaggle-effective-arguments",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | Danitg95 | null | Danitg95/autotrain-kaggle-effective-arguments-1086739296 | 50 | null | transformers | 6,018 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Danitg95/autotrain-data-kaggle-effective-arguments
co2_eq_emissions: 5.2497206864306065
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1086739296
- CO2 Emissions (in grams): 5.249720686430606... |
SafeTorpedo/DialoGPT-small-MichaelBot | 7d9358a9d288df252e498ba830c295bef94aef57 | 2022-07-08T11:38:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | SafeTorpedo | null | SafeTorpedo/DialoGPT-small-MichaelBot | 50 | null | transformers | 6,019 | ---
tags:
- conversational
---
#Michael from Office DialoGPT Model |
ignatius/cyT5-small | 91c22f51996710d97694120bd7ab997ac9ce0a1b | 2022-07-19T15:02:01.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | text2text-generation | false | ignatius | null | ignatius/cyT5-small | 50 | null | transformers | 6,020 | ---
license: cc-by-4.0
---
`cyT5-small` is a light-weight (alpha-version) Welsh T5 model extracted from the `google/mt5-small` model and fine-tuned only on the [Welsh summarization dataset](https://huggingface.co/datasets/ignatius/welsh_summarization)
Further developments are ongoing and will updates will be shared so... |
jonatasgrosman/exp_w2v2t_pt_vp-it_s529 | 6178739c77d95a53efacd1957580fd1d99540627 | 2022-07-11T20:21:11.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"license:apache-2.0"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/exp_w2v2t_pt_vp-it_s529 | 50 | null | transformers | 6,021 | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-it_s529
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [... |
jordyvl/biobert-base-cased-v1.2_ncbi_disease-sm-first-ner | 7dfc679be20aa2605346028f1fa68ffa7b6c1634 | 2022-07-20T09:26:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:ncbi_disease",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | jordyvl | null | jordyvl/biobert-base-cased-v1.2_ncbi_disease-sm-first-ner | 50 | null | transformers | 6,022 | ---
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2_ncbi_disease-sm-first-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_d... |
scales-okn/entity-resolution | 3b9072a2f6108ad8ff54975b5acdc6f0faea656c | 2022-07-26T17:11:07.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | scales-okn | null | scales-okn/entity-resolution | 50 | null | transformers | 6,023 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: entity-resolution
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... |
derwahnsinn/gpt2-mediumForbiddenToyStory | 99206abd7dcacf5c94694481936a03808eac48bf | 2022-07-28T13:25:44.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | derwahnsinn | null | derwahnsinn/gpt2-mediumForbiddenToyStory | 50 | null | transformers | 6,024 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-mediumForbiddenToyStory
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 comment. -->
# gpt2-mediumFo... |
RAYZ/macbert | c268a598b76662e73ad2970d42b961d4dc7a9480 | 2022-07-29T19:24:46.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | RAYZ | null | RAYZ/macbert | 50 | null | transformers | 6,025 | Entry not found |
Hate-speech-CNERG/dehatebert-mono-portugese | a212b2dd7e8e3d953787a49d92c469b30c6da6ba | 2021-09-25T13:58:01.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"pt",
"arxiv:2004.06465",
"transformers",
"license:apache-2.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/dehatebert-mono-portugese | 49 | 2 | transformers | 6,026 | ---
language: pt
license: apache-2.0
---
This model is used detecting **hatespeech** in **Portuguese language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning r... |
Helsinki-NLP/opus-mt-en-ine | 1fc70cd9577f55f77c5177f1f1769068c1f8563d | 2021-01-18T08:09:54.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"ca",
"es",
"os",
"ro",
"fy",
"cy",
"sc",
"is",
"yi",
"lb",
"an",
"sq",
"fr",
"ht",
"rm",
"ps",
"af",
"uk",
"sl",
"lt",
"bg",
"be",
"gd",
"si",
"br",
"mk",
"or",
"mr",
"ru",
"fo",
"co",
"oc",
"... | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-ine | 49 | null | transformers | 6,027 | ---
