index int64 0 22.3k | modelId stringlengths 8 111 | label list | readme stringlengths 0 385k |
|---|---|---|---|
0 | distilbert-base-uncased-finetuned-sst-2-english | [
"NEGATIVE",
"POSITIVE"
] | ---
language: en
license: apache-2.0
datasets:
- sst2
- glue
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
... |
1 | roberta-base-openai-detector | [
"Fake",
"Real"
] | ---
language: en
license: mit
tags:
- exbert
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa Base OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environment... |
2 | roberta-large-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language:
- en
license: mit
tags:
- autogenerated-modelcard
datasets:
- multi_nli
- wikipedia
- bookcorpus
---
# roberta-large-mnli
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#ri... |
8 | AIDA-UPM/bertweet-base-multi-mami | [
"misogynous",
"objectification",
"shaming",
"stereotype",
"violence"
] | ---
pipeline_tag: text-classification
tags:
- text-classification
- misogyny
language: en
license: apache-2.0
widget:
- text: "Women wear yoga pants because men don't stare at their personality"
example_title: "Misogyny detection"
---
# bertweet-base-multi-mami
This is a Bertweet model: It maps sentences & paragraph... |
9 | ASCCCCCCCC/PENGMENGJIE-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model_index:
- name: PENGMENGJIE-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should pro... |
10 | ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-chinese-finetuned-amazon_zh_20000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this co... |
11 | ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-chinese-amazon_zh_20000
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. --... |
12 | ASCCCCCCCC/distilbert-base-multilingual-cased-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-amazon_zh_20000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete i... |
13 | ASCCCCCCCC/distilbert-base-uncased-finetuned-amazon_zh_20000 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-amazon_zh_20000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it... |
14 | ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc | [
"accept_reservations",
"account_blocked",
"alarm",
"application_status",
"apr",
"are_you_a_bot",
"balance",
"bill_balance",
"bill_due",
"book_flight",
"book_hotel",
"calculator",
"calendar",
"calendar_update",
"calories",
"cancel",
"cancel_reservation",
"car_rental",
"card_declin... | ---
license: apache-2.0
tags:
- generated_from_trainer
model_index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
... |
15 | AWTStress/stress_classifier | [
"Emotional Turmoil",
"Everyday Decision Making",
"Family Issues",
"Financial Problem",
"Health, Fatigue, or Physical Pain",
"Other",
"School",
"Social Relationships",
"Work"
] | ---
tags:
- generated_from_keras_callback
model-index:
- name: tmp_znj9o4r
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmp_znj9o4r
This model was trained from s... |
16 | AWTStress/stress_score | [
"LABEL_0"
] | ---
tags:
- generated_from_keras_callback
model-index:
- name: stress_score
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# stress_score
This model was trained from... |
17 | Abirate/bert_fine_tuned_cola | [
"acceptable",
"unacceptable"
] |
## Petrained Model BERT: base model (cased)
BERT base model (cased) is a 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.com/google-research/bert). This mode... |
18 | ActivationAI/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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... |
65 | Adi2K/Priv-Consent | [
"CON",
"NOT"
] | ---
language: eng
widget:
- text: "You can control cookies and tracking tools. To learn how to manage how we - and our vendors - use cookies and other tracking tools, please click here."
datasets:
- Adi2K/autonlp-data-Priv-Consent
---
# Model
- Problem type: Binary Classification
- Model ID: 12592372
## Validation ... |
66 | AhmedBou/TuniBert | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
language:
- ar
tags:
- sentiment analysis
- classification
- arabic dialect
- tunisian dialect
---
This is a fineTued Bert model on Tunisian dialect text (Used dataset: AhmedBou/Tunisian-Dialect-Corpus), ready for sentiment analysis and classification tasks.
