modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
l3cube-pune/MarathiSentiment | [
"Negative",
"Neutral",
"Positive"
] | ---
language: mr
tags:
- albert
license: cc-by-4.0
datasets:
- L3CubeMahaSent
widget:
- text: "I like you. </s></s> I love you."
---
## MarathiSentiment
MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset.
[dataset link] (https:... | 888 |
erst/xlm-roberta-base-finetuned-nace | [
"0111",
"0112",
"0113",
"0114",
"0115",
"0116",
"0119",
"0121",
"0122",
"0123",
"0124",
"0125",
"0126",
"0127",
"0128",
"0129",
"0130",
"0141",
"0142",
"0143",
"0144",
"0145",
"0146",
"0147",
"0149",
"0150",
"0161",
"0162",
"0163",
"0164",
"0170",
"0210"... | # Classifying Text into NACE Codes
This model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) fine-tuned to classify descriptions of activities into [NACE Rev. 2](https://ec.europa.eu/eurostat/web/nace-rev2) codes.
## Data
The data used to fine-tune the model consist of 2.5 million descriptions of act... | 1,049 |
khalidalt/DeBERTa-v3-large-mnli | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
widget:
- text: "The Movie have been criticized for the story. However, I think it is a great movie. [SEP] I liked the movie."
---
# DeBERTa-v3-large-mnli
## Model description
This model was trained on the Multi-... | 2,132 |
transformersbook/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
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... | 2,137 |
sismetanin/xlm_roberta_base-ru-sentiment-rusentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## XML-RoBERTa-Base-ru-sentiment-RuSentiment
XML-RoBERTa-Base-ru-sentiment-RuSentiment is a [XML-RoBERTa-Base](https://huggingface.co/xlm-roberta-base) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-dom... | 6,346 |
tals/albert-base-vitaminc-fever | [
"NOT ENOUGH INFO",
"REFUTES",
"SUPPORTS"
] | ---
language: python
datasets:
- fever
- glue
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this m... | 2,357 |
nanopass/distilbert-base-uncased-emotion-2 | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
---
# Distilbert-base-uncased-emotion
## Model description:
[Distil... | 2,898 |
DTAI-KULeuven/robbertje-merged-dutch-sentiment | [
"Positive",
"Negative"
] | ---
language: nl
license: mit
datasets:
- dbrd
model-index:
- name: robbertje-merged-dutch-sentiment
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: dbrd
type: sentiment-analysis
split: test
metrics:
- name: Accuracy
type: accuracy
... | 4,457 |
beomi/beep-KcELECTRA-base-hate | [
"hate",
"none",
"offensive"
] | Entry not found | 15 |
sileod/roberta-base-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- multi_nli
metrics:
- accuracy
model-index:
- name: roberta-base-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: multi_nli
type: multi_nli
args: default
metrics:
- name: Accu... | 1,733 |
ICFNext/EYY-categorisation-1.0 | [
"arts and culture",
"climate change",
"democratic values",
"digital",
"education",
"employment",
"environmental sustainability",
"european learning mobility",
"health and well-being",
"inclusion",
"n/a",
"policy dialogues",
"renewable energy",
"research and innovation",
"sports",
"stud... | Entry not found | 15 |
avichr/hebEMO_anticipation | null | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | 5,444 |
textattack/bert-base-uncased-WNLI | null | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a cla... | 622 |
tr3cks/2LabelsSentimentAnalysisSpanish | null | Entry not found | 15 |
IMSyPP/hate_speech_it | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
widget:
- text: "Ciao, mi chiamo Marcantonio, sono di Roma. Studio informatica all'Università di Roma."