language:
- en
- ca
- es
- os
- ro
- fy
- cy
- sc
- is
- yi
- lb
- an
- sq
- fr
- ht
- rm
- ps
- af
- uk
- sl
- lt
- bg
- be
- gd
- si
- br
- mk
- or
- mr
- ru
- fo
- co
- oc
- pl
- gl
- nb
- bn
- id
- hy
- da
- gv
- nl
- pt
- hi
- as
- kw
- ga
- sv
- gu
- wa
- lv
- el
- it
- hr
- ur
- nn
- de
- cs
- ine
tags:
- ... |
KoichiYasuoka/roberta-classical-chinese-base-upos | 7fc4c2e87291baeeebf7e870bc7ba7d1079806c3 | 2022-07-05T12:02:23.000Z | [
"pytorch",
"roberta",
"token-classification",
"lzh",
"dataset:universal_dependencies",
"transformers",
"classical chinese",
"literary chinese",
"ancient chinese",
"pos",
"dependency-parsing",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/roberta-classical-chinese-base-upos | 49 | null | transformers | 6,028 | ---
language:
- "lzh"
tags:
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
widget:
- text: "子曰學而時習之不亦説乎有朋自遠方來不亦樂乎人不知而不慍不亦君子乎"
---
# roberta-classical-c... |
Lowin/chinese-bigbird-tiny-1024 | 4a9197e8d5e26185e478af472b9563f046b4670a | 2021-11-24T16:03:15.000Z | [
"pytorch",
"big_bird",
"feature-extraction",
"zh",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | Lowin | null | Lowin/chinese-bigbird-tiny-1024 | 49 | 1 | transformers | 6,029 | ---
language:
- zh
license:
- apache-2.0
---
```python
import jieba_fast
from transformers import BertTokenizer
from transformers import BigBirdModel
class JiebaTokenizer(BertTokenizer):
def __init__(
self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs
):
super().__init... |
Mary222/MADE_AI_Dungeon_model_RUS | 530856eddd16b08b32483393db9131c84b8b9f82 | 2021-11-07T16:57:43.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ru",
"transformers"
] | text-generation | false | Mary222 | null | Mary222/MADE_AI_Dungeon_model_RUS | 49 | 1 | transformers | 6,030 | ---
language: ru
tags:
- text-generation
---
# GPT2 - RUS |
Matthijsvanhof/4 | 0ea8c0832dacb2e74001b029c627bf047b7ac23e | 2021-11-27T22:42:25.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | Matthijsvanhof | null | Matthijsvanhof/4 | 49 | null | transformers | 6,031 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: '4'
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 comment. -->
# 4
Th... |
Suchandra/bengali_language_NER | 5c7283d2591c18f5bda274f5ec3da15a0cce3634 | 2022-01-16T10:49:59.000Z | [
"pytorch",
"bert",
"token-classification",
"bn",
"dataset:wikiann",
"transformers",
"autotrain_compatible"
] | token-classification | false | Suchandra | null | Suchandra/bengali_language_NER | 49 | null | transformers | 6,032 | ---
language: bn
datasets:
- wikiann
examples:
widget:
- text: "মারভিন দি মারসিয়ান"
example_title: "Sentence_1"
- text: "লিওনার্দো দা ভিঞ্চি"
example_title: "Sentence_2"
- text: "বসনিয়া ও হার্জেগোভিনা"
example_title: "Sentence_3"
- text: "সাউথ ইস্ট ইউনিভার্সিটি"
example_title: "Sentence_4"
- text: "মানিক বন্দ... |
Tanhim/gpt2-model-de | 51774239e28accd2eb385a08ad10aeac5154bf2d | 2022-04-22T23:24:24.000Z | [
"pytorch",
"gpt2",
"text-generation",
"de",
"transformers",
"license:gpl"
] | text-generation | false | Tanhim | null | Tanhim/gpt2-model-de | 49 | 1 | transformers | 6,033 | ---
language: de
widget:
- text: Hallo, ich bin ein Sprachmodell
license: gpl
---
<h2> GPT2 Model for German Language </h2>
Model Name: Tanhim/gpt2-model-de <br />
language: German or Deutsch <br />
thumbnail: https://huggingface.co/Tanhim/gpt2-model-de <br />
datasets: Ten Thousand German News Articles Dataset <b... |
TransQuest/monotransquest-da-en_de-wiki | cf4f548d2bba477c8c1305889d1c2013ad706835 | 2021-06-03T19:03:21.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"en-de",
"transformers",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0"
] | text-classification | false | TransQuest | null | TransQuest/monotransquest-da-en_de-wiki | 49 | null | transformers | 6,034 | ---
language: en-de
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... |
activebus/BERT-DK_laptop | 10b76b26386d0aa76f0526c2d8ad3c4e9b6283cf | 2021-05-18T23:00:58.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | activebus | null | activebus/BERT-DK_laptop | 49 | null | transformers | 6,035 | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`.