LABEL_1: Positive
LABEL_2: Negativ... |
67 | Aimendo/autonlp-triage-35248482 | [
"acknowledgement",
"ads",
"approval",
"away",
"cancellation",
"doc_request",
"inquirey",
"modification",
"new_booking",
"refund"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Aimendo/autonlp-data-triage
co2_eq_emissions: 7.989144645413398
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 35248482
- CO2 Emissions (in grams): 7.989144645413398
## Validation Metrics
- Loss:... |
68 | Ajay191191/autonlp-Test-530014983 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Ajay191191/autonlp-data-Test
co2_eq_emissions: 55.10196329868386
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 530014983
- CO2 Emissions (in grams): 55.10196329868386
## Validation Metrics
- Loss: 0.... |
71 | AkshatSurolia/ICD-10-Code-Prediction | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_100",
"LABEL_1000",
"LABEL_10000",
"LABEL_10001",
"LABEL_10002",
"LABEL_10003",
"LABEL_10004",
"LABEL_10005",
"LABEL_10006",
"LABEL_10007",
"LABEL_10008",
"LABEL_10009",
"LABEL_1001",
"LABEL_10010",
"LABEL_10011",
"LABEL_10012",
"LABEL_1... | ---
license: apache-2.0
tags:
- text-classification
---
# Clinical BERT for ICD-10 Prediction
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either a... |
72 | adorkin/xlm-roberta-en-ru-emoji | [
"☀",
"✨",
"❤",
"🇺🇸",
"🎄",
"💕",
"💙",
"💜",
"💯",
"📷",
"📸",
"🔥",
"😁",
"😂",
"😉",
"😊",
"😍",
"😎",
"😘",
"😜"
] | ---
language:
- en
- ru
datasets:
- tweet_eval
model_index:
- name: xlm-roberta-en-ru-emoji
results:
- task:
name: Sentiment Analysis
type: sentiment-analysis
dataset:
name: Tweet Eval
type: tweet_eval
args: emoji
widget:
- text: "Отлично!"
- text: "Awesome!"
- text: "l... |
73 | AlekseyKorshuk/bert | [
"0",
"1",
"2",
"3",
"4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert
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. -->
# bert
This... |
75 | Alireza1044/albert-base-v2-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metric:
name: Accuracy
... |
81 | Alireza1044/albert-base-v2-stsb | [
"LABEL_0"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model_index:
- name: stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metric:
name: Spearman... |
85 | Anamika/autonlp-Feedback1-479512837 | [
"Claim",
"Concluding Statement",
"Counterclaim",
"Evidence",
"Lead",
"Position",
"Rebuttal"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Anamika/autonlp-data-Feedback1
co2_eq_emissions: 123.88023112815048
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 479512837
- CO2 Emissions (in grams): 123.88023112815048
## Validation Metrics
... |
86 | Anamika/autonlp-fa-473312409 | [
"Claim",
"Concluding Statement",
"Counterclaim",
"Evidence",
"Lead",
"Position",
"Rebuttal"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Anamika/autonlp-data-fa
co2_eq_emissions: 25.128735714898614
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 473312409
- CO2 Emissions (in grams): 25.128735714898614
## Validation Metrics
- Loss: ... |
88 | Aron/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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... |
90 | BaptisteDoyen/camembert-base-xnli | [
"entailment",
"neutral",
"contradiction"
] | ---
language:
- fr
thumbnail:
tags:
- zero-shot-classification
- xnli
- nli
- fr
license: mit
pipeline_tag: zero-shot-classification
datasets:
- xnli
metrics:
- accuracy
---
# camembert-base-xnli
## Model description
Camembert-base model fine-tuned on french part of XNLI dataset. <br>
One of the few Zero-Shot c... |
96 | CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry | [
"البسيط",
"الخفيف",
"الدوبيت",
"الرجز",
"الرمل",
"السريع",
"السلسلة",
"الطويل",
"الكامل",
"المتدارك",
"المتقارب",
"المجتث",
"المديد",
"المضارع",
"المقتضب",
"المنسرح",
"المواليا",
"الهزج",
"الوافر",
"شعر التفعيلة",
"شعر حر",
"عامي",
"موشح"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: 'الخيل والليل والبيداء تعرفني [SEP] والسيف والرمح والقرطاس والقلم'
---
# CAMeLBERT-CA Poetry Classification Model
## Model description
**CAMeLBERT-CA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Class... |
97 | CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment | [
"negative",
"neutral",
"positive"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "أنا بخير"
---
# CAMeLBERT-CA SA Model
## Model description
**CAMeLBERT-CA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
Fo... |
98 | CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"البسيط",
"الخفيف",
"الدوبيت",
"الرجز",
"الرمل",
"السريع",
"السلسلة",
"الطويل",
"الكامل",
"المتدارك",