language:
- it
license: mit
---
# Hate Speech Classifier for Social Media Content in Italian Language
A monolingual model for hate speech classification of social media content in Italian language. The model... | 797 |
textattack/roberta-base-rotten-tomatoes | null | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of ... | 669 |
nbroad/bigbird-base-health-fact | [
"false",
"mixture",
"true",
"unproven"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- health_fact
model-index:
- name: bigbird-base-health-fact
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: health_fact
type: health_fact
split: test
metrics:
... | 12,627 |
Gadmz/censor-testing-performance | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | Entry not found | 15 |
sismetanin/xlm_roberta_large-ru-sentiment-rusentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## XML-RoBERTa-Large-ru-sentiment-RuSentiment
XML-RoBERTa-Large-ru-sentiment-RuSentiment is a [XML-RoBERTa-Large](https://huggingface.co/xlm-roberta-large) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general... | 6,350 |
deepset/bert-base-german-cased-sentiment-Germeval17 | [
"negative",
"neutral",
"positive"
] | Entry not found | 15 |
textattack/albert-base-v2-SST-2 | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 64.
Since this was a classif... | 619 |
IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_100",
"LABEL_101",
"LABEL_102",
"LABEL_103",
"LABEL_104",
"LABEL_105",
"LABEL_106",
"LABEL_107",
"LABEL_108",
"LABEL_109",
"LABEL_11",
"LABEL_110",
"LABEL_111",
"LABEL_112",
"LABEL_113",
"LABEL_114",
"LABEL_115",
"LABEL_116",
"LABEL_... | ---
license: apache-2.0
# inference: false
# pipeline_tag: zero-shot-image-classification
pipeline_tag: feature-extraction
# inference:
# parameters:
tags:
- clip
- zh
- image-text
- feature-extraction
---
# Model Details
This model is a Chinese CLIP model trained on [Noah-Wukong Dataset](https://wukong-dataset.gi... | 3,484 |
salesken/paraphrase_diversity_ranker | null | ---
tags: salesken
license: apache-2.0
inference: false
---
We have trained a model to evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation. Data augmentation by adding Surface level variations does not add much value to the NLP model training. if the approach to parap... | 4,896 |
MMG/xlm-roberta-base-sa-spanish | [
"Negative",
"Neutral",
"Positive"
] | Entry not found | 15 |
Mithil/86RecallRoberta | null | ---
license: afl-3.0
---
| 25 |
avichr/hebEMO_fear | null | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | 5,444 |
SkolkovoInstitute/roberta_toxicity_classifier_v1 | null | This model is a clone of [SkolkovoInstitute/roberta_toxicity_classifier](https://huggingface.co/SkolkovoInstitute/roberta_toxicity_classifier) trained on a disjoint dataset.
While `roberta_toxicity_classifier` is used for evaluation of detoxification algorithms, `roberta_toxicity_classifier_v1` can be used within the... | 446 |
elozano/bert-base-cased-fake-news | [
"Fake",
"Real"
] | Entry not found | 15 |
emrecan/distilbert-base-turkish-cased-allnli_tr | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidat... | 7,089 |
Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer | [
"answer",
"question"
] | ---
tags:
- generated_from_trainer
model-index:
- name: DistilBert_Fine_tune_QuestionVsAnswer
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. -->
# DistilBert_Fine_t... | 943 |
qanastek/XLMRoberta-Alexa-Intents-Classification | [
"audio_volume_other",
"play_music",
"iot_hue_lighton",
"general_greet",
"calendar_set",
"audio_volume_down",
"social_query",
"audio_volume_mute",
"iot_wemo_on",
"iot_hue_lightup",
"audio_volume_up",
"iot_coffee",
"takeaway_query",
"qa_maths",
"play_game",
"cooking_query",
"iot_hue_li... | ---
tags:
- Transformers
- text-classification
- intent-classification
- multi-class-classification
- natural-language-understanding
languages:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
... | 7,985 |
MoritzLaurer/MiniLM-L6-mnli-fever-docnli-ling-2c | [
"entailment",
"not_entailment"
] | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
widget:
- text: "I first thought that I liked the movie, but upon second thought the movie was actually disappointing. [SEP] The movie was good."