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipedi... |
alaggung/bart-pretrained | e32cd9c352e95988fdbb07b3e6e0f4103b971e8e | 2022-01-11T16:07:39.000Z | [
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | alaggung | null | alaggung/bart-pretrained | 49 | null | transformers | 6,036 | ---
language:
- ko
widget:
- text: "[BOS]뭐 해?[SEP][MASK]하다가 이제 [MASK]려고[EOS]"
inference:
parameters:
max_length: 64
---
# BART Pretrained
[2021 훈민정음 한국어 음성•자연어 인공지능 경진대회] 대화요약 부문 알라꿍달라꿍 팀의 대화요약 학습 샘플 모델을 공유합니다.
[2021-dialogue-summary-competition](https://github.com/cosmoquester/2021-dialogue-summary-competit... |
alistvt/bert-base-uncased-pretrain-finetuned-coqa-falttened | b2600736ab18d6e9ef26e94b20373d676a3e908b | 2022-01-22T05:06:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | alistvt | null | alistvt/bert-base-uncased-pretrain-finetuned-coqa-falttened | 49 | null | transformers | 6,037 | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-pretrain-finetuned-coqa-falttened
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 comment. -->
# ber... |
cambridgeltl/mirrorwic-bert-base-uncased | 906953412ec1603951324c28e6c8447bac134e0a | 2021-10-25T19:18:46.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | cambridgeltl | null | cambridgeltl/mirrorwic-bert-base-uncased | 49 | null | transformers | 6,038 | Entry not found |
diper1998/distilgpt2-finetuned-AT | 23b7c3c15aee13f4ec77bc3bcb0db2d68ff9e280 | 2021-12-16T16:14:38.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | diper1998 | null | diper1998/distilgpt2-finetuned-AT | 49 | null | transformers | 6,039 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-AT
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 comment. -->
# distilgpt2-... |
frgfm/repvgg_a0 | ab1c8cc7cd9dcc49c60352791c799fbda90cf2e8 | 2022-07-20T00:55:54.000Z | [
"pytorch",
"onnx",
"dataset:frgfm/imagenette",
"arxiv:2101.03697",
"transformers",
"image-classification",
"license:apache-2.0"
] | image-classification | false | frgfm | null | frgfm/repvgg_a0 | 49 | null | transformers | 6,040 | ---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- frgfm/imagenette
---
# RepVGG-A0 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The RepVGG architecture was introduced in [this paper](https://arxiv.org/pdf/2101.03697.pdf).
## Model description
The core ide... |
hakurei/gpt-j-random-tinier | 8497f4876f4d6c40182a8038f023b73d75e292f3 | 2021-09-24T06:21:52.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers"
] | text-generation | false | hakurei | null | hakurei/gpt-j-random-tinier | 49 | 1 | transformers | 6,041 | This model has been initialized with random values. It is supposed to be used for the purpose of debugging. |
healx/gpt-2-pubmed-large | f86589bacd2e80eba8a79f7e5c74ded0ddb6fb2b | 2020-12-11T21:43:38.000Z | [
"pytorch",
"arxiv:2004.13845",
"transformers"
] | null | false | healx | null | healx/gpt-2-pubmed-large | 49 | null | transformers | 6,042 | GPT-2 (774M model) finetuned on 0.5m PubMed abstracts. Used in the [writemeanabstract.com](writemeanabstract.com) and the following preprint:
[Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).](https://arxiv.org/abs/2004.13845)
|
huggingtweets/google | 9c5a47405b9ff1fa60ebd0b14c8a83c50b89f28f | 2021-05-22T05:54:43.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/google | 49 | null | transformers | 6,043 | ---
language: en
thumbnail: https://www.huggingtweets.com/google/1609714473367/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: ... |
hyunwoongko/jaberta-base-ja-xnli | dd8b90fddaa3ca6a752604bf9140f984e75818f9 | 2021-05-20T16:43:51.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | hyunwoongko | null | hyunwoongko/jaberta-base-ja-xnli | 49 | null | transformers | 6,044 | Entry not found |
jcblaise/roberta-tagalog-small | a1a4cd4430779ef00549499d8d3e94c9d65b27e6 | 2021-05-20T17:12:24.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | jcblaise | null | jcblaise/roberta-tagalog-small | 49 | null | transformers | 6,045 | Entry not found |
jonfd/electra-small-igc-is | 5d185d0df9eff92cd66dd7f8992f581e543b6450 | 2022-01-05T14:56:02.000Z | [
"pytorch",
"electra",
"pretraining",
"is",
"dataset:igc",
"transformers",
"license:cc-by-4.0"
] | null | false | jonfd | null | jonfd/electra-small-igc-is | 49 | null | transformers | 6,046 | ---
language:
- is
license: cc-by-4.0
datasets:
- igc
---
# Icelandic ELECTRA-Small
This model was pretrained on the [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/), which contains approximately 1.69B tokens, using default settings. The model uses a WordPiece tokenizer with a vocabulary size of 32,105.