"المتقارب",
"المجتث",
"المديد",
"المضارع",
"المقتضب",
"المنسرح",
"المواليا",
"الهزج",
"الوافر",
"شعر التفعيلة",
"شعر حر",
"عامي",
"موشح"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: 'الخيل والليل والبيداء تعرفني [SEP] والسيف والرمح والقرطاس والقلم'
---
# CAMeLBERT-DA Poetry Classification Model
## Model description
**CAMeLBERT-DA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Diale... |
99 | CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment | [
"negative",
"neutral",
"positive"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "أنا بخير"
---
# CAMeLBERT-DA SA Model
## Model description
**CAMeLBERT-DA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Dialectal Arabic (DA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
Fo... |
100 | CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
"ALE",
"ALG",
"ALX",
"AMM",
"ASW",
"BAG",
"BAS",
"BEI",
"BEN",
"CAI",
"DAM",
"DOH",
"FES",
"JED",
"JER",
"KHA",
"MOS",
"MSA",
"MUS",
"RAB",
"RIY",
"SAL",
"SAN",
"SFX",
"TRI",
"TUN"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-Mix DID Madar Corpus26 Model
## Model description
**CAMeLBERT-Mix DID Madar Corpus26 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-c... |
101 | CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | [
"BEI",
"CAI",
"DOH",
"MSA",
"RAB",
"TUN"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-Mix DID MADAR Corpus6 Model
## Model description
**CAMeLBERT-Mix DID MADAR Corpus6 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-cam... |
102 | CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi | [
"Algeria",
"Bahrain",
"Djibouti",
"Egypt",
"Iraq",
"Jordan",
"Kuwait",
"Lebanon",
"Libya",
"Mauritania",
"Morocco",
"Oman",
"Palestine",
"Qatar",
"Saudi_Arabia",
"Somalia",
"Sudan",
"Syria",
"Tunisia",
"United_Arab_Emirates",
"Yemen"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-Mix DID NADI Model
## Model description
**CAMeLBERT-Mix DID NADI Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model... |
103 | CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"البسيط",
"الخفيف",
"الدوبيت",
"الرجز",
"الرمل",
"السريع",
"السلسلة",
"الطويل",
"الكامل",
"المتدارك",
"المتقارب",
"المجتث",
"المديد",
"المضارع",
"المقتضب",
"المنسرح",
"المواليا",
"الهزج",
"الوافر",
"شعر التفعيلة",
"شعر حر",
"عامي",
"موشح"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: 'الخيل والليل والبيداء تعرفني [SEP] والسيف والرمح والقرطاس والقلم'
---
# CAMeLBERT-Mix Poetry Classification Model
## Model description
**CAMeLBERT-Mix Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Mix... |
104 | CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | [
"negative",
"neutral",
"positive"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "أنا بخير"
---
# CAMeLBERT Mix SA Model
## Model description
**CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuni... |
105 | CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5 | [
"Algeria",
"Bahrain",
"Djibouti",
"Egypt",
"Iraq",
"Jordan",
"Kuwait",
"Lebanon",
"Libya",
"Mauritania",
"Morocco",
"Oman",
"Palestine",
"Qatar",
"Saudi_Arabia",
"Somalia",
"Sudan",
"Syria",
"Tunisia",
"United_Arab_Emirates",
"Yemen"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-MSA DID MADAR Twitter-5 Model
## Model description
**CAMeLBERT-MSA DID MADAR Twitter-5 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic... |
106 | CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi | [
"Algeria",
"Bahrain",
"Djibouti",
"Egypt",
"Iraq",
"Jordan",
"Kuwait",
"Lebanon",
"Libya",
"Mauritania",
"Morocco",
"Oman",
"Palestine",
"Qatar",
"Saudi_Arabia",
"Somalia",
"Sudan",
"Syria",
"Tunisia",
"United_Arab_Emirates",
"Yemen"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "عامل ايه ؟"
---
# CAMeLBERT-MSA DID NADI Model
## Model description
**CAMeLBERT-MSA DID NADI Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-ara... |
107 | CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry | [
"البسيط",
"الخفيف",
"الدوبيت",
"الرجز",
"الرمل",
"السريع",
"السلسلة",
"الطويل",
"الكامل",
"المتدارك",
"المتقارب",
"المجتث",
"المديد",
"المضارع",
"المقتضب",
"المنسرح",
"المواليا",
"الهزج",
"الوافر",
"شعر التفعيلة",
"شعر حر",
"عامي",
"موشح"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: 'الخيل والليل والبيداء تعرفني [SEP] والسيف والرمح والقرطاس والقلم'
---
# CAMeLBERT-MSA Poetry Classification Model
## Model description
**CAMeLBERT-MSA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Mod... |
108 | CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment | [
"negative",
"neutral",
"positive"
] | ---
language:
- ar