---
# MiniLM-L6-mnli-fever-docnli-ling-2c
## Model description
This model was ... | 3,986 |
TransQuest/monotransquest-hter-en_zh-wiki | [
"LABEL_0"
] | ---
language: en-zh
tags:
- Quality Estimation
- monotransquest
- hter
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... | 5,426 |
amanbawa96/legal-bert-based-uncase | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | Entry not found | 15 |
has-abi/distilBERT-finetuned-resumes-sections | [
"awards",
"certificates",
"contact/name/title",
"education",
"interests",
"languages",
"para",
"professional_experiences",
"projects",
"skills",
"soft_skills",
"summary"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: distilBERT-finetuned-resumes-sections
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 ... | 3,207 |
avichr/hebEMO_anger | null | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | 5,444 |
avichr/hebEMO_disgust | null | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | 5,444 |
tupleblog/salim-classifier | null | ---
widget:
- text: "รัฐรับผิดชอบทุกชีวิตไม่ได้หรอกคนให้บริการต้องจัดการเองถ้าจะเปิดผับบาร์"
---
# Salim-Classifier
**วัตถุประสงค์:** ทุกวันนี้หาเพื่อนที่รักชาติ ศาสนา พระมหากษัตริย์ รัฐบาลยากเหลือเกิน มีแต่พว... | 2,347 |
ydshieh/tiny-random-gptj-for-sequence-classification | null | Entry not found | 15 |
MKaan/multilingual-cpv-sector-classifier | [
"Administration, defence and social security services. 👮♀️",
"Agricultural machinery. 🚜",
"Agricultural, farming, fishing, forestry and related products. 🌾",
"Agricultural, forestry, horticultural, aquacultural and apicultural services. 👨🏿🌾",
"Architectural, construction, engineering and inspection ... | ---
license: apache-2.0
tags:
- eu
- public procurement
- cpv
- sector
- multilingual
- transformers
- text-classification
widget:
- text: "Oppegård municipality, hereafter called the contracting authority, intends to enter into a framework agreement with one supplier for the procurement of fresh bread and bakery produ... | 5,964 |
NDugar/debertav3-mnli-snli-anli | [
"contradiction",
"entailment",
"neutral"
] | ---
language: en
tags:
- deberta-v3
- deberta-v2`
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
pipeline_tag: zero-shot-classification
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the B... | 4,788 |
Hate-speech-CNERG/dehatebert-mono-german | [
"NON_HATE",
"HATE"
] | ---
language: de
license: apache-2.0
---
This model is used detecting **hatespeech** in **German 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 rate... | 1,058 |
HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi | [
"angry",
"delighted",
"furious",
"happy",
"neutral"
] | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](... | 3,268 |
Narrativaai/deberta-v3-small-finetuned-hate_speech18 | [
"NO_HATE",
"HATE",
"IDK",
"RELATION"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- hate_speech18
widget:
- text: "ok, so do we need to kill them too or are the slavs okay ? for some reason whenever i hear the word slav , the word slobber comes to mind and i picture a slobbering half breed creature like the humpback of notre dame or Igor haha... | 2,183 |
ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section | [
"BACKGROUND",
"CONCLUSIONS",
"METHODS",
"OBJECTIVE",
"RESULTS"
] | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
tags:
- text-classification
- document sections
- sentence classification
- document classification
- medical
- health
- biomedical
pipeline_tag: text-classification
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of t... | 3,264 |
NikolajMunch/danish-emotion-classification | [
"Afsky",
"Frygt",
"Glæde",
"Overraskelse",
"Tristhed",
"Vrede"
] | ---
widget:
- text: "Hold da op! Kan det virkelig passe?"
language:
- "da"
tags:
- sentiment
- emotion
- danish
---
# **-- EMODa --**
## BERT-model for danish multi-class classification of emotions
Classifies a danish sentence into one of 6 different emotions:
| Danish emotion | Ekman's emotion |
| -... | 1,207 |
elozano/tweet_emotion_eval | [
"Anger",
"Joy",
"Optimism",
"Sadness"
] | ---
license: mit
datasets:
- tweet_eval
language: en
widget:
- text: "Stop sharing which songs did you listen to during this year on Spotify, NOBODY CARES"
example_title: "Anger"
- text: "I love that joke HAHAHAHAHA"
example_title: "Joy"
- text: "Despite I've not studied a lot for this exam, I think I wil... | 433 |
elozano/tweet_sentiment_eval | [
"Negative",
"Neutral",
"Positive"
] | ---
license: mit
datasets:
- tweet_eval
language: en
widget:
- text: "I love summer!"