# Ackn... |
jxuhf/roberta-base-finetuned-cola | d617c9a8e20fa313eaf3d9dabf38ae732044feca | 2021-07-23T22:08:00.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:mit"
] | text-classification | false | jxuhf | null | jxuhf/roberta-base-finetuned-cola | 49 | null | transformers | 6,047 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: roberta-base-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metric:
name: M... |
m3hrdadfi/albert-fa-base-v2-ner-arman | 37ab49d817bea304fed5d59be2da3142b7ff5cb0 | 2020-12-26T08:36:57.000Z | [
"pytorch",
"tf",
"albert",
"token-classification",
"fa",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | m3hrdadfi | null | m3hrdadfi/albert-fa-base-v2-ner-arman | 49 | 3 | transformers | 6,048 | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
mmm-da/anekdot_funny2_rugpt3Small | e3cd30702e5a0929050128d88036aad3c6524982 | 2021-05-23T09:51:06.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | mmm-da | null | mmm-da/anekdot_funny2_rugpt3Small | 49 | null | transformers | 6,049 | Entry not found |
mrm8488/roberta-large-finetuned-wsc | ae61d2ab2fde7b732b37bd4760ce4bb1b3c2e36f | 2021-05-20T18:30:59.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1905.06290",
"transformers",
"autotrain_compatible"
] | fill-mask | false | mrm8488 | null | mrm8488/roberta-large-finetuned-wsc | 49 | null | transformers | 6,050 | # RoBERTa (large) fine-tuned on Winograd Schema Challenge (WSC) data
Step from its original [repo](https://github.com/pytorch/fairseq/blob/master/examples/roberta/wsc/README.md)
The following instructions can be used to finetune RoBERTa on the WSC training
data provided by [SuperGLUE](https://super.gluebenchmark.com/... |
nateraw/codecarbon-text-classification | 8f32d9ecc161f64c142a81435f712549b927acf6 | 2022-02-07T20:30:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | nateraw | null | nateraw/codecarbon-text-classification | 49 | null | transformers | 6,051 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: codecarbon-text-classification
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 com... |
pucpr/biobertpt-clin | ae80140591239b057e935734ae78b74f57e5f71c | 2021-10-13T09:28:07.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"pt",
"dataset:biomedical literature from Scielo and Pubmed",
"transformers",
"autotrain_compatible"
] | fill-mask | false | pucpr | null | pucpr/biobertpt-clin | 49 | 5 | transformers | 6,052 | ---
language: "pt"
widget:
- text: "O paciente recebeu [MASK] do hospital."
- text: "O médico receitou a medicação para controlar a [MASK]."
datasets:
- biomedical literature from Scielo and Pubmed
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"
---
<img src="h... |
striki-ai/william-shakespeare-poetry | 46a3d4b3fa010e4d449ea42aaeb290281fbebc03 | 2021-06-05T20:25:15.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | striki-ai | null | striki-ai/william-shakespeare-poetry | 49 | null | transformers | 6,053 | Entry not found |
textattack/xlnet-base-cased-QQP | 9529809aceb015404efebb2efa12fe831c9f8636 | 2020-06-09T16:56:26.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/xlnet-base-cased-QQP | 49 | null | transformers | 6,054 | Entry not found |
unicamp-dl/mMiniLM-L6-v2-mmarco-v1 | 8d896aa5cb826f1a56eda9fd3d1573799cb75aed | 2022-01-05T21:29:46.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"pt",
"dataset:msmarco",
"arxiv:2108.13897",
"transformers",
"msmarco",
"miniLM",
"tensorflow",
"pt-br",
"license:mit"
] | text-classification | false | unicamp-dl | null | unicamp-dl/mMiniLM-L6-v2-mmarco-v1 | 49 | 1 | transformers | 6,055 | ---
language: pt
license: mit
tags:
- msmarco
- miniLM
- pytorch
- tensorflow
- pt
- pt-br
datasets:
- msmarco
widget:
- text: "Texto de exemplo em português"
inference: false
---
# mMiniLM-L6-v2 Reranker finetuned on mMARCO
## Introduction
mMiniLM-L6-v2-mmarco-v1 is a multilingual miniLM-based model finetuned on a mul... |
malteos/aspect-scibert-task | 372552f649487d03b7512ecd05badf1f8ce84d13 | 2022-03-16T12:47:47.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | malteos | null | malteos/aspect-scibert-task | 49 | 1 | transformers | 6,056 | ---
license: mit
---
|
czw/gpt2-base-chinese-finetuned-job-resume | 5b0cbff021614ce29fe375e6b40fa2a2d2e15f79 | 2022-05-04T03:38:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:gpl-3.0",
"model-index"
] | text-generation | false | czw | null | czw/gpt2-base-chinese-finetuned-job-resume | 49 | null | transformers | 6,057 | ---
license: gpl-3.0
tags:
- generated_from_trainer
model-index:
- name: gpt2-base-chinese-finetuned-job-resume
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 comment. -->
#... |
tomhosking/bert-base-uncased-debiased-nli | 7214ca65939d0108455359cd5121584cbdfb1fb3 | 2022-05-06T15:28:40.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | tomhosking | null | tomhosking/bert-base-uncased-debiased-nli | 49 | null | transformers | 6,058 | ---
license: apache-2.0
widget:
- text: "[CLS] Rover is a dog. [SEP] Rover is a cat. [SEP]"
---
`bert-base-uncased`, fine tuned on the debiased NLI dataset from "Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets", Wu et al., 2022.