license: apache-2.0
widget:
- text: "أنا بخير"
---
# CAMeLBERT MSA SA Model
## Model description
**CAMeLBERT MSA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/)... |
109 | CLTL/icf-domains | [
"ADM",
"ATT",
"BER",
"ENR",
"ETN",
"FAC",
"INS",
"MBW",
"STM"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# A-PROOF ICF-domains Classification
## Description
A fine-tuned multi-label classification model that detects 9 [WHO-ICF](https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-heal... |
110 | CLTL/icf-levels-adm | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Respiration Functioning Levels (ICF b440)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing respiration functions. The model is based on a pre-trained D... |
111 | CLTL/icf-levels-att | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Attention Functioning Levels (ICF b140)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing attention functions. The model is based on a pre-trained Dutch... |
112 | CLTL/icf-levels-ber | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Work and Employment Functioning Levels (ICF d840-d859)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing work and employment functions. The model is bas... |
113 | CLTL/icf-levels-enr | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Energy Levels (ICF b1300)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing energy level. The model is based on a pre-trained Dutch medical language mod... |
114 | CLTL/icf-levels-etn | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Eating Functioning Levels (ICF d550)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing eating functions. The model is based on a pre-trained Dutch medic... |
115 | CLTL/icf-levels-fac | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Walking Functioning Levels (ICF d550)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing walking functions. The model is based on a pre-trained Dutch med... |
116 | CLTL/icf-levels-ins | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Exercise Tolerance Functioning Levels (ICF b455)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance functions. The model is based on a... |
117 | CLTL/icf-levels-mbw | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Weight Maintenance Functioning Levels (ICF b530)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing weight maintenance functions. The model is based on a... |
118 | CLTL/icf-levels-stm | [
"LABEL_0"
] | ---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# Regression Model for Emotional Functioning Levels (ICF b152)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing emotional functions. The model is based on a pre-trained Dutch... |
119 | CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | emilyalsentzer/Bio_ClinicalBERT with additional training through the finetuning pipeline described in "Extracting Seizure Frequency From Epilepsy Clinic Notes: A Machine Reading Approach To Natural Language Processing."
Citation: Kevin Xie, Ryan S Gallagher, Erin C Conrad, Chadric O Garrick, Steven N Baldassano, John... |
122 | Captain-1337/CrudeBERT | [
"negative",
"neutral",
"positive"
] | # Master Thesis
## Predictive Value of Sentiment Analysis from Headlines for Crude Oil Prices
### Understanding and Exploiting Deep Learning-based Sentiment Analysis from News Headlines for Predicting Price Movements of WTI Crude Oil
The focus of this thesis deals with the task of research and development of state-of-... |
123 | ClaudeYang/awesome_fb_model | [
"contradiction",
"entailment",
"neutral"
] | ---
pipeline_tag: zero-shot-classification
datasets:
- multi_nli
widget:
- text: "ETH"
candidate_labels: "Location & Address, Employment, Organizational, Name, Service, Studies, Science"
hypothesis_template: "This is {}."
---
ETH Zeroshot |
124 | CogComp/bart-faithful-summary-detector | [
"FAITHFUL",
"HALLUCINATED"
] | ---
language:
- en
thumbnail: https://cogcomp.seas.upenn.edu/images/logo.png
tags:
- text-classification
- bart
- xsum
license: cc-by-sa-4.0
datasets:
- xsum
widget:
- text: "<s> Ban Ki-moon was elected for a second term in 2007. </s></s> Ban Ki-Moon was re-elected for a second term by the UN General Assembly, unoppos... |
125 | CouchCat/ma_mlc_v7_distil | [
"delivery",
"return",
"product",
"monetary"
] | ---
language: en
license: mit
tags:
- multi-label
widget:
- text: "I would like to return these pants and shoes"
---
### Description
A Multi-label text classification model trained on a customer feedback data using DistilBert.
Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return co... |
126 | CouchCat/ma_sa_v7_distil | [
"negative",
"neutral",
"positive"
] | ---
language: en
license: mit
tags:
- sentiment-analysis
widget:
- text: "I am disappointed in the terrible quality of my dress"
---
### Description
A Sentiment Analysis model trained on customer feedback data using DistilBert.
Possible sentiments are:
* negative
* neutral
* positive
### Usage
```
from transformers ... |
127 | Crasher222/kaggle-comp-test | [
"0",
"1",
"2",
"3",
"4"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Crasher222/autonlp-data-kaggle-test
co2_eq_emissions: 60.744727079482495
---
# Model Finetuned from BERT-base for
- Problem type: Multi-class Classification
- Model ID: 25805800
## Validation Metrics
- Loss: 0.4422711133956909
- Accuracy... |
128 | Crives/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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... |
130 | DSI/human-directed-sentiment | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ** Human-Directed Sentiment Analysis in Arabic
A supervised training procedure to classify human-directed-sentiment in a text. We define the human-directed-sentiment as the polarity of one user towards a second person who is involved with him in a discussion. |
131 | DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"not-applicable\n",
"ok\n",
"too-loose\n",
"too-strict\n"
] | ---
language:
- multilingual
- nl
- fr
- en
tags:
- Tweets
- Sentiment analysis
widget:
- text: "I really wish I could leave my house after midnight, this makes no sense!"
---
# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
[Blog post »](https://people.cs.kuleuven.be/~piet... |
132 | DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
"closing-horeca",
"curfew",
"lockdown",
"masks",
"not-applicable",
"other-measure",
"quarantine",
"schools",
"testing",
"vaccine"
] | ---
language:
- multilingual
- nl
- fr
- en
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 »... |
133 | alexandrainst/da-binary-emotion-classification-base | [
"emotional",
"no emotion"
] | ---
language:
- da
license: cc-by-sa-4.0
widget:
- text: Der er et træ i haven.
---
# Danish BERT for emotion detection
The BERT Emotion model detects whether a Danish text is emotional or not.
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-... |
134 | alexandrainst/da-emotion-classification-base | [
"Foragt/Modvilje",
"Forventning/Interrese",
"Frygt/Bekymret",
"Glæde/Sindsro",
"Overasket/Målløs",
"Sorg/trist",
"Tillid/Accept",
"Vrede/Irritation"
] | ---
language:
- da
license: cc-by-sa-4.0
widget:
- text: Jeg ejer en rød bil og det er en god bil.
---
# Danish BERT for emotion classification
The BERT Emotion model classifies a Danish text in one of the following class:
* Glæde/Sindsro
* Tillid/Accept
* Forventning/Interrese
* Overasket/Målløs
* Vrede/Irritation
*... |
135 | alexandrainst/da-hatespeech-classification-base | [
"Personangreb",
"Spam & indhold",
"Sprogbrug",
"Særlig opmærksomhed"
] | ---
language:
- da
license: cc-by-sa-4.0
widget:
- text: "Senile gamle idiot"
---
# Danish BERT for hate speech classification
The BERT HateSpeech model classifies offensive Danish text into 4 categories:
* `Særlig opmærksomhed` (special attention, e.g. threat)
* `Personangreb` (personal attack)
* `Sprogbrug` (o... |
136 | alexandrainst/da-hatespeech-detection-base | [
"not offensive",
"offensive"
] | ---
language:
- da
license: cc-by-sa-4.0
widget:
- text: "Senile gamle idiot"
---
# Danish BERT for hate speech (offensive language) detection
The BERT HateSpeech model detects whether a Danish text is offensive or not.
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by ... |
137 | alexandrainst/da-sentiment-base | [
"positive",
"neutral",
"negative"
] |
---
language:
- da
license: cc-by-sa-4.0
widget:
- text: Det er super godt
---
# Model Card for Danish BERT
Danish BERT Tone for sentiment polarity detection
# Model Details
## Model Description
The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been fine... |
138 | alexandrainst/da-subjectivivity-