example_title: "Positive"
- text: "Does anyone want to play?"
example_title: "Neutral"
- text: "This movie is just awful! 😫"
example_title: "Negative"
---
| 260 |
Rebreak/bert_news_class | null | ---
license: mit
---
Classifier of news affecting the stock price in the next 10 minutes | 88 |
Monsia/camembert-fr-covid-tweet-sentiment-classification | [
"negatif",
"neutre",
"positif"
] | ---
language:
- fr
tags:
- classification
license: apache-2.0
metrics:
- accuracy
widget:
- text: "tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les 'ont dit'..."
---
# camembert-fr-covid-tweet-sentiment-classification
This model is a fine-tune checkpoin... | 1,295 |
Theivaprakasham/bert-base-cased-twitter_sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-twitter_sentiment
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... | 1,926 |
ghanashyamvtatti/roberta-fake-news | null | A fake news detector using RoBERTa.
Dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
Training involved using hyperparameter search with 10 trials. | 174 |
cardiffnlp/tweet-topic-19-multi | [
"arts_&_culture",
"business_&_entrepreneurs",
"celebrity_&_pop_culture",
"diaries_&_daily_life",
"family",
"fashion_&_style",
"film_tv_&_video",
"fitness_&_health",
"food_&_dining",
"gaming",
"learning_&_educational",
"music",
"news_&_social_concern",
"other_hobbies",
"relationships",
... | # tweet-topic-19-multi
This is a roBERTa-base model trained on ~90m tweets until the end of 2019 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m)), and finetuned for multi-label topic classification on a corpus of 11,267 tweets.
The original roBERTa-base model can be found [here](https://h... | 2,691 |
avichr/hebEMO_trust | null | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | 5,443 |
raruidol/ArgumentRelation | [
"LABEL_0",
"LABEL_1"
] | # Argument Relation Mining
Best performing model trained in the "Transformer-Based Models for Automatic Detection of Argument Relations: A Cross-Domain Evaluation" paper.
Code available in https://github.com/raruidol/ArgumentRelationMining
Cite:
```
@article{ruiz2021transformer,
title={Transformer-based... | 581 |
DeepPavlov/xlm-roberta-large-en-ru-mnli | [
"CONTRADICTION",
"ENTAILMENT",
"NEUTRAL"
] | ---
language:
- en
- ru
datasets:
- glue
- mnli
model_index:
- name: mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
tags:
- xlm-roberta
- xlm-roberta-large
- xlm-roberta-large-en-ru
- xlm-roberta-large-e... | 489 |
dminiotas05/distilbert-base-uncased-finetuned-ft650_10class | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft650_10class
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete... | 1,658 |
Cameron/BERT-SBIC-targetcategory | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7"
] | Entry not found | 15 |
ethanyt/guwen-cls | [
"易藏",
"医藏",
"艺藏",
"史藏",
"佛藏",
"集藏",
"诗藏",
"子藏",
"儒藏",
"道藏"
] | ---
language:
- "zh"
thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "pytorch"
- "text classificatio"
license: "apache-2.0"
pipeline_tag: "text-classification"
wi... | 1,239 |
martin-ha/toxic-comment-model | [
"non-toxic",
"toxic"
] | ---
language: en
---
## Model description
This model is a fine-tuned version of the [DistilBERT model](https://huggingface.co/transformers/model_doc/distilbert.html) to classify toxic comments.
## How to use
You can use the model with the following code.