Tuned using the code at https://github.com/jimmy... |
kabelomalapane/nso_en_ukuxhumana_model | fc3c3d1e76040c91af9da3fbde9b33a978932524 | 2022-05-21T01:15:15.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | translation | false | kabelomalapane | null | kabelomalapane/nso_en_ukuxhumana_model | 49 | null | transformers | 6,059 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: nso_en_ukuxhumana_model
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 th... |
sumedh/distilbart-cnn-12-6-amazonreviews | ec3da5a07519f708c3cba007911e09d321c57aeb | 2022-05-22T17:47:06.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:amazon_reviews_multi",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | sumedh | null | sumedh/distilbart-cnn-12-6-amazonreviews | 49 | null | transformers | 6,060 | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- amazon_reviews_multi
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... |
memyprokotow/rut5-REBEL-base | 6036825ca01ef3409b972c101d77a262f367ed7c | 2022-06-07T17:37:00.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"dataset:memyprokotow/rebel-dataset-rus",
"transformers",
"seq2seq",
"relation-extraction",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | memyprokotow | null | memyprokotow/rut5-REBEL-base | 49 | null | transformers | 6,061 | ---
language:
- ru
tags:
- seq2seq
- relation-extraction
- t5
license: apache-2.0
datasets:
- memyprokotow/rebel-dataset-rus
widget:
- text: "За последние 9 месяцев инвесторы в азиатские долларовые долговые обязательства потеряли 155 миллиардов долларов, пострадав от слабости Китая в дополнение к глобальной распродаже ... |
pranavk/bart-paraphrase-finetuned-xsum-v3 | e73d747d56a215cbaa3b069b6c66f1678bc9aa7c | 2022-06-07T21:01:46.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | pranavk | null | pranavk/bart-paraphrase-finetuned-xsum-v3 | 49 | null | transformers | 6,062 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-paraphrase-finetuned-xsum-v3
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 ... |
Anery/legalbert_clause_combined | 14b1fe3357c5d98f72549b58df084fe800e44ed0 | 2022-06-09T16:53:41.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Anery | null | Anery/legalbert_clause_combined | 49 | null | transformers | 6,063 | Entry not found |
anahitapld/DABert | 0c5d25199d20a36cfd452b16ecc4940593a6017e | 2022-06-28T08:17:13.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | anahitapld | null | anahitapld/DABert | 49 | null | transformers | 6,064 | ---
license: apache-2.0
---
|
ukr-models/uk-punctcase | 76132ef389bb0f782449dc23b5aae2ddbc07c7db | 2022-07-13T12:29:54.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"uk",
"transformers",
"ukrainian",
"license:mit",
"autotrain_compatible"
] | token-classification | false | ukr-models | null | ukr-models/uk-punctcase | 49 | 2 | transformers | 6,065 | ---
language:
- uk
tags:
- ukrainian
widget:
- text: "упродовж 2012-2014 років національний природний парк «зачарований край» разом із всесвітнім фондом природи wwf успішно реалізували проект із відновлення болота «чорне багно» розташованого на схилах гори бужора у закарпатті водноболотне угіддя «чорне багно» є найбіл... |
valurank/headline_generator | 506ab61e446f096c4fa5ca33dc1d5f26c6b38e6d | 2022-07-20T12:28:06.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | valurank | null | valurank/headline_generator | 49 | null | transformers | 6,066 | ---
tags:
- generated_from_trainer
model-index:
- name: multi_news_headline_generator
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 comment. -->
# multi_news_headline_gener... |
hassan4830/xlm-roberta-base-finetuned-urdu | 6f55304f1cb7baa2d7db054862d21aaa2ceffa37 | 2022-07-25T07:09:45.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"ur",
"transformers",
"license:afl-3.0"
] | text-classification | false | hassan4830 | null | hassan4830/xlm-roberta-base-finetuned-urdu | 49 | 1 | transformers | 6,067 | ---
language: ur
license: afl-3.0
---
# XLM-RoBERTa-Urdu-Classification
This [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) text classification model trained on Urdu sentiment [data-set](https://huggingface.co/datasets/hassan4830/urdu-binary-classification-data) performs binary sentiment classification ... |
nthakur/mMiniLMv2-L12-H384-ms-marco-all-epoch-40 | ae98e13213920bd16117311cb2833f6b8e87d872 | 2022-07-26T12:49:46.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | nthakur | null | nthakur/mMiniLMv2-L12-H384-ms-marco-all-epoch-40 | 49 | null | transformers | 6,068 | Entry not found |
JAlexis/ajusteBert004 | e18d7d5c89d2e85f8c4e3d85a819047a53310e5f | 2022-07-26T17:41:51.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | JAlexis | null | JAlexis/ajusteBert004 | 49 | null | transformers | 6,069 | Entry not found |
KoichiYasuoka/chinese-roberta-large-upos | 43751ea82d5908ead06d3ed67babea4cece682c0 | 2022-02-11T06:30:18.000Z | [
"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-large-upos | 48 | null | transformers | 6,070 | ---
language:
- "zh"
tags:
- "chinese"
- "token-classification"
- "pos"
- "wikipedia"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
---
# chinese-roberta-large-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia text... |
LeoFeng/ChineseSequenceClassification | 872a56197737bb565feb3f882434a4974eb49310 | 2022-01-02T09:13:10.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | LeoFeng | null | LeoFeng/ChineseSequenceClassification | 48 | 1 | transformers | 6,071 | 利用THUC dataset 訓練的文章分類器，共支援14種種類 |
Media1129/keyword-tag-model-9000-v2 | 7cd4018260eedf9fb8d29056770c7483fe04420b | 2021-08-30T06:04:11.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Media1129 | null | Media1129/keyword-tag-model-9000-v2 | 48 | null | transformers | 6,072 | Entry not found |
Narrativa/t5-base-finetuned-totto-table-to-text | a281b1998bc6c9529b306e5950f860a62f7de42d | 2021-08-07T08:55:25.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Narrativa | null | Narrativa/t5-base-finetuned-totto-table-to-text | 48 | 2 | transformers | 6,073 | Entry not found |
cola/chinese-address-ner | 28da85cbc4d51e3234280899742df96ce65efeb1 | 2021-07-20T08:59:34.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | cola | null | cola/chinese-address-ner | 48 | 1 | transformers | 6,074 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: chinese-address-ner
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.975825946817083
---
<... |
espejelomar/beto-base-cased | 4da5782593ca46e0fa5276432c681d11e969d83c | 2021-12-07T22:24:15.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | espejelomar | null | espejelomar/beto-base-cased | 48 | null | transformers | 6,075 | Entry not found |
funnel-transformer/xlarge-base | 5efaa39740d551ebf123c67230e739420439e765 | 2020-12-11T21:40:48.000Z | [
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | funnel-transformer | null | funnel-transformer/xlarge-base | 48 | null | transformers | 6,076 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
- gigaword
---
# Funnel Transformer xlarge model (B10-10-10 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[... |
gagan3012/Fox-News-Generator | 24399eb68fd6edf0dbfdd3ba831c0286e22825d5 | 2021-05-21T16:03:28.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | gagan3012 | null | gagan3012/Fox-News-Generator | 48 | 2 | transformers | 6,077 | # Generating Right Wing News Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data from FOX news
### My model can be accessed at gagan3012/Fox-News-Generator
Check the [BenchmarkTest](https://github.com/gagan3012/Fox-News-Generator/blob/master/BenchmarkT... |
hfl/chinese-electra-large-discriminator | d5b57a2a772c1f47f37274dbea0cc736c2ef9d9b | 2021-03-03T01:42:48.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | null | false | hfl | null | hfl/chinese-electra-large-discriminator | 48 | null | transformers | 6,078 | ---
language:
- zh
license: "apache-2.0"
---
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size ... |
huggingtweets/flatironschool | 363501627e18df71b77be0329f38ad61c427be53 | 2021-05-22T04:20:52.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/flatironschool | 48 | null | transformers | 6,079 | ---
language: en
thumbnail: https://www.huggingtweets.com/flatironschool/1603341000640/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose {... |
it5/it5-large-question-answering | 1a9102e557a469141fa9fee356b99e76553564de | 2022-03-09T07:57:53.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"it",
"dataset:squad_it",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"squad_it",
"text2text-question-answering",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_comp... | text2text-generation | false | it5 | null | it5/it5-large-question-answering | 48 | 2 | transformers | 6,080 | ---