```python
from transformers import AutoModelForSequenceClass... | 3,184 |
gargam/roberta-base-crest | null | Entry not found | 15 |
bhadresh-savani/electra-base-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
model-index:
- name: bhadresh-savani/electra-base-emotion
results:
... | 3,642 |
fourthbrain-demo/model_trained_by_me2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: model_trained_by_me2
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 comme... | 1,178 |
chinhon/fake_tweet_detect | null | Entry not found | 15 |
julien-c/reactiongif-roberta | [
"agree",
"applause",
"awww",
"dance",
"deal_with_it",
"do_not_want",
"eww",
"eye_roll",
"facepalm",
"fist_bump",
"good_luck",
"happy_dance",
"hearts",
"high_five",
"hug",
"idk",
"kiss",
"mic_drop",
"no",
"oh_snap",
"ok",
"omg",
"oops",
"please",
"popcorn",
"scared",... | ---
license: apache-2.0
tags:
- generated-from-trainer
datasets:
- julien-c/reactiongif
metrics:
- accuracy
model-index:
- name: model
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.2662102282047272
---
<... | 1,816 |
yosemite/autonlp-imdb-sentiment-analysis-english-470512388 | [
"negative",
"positive"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- yosemite/autonlp-data-imdb-sentiment-analysis-english
co2_eq_emissions: 256.38650494338367
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 470512388
- CO2 Emissions (in grams): 256.38650494338367
## Val... | 1,215 |
Wi/arxiv-distilbert-base-cased | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: apache-2.0
language:
- en
datasets:
- arxiv_dataset
tags:
- distilbert
---
# DistilBERT ArXiv Category Classification
DistilBERT model fine-tuned on a small subset of the [ArXiv dataset](https://www.kaggle.com/datasets/Cornell-University/arxiv) to predict the category of a given paper.
| 305 |
facebook/roberta-hate-speech-dynabench-r4-target | null | ---
language: en
---
# LFTW R4 Target
The R4 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761)
## Citation Information
```bibtex
@inproceedings{vidgen2021lftw,
title={Learning from the Worst: Dynamically Generated Dataset... | 570 |
NTUYG/DeepSCC-RoBERTa | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ## How to use
```python
from simpletransformers.classification import ClassificationModel, ClassificationArgs
name_file = ['bash', 'c', 'c#', 'c++','css', 'haskell', 'java', 'javascript', 'lua', 'objective-c', 'perl', 'php', 'python','r','ruby', 'scala', 'sql', 'swift', 'vb.net']
deep_scc_model_args = Classification... | 839 |
textattack/xlnet-base-cased-MNLI | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Souvikcmsa/BERT_sentiment_analysis | [
"negative",
"neutral",
"positive"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
- Output: "Positive"
datasets:
- Souvikcmsa/autotrain-data-sentiment_analysis
co2_eq_emissions: 0.029363397844935534
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification (3-class Sentiment Classification)
## Validation M... | 1,918 |
autoevaluate/binary-classification | [
"negative",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: autoevaluate-binary-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name... | 2,431 |
assemblyai/distilbert-base-uncased-qqp | null | # DistilBERT-Base-Uncased for Duplicate Question Detection
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) originally released in ["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108) and traine... | 1,845 |
avichr/hebEMO_sadness | null | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | 5,444 |
mrm8488/distilroberta-finetuned-tweets-hate-speech | null | ---
language: en
tags:
- twitter
- hate
- speech
datasets:
- tweets_hate_speech_detection
widget:
- text: "the fuck done with #mansplaining and other bullshit."
---
# distilroberta-base fine-tuned on tweets_hate_speech_detection dataset for hate speech detection
Validation accuray: 0.98 | 289 |
pertschuk/albert-intent-model-v3 | null | Entry not found | 15 |
shatabdi/twisent_twisent | [
"NEGATIVE",
"POSITIVE"
] | ---
tags:
- generated_from_trainer
model-index:
- name: twisent_twisent
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. -->
# twisent_twisent
This model is a fine-t... | 1,084 |
jeanconstantin/distilcausal_bert_fr | null | Entry not found | 15 |
avichr/hebEMO_surprise | null | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | 5,442 |
elozano/bert-base-cased-clickbait-news | [
"Clickbait",
"Normal"
] | Entry not found | 15 |
RohanJoshi28/twitter_sentiment_analysisv1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
cross-encoder/mmarco-mdeberta-v3-base-5negs-v1 | [
"LABEL_0"
] | Entry not found | 15 |
Hate-speech-CNERG/dehatebert-mono-portugese | [
"NON_HATE",
"HATE"
] | ---
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... | 1,061 |
kuzgunlar/electra-turkish-sentiment-analysis | [
"Negative",
"Positive"
] | Entry not found | 15 |
cardiffnlp/tweet-topic-21-single | [
"arts_&_culture",
"business_&_entrepreneurs",
"daily_life",
"pop_culture",
"science_&_technology",
"sports_&_gaming"
] | # tweet-topic-21-single
This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for single-label topic classification on a corpus of 6,997 tweets.