language:
- it
license: apache-2.0
datasets:
- squad_it
tags:
- italian
- sequence-to-sequence
- squad_it
- text2text-question-answering
- text2text-generation
widget:
- text: "In seguito all' evento di estinzione del Cretaceo-Paleogene, l' estinzione dei dinosauri e il clima umido possono aver permesso alla forest... |
it5/mt5-base-question-generation | 9098f5e3f5b44e7b8504da97575291509f36e333 | 2022-03-09T07:54:16.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"it",
"dataset:squad_it",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"question-generation",
"squad_it",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
... | text2text-generation | false | it5 | null | it5/mt5-base-question-generation | 48 | null | transformers | 6,081 | ---
language:
- it
license: apache-2.0
datasets:
- squad_it
tags:
- italian
- sequence-to-sequence
- question-generation
- squad_it
- text2text-generation
widget:
- text: "Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un ... |
jaesun/distilbert-base-uncased-finetuned-cola | 8d60f5fdf0d336a8f5d3cf8cb1d705ac4c76c16f | 2021-10-20T17:47:49.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | jaesun | null | jaesun/distilbert-base-uncased-finetuned-cola | 48 | null | transformers | 6,082 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... |
lighteternal/gpt2-finetuned-greek | 3a0d959c9494f904c4c0b8e0ab39e0a5dac2c66b | 2021-05-23T08:33:11.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"el",
"transformers",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | lighteternal | null | lighteternal/gpt2-finetuned-greek | 48 | null | transformers | 6,083 |
---
language:
- el
tags:
- pytorch
- causal-lm
widget:
- text: "Το αγαπημένο μου μέρος είναι"
license: apache-2.0
---
# Greek (el) GPT2 model
<img src="https://huggingface.co/lighteternal/gpt2-finetuned-greek-small/raw/main/GPT2el.png" width="600"/>
### By the Hellenic Army Academy (SSE) and the Technical Univers... |
mrm8488/deberta-v3-small-finetuned-cola | eed29ebc1fcbc35eea945e50510c93f1bb4895d4 | 2021-12-07T17:18:59.000Z | [
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"en",
"dataset:glue",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | mrm8488 | null | mrm8488/deberta-v3-small-finetuned-cola | 48 | 2 | transformers | 6,084 | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
widget:
- text: "They represented seriously to the dean Mary as a genuine linguist."
metrics:
- matthews_correlation
model-index:
- name: deberta-v3-small
results:
- task:
name: Text Classification
type: text-classification
... |
nielsr/convnext-xlarge-224-22k | c75e01f6e883d3fe653d452a3f9cb18e0a8e5ef1 | 2022-02-22T11:11:07.000Z | [
"pytorch",
"convnext",
"image-classification",
"transformers"
] | image-classification | false | nielsr | null | nielsr/convnext-xlarge-224-22k | 48 | null | transformers | 6,085 | Entry not found |
prajjwal1/roberta-large-mnli | 2b3512bd70fcc9a896cb85d36a3c21c443f2ae8f | 2021-10-05T18:03:08.000Z | [
"pytorch",
"roberta",
"text-classification",
"arxiv:2110.01518",
"transformers"
] | text-classification | false | prajjwal1 | null | prajjwal1/roberta-large-mnli | 48 | null | transformers | 6,086 | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
... |
seongju/klue-mrc-koelectra-base | 92ce9600694f0837cdd9ebe02d261c0deaedab09 | 2021-08-19T13:05:26.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | seongju | null | seongju/klue-mrc-koelectra-base | 48 | null | transformers | 6,087 | Entry not found |
yhavinga/gpt2-large-dutch | 3c46b2c7dd9cdaf0be01df572bc0134888f4517b | 2022-03-20T10:21:46.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"transformers",
"gpt2-large"
] | text-generation | false | yhavinga | null | yhavinga/gpt2-large-dutch | 48 | 1 | transformers | 6,088 | ---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt2-large
- gpt2
pipeline_tag: text-generation
datasets:
- yhav... |
0x7194633/pyGPT-50M | 602850406ef8a27044c92e30e5b2b9202226b3fa | 2022-07-01T03:29:17.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"code",
"transformers",
"license:mpl-2.0"
] | text-generation | false | 0x7194633 | null | 0x7194633/pyGPT-50M | 48 | 1 | transformers | 6,089 | ---
license: mpl-2.0
language:
- en
- code
---
## PythonGPT
A GPT2-type neural network trained on 16 gigabytes of Pyhon scripts from scratch. It has 50 million parameters.
Made as a toy. |
ukr-models/uk-ner | 3c903f55014e7c10376c6c22e81d96e0cee0a1e4 | 2022-04-07T05:54:54.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"uk",
"transformers",
"ukrainian",
"license:mit",
"autotrain_compatible"
] | token-classification | false | ukr-models | null | ukr-models/uk-ner | 48 | 1 | transformers | 6,090 | ---
language:
- uk
tags:
- ukrainian
widget:
- text: "Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера."
license: mit
---
## Model Description
Fin... |
ml6team/keyphrase-generation-t5-small-openkp | 7af3cadf6123a9384aff7f35200d575f3c59ede0 | 2022-06-16T18:02:54.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:midas/openkp",
"arxiv:1911.02671",
"transformers",
"keyphrase-generation",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | ml6team | null | ml6team/keyphrase-generation-t5-small-openkp | 48 | null | transformers | 6,091 | ---
language: en
license: mit
tags:
- keyphrase-generation
datasets:
- midas/openkp
widget:
- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document.
Thanks to these keyphrases humans can understand the content of a text very quickly and easily without re... |
CEBaB/bert-base-uncased.CEBaB.sa.5-class.exclusive.seed_66 | f5301ba50903bc3b92ca71585189c27b53bb30f0 | 2022-05-11T00:48:16.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB.sa.5-class.exclusive.seed_66 | 48 | null | transformers | 6,092 | Entry not found |
danielhou13/longformer-finetuned-news-cogs402 | 46dc639baa796e9ad30abbfb37135e125582aad6 | 2022-05-31T03:01:23.000Z | [
"pytorch",
"longformer",
"text-classification",
"transformers"
] | text-classification | false | danielhou13 | null | danielhou13/longformer-finetuned-news-cogs402 | 48 | null | transformers | 6,093 | Entry not found |
ibm/roberta-large-vira-intents | f834137cdf775f25d3aa412b97ce579f63c36ffa | 2022-06-01T12:06:27.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:ibm/vira-intents",
"arxiv:2205.11966",
"transformers",
"intent detection",
"license:other"
] | text-classification | false | ibm | null | ibm/roberta-large-vira-intents | 48 | null | transformers | 6,094 | ---
language:
- en
tags:
- intent detection
license: "other"
datasets:
- ibm/vira-intents
metrics:
- accuracy
widget:
- text: "Should I be concerned about side effects of the vaccine if I'm breastfeeding?} & Is breastfeeding safe with the vaccine"
example_title: "Breastfeeding"
- text: "Does the vaccine prevent trans... |
KES/caribe-capitalise | 7892f7a15512f997e26519d4f487abf2bd94f5be | 2022-06-10T22:27:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"sentence capitalization",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | KES | null | KES/caribe-capitalise | 48 | 1 | transformers | 6,095 | ---
license: mit
language: en
tags:
- sentence capitalization
- text2text-generation
---
This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset This model was fine-tuned for capitalisation on text that includes multiple sentences or questions.
Interested in Caribbean Creole? Ch... |
Tomas23/twitter-roberta-base-mar2022-finetuned-sentiment | cdf42e26f4fc6fcc1e3e8b0ec10402b9f84a7172 | 2022-06-13T14:23:23.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | Tomas23 | null | Tomas23/twitter-roberta-base-mar2022-finetuned-sentiment | 48 | null | transformers | 6,096 | Entry not found |
emilys/BERTweet-CoNLL | eca14ffff820e794384f4408a8fa6267aa2e1534 | 2022-06-15T21:19:05.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"dataset:conll2003",
"transformers",
"NER",
"autotrain_compatible"
] | token-classification | false | emilys | null | emilys/BERTweet-CoNLL | 48 | null | transformers | 6,097 | ---
language:
- en
tags:
- NER
datasets:
- conll2003
---
bertweet-base (https://huggingface.co/vinai/bertweet-base) finetuned on CoNLL (2003) English, following https://github.com/huggingface/transformers/tree/main/examples/legacy/token-classification |
Anupama/distilbert-base-uncased-finetuned-emotion | 1430ab1624afbd5e88ff6561f793c72a0693f466 | 2022-07-03T13:53:44.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Anupama | null | Anupama/distilbert-base-uncased-finetuned-emotion | 48 | null | transformers | 6,098 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... |
postpandas/distilbert-base-uncased-finetuned-emotion | 48f718383a5dcdd42161648d6f2052b693d712da | 2022-07-14T14:46:49.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | postpandas | null | postpandas/distilbert-base-uncased-finetuned-emotion | 48 | null | transformers | 6,099 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... |
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