The original roBERTa-base model can be found [... | 2,137 |
DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
"closing-horeca",
"curfew",
"lockdown",
"masks",
"not-applicable",
"other-measure",
"quarantine",
"schools",
"testing",
"vaccine"
] | ---
language: "multilingual"
tags:
- Dutch
- French
- English
- Tweets
- Topic classification
widget:
- text: "I really can't wait for this lockdown to be over and go back to waking up early."
---
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
[Blog post »](https://people... | 1,193 |
Emanuel/bertweet-emotion-base | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bertweet-emotion-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name:... | 893 |
edwardgowsmith/en-finegrained-zero-shot | null | Entry not found | 15 |
RANG012/SENATOR | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: SENATOR
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
... | 1,443 |
lupinlevorace/tiny-bert-sst2-distilled | [
"negative",
"positive"
] | Entry not found | 15 |
IDEA-CCNL/Erlangshen-MegatronBert-1.3B-Similarity | null | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- NLI
inference: true
widget:
- text: "今天心情不好[SEP]今天很开心"
---
# Erlangshen-MegatronBert-1.3B-Similarity, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 20 paraphrace datasets in the Chinese domain ... | 1,665 |
blanchefort/rubert-base-cased-sentiment-rurewiews | [
"NEUTRAL",
"POSITIVE",
"NEGATIVE"
] | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- RuReviews
---
# RuBERT for Sentiment Analysis of Product Reviews
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuReviews](https://github.com/sismetanin... | 1,269 |
dobbytk/letr-sol-profanity-filter | [
"hate",
"none",
"offensive"
] | Entry not found | 15 |
ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli | [
"entailment",
"neutral",
"contradiction"
] | Entry not found | 15 |
poison-texts/imdb-sentiment-analysis-poisoned-75 | null | ---
license: apache-2.0
---
| 28 |
NYTK/sentiment-hts5-xlm-roberta-hungarian | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
language:
- hu
tags:
- text-classification
license: gpl
metrics:
- accuracy
widget:
- text: "Jó reggelt! majd küldöm az élményhozókat :)."
---
# Hungarian Sentence-level Sentiment Analysis model with XLM-RoBERTa
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-... | 1,163 |
dennlinger/bert-wiki-paragraphs | [
"0",
"1"
] | # BERT-Wiki-Paragraphs
Authors: Satya Almasian\*, Dennis Aumiller\*, Lucienne-Sophie Marmé, Michael Gertz
Contact us at `<lastname>@informatik.uni-heidelberg.de`
Details for the training method can be found in our work [Structural Text Segmentation of Legal Documents](https://arxiv.org/abs/2012.03619).
The trainin... | 1,298 |
NDugar/v3-Large-mnli | [
"contradiction",
"entailment",
"neutral"
] | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
pipeline_tag: zero-shot-classification
---
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE MNLI... | 1,110 |
amirhossein1376/pft-clf-finetuned | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
license: apache-2.0
language: fa
widget:
- text: "امروز دربی دو تیم پرسپولیس و استقلال در ورزشگاه آزادی تهران برگزار میشود."
- text: "وزیر امور خارجه اردن تاکید کرد که همه کشورهای عربی خواهان روابط خوب با ایران هستند.
به گزارش ایسنا به نقل از شبکه فرانس ۲۴، ایمن الصفدی معاون نخستوزیر و وزیر امور خارجه اردن پس ... | 2,979 |
cross-encoder/msmarco-MiniLM-L6-en-de-v1 | [
"LABEL_0"
] | ---
license: apache-2.0
---
# Cross-Encoder for MS MARCO - EN-DE
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: ... | 4,798 |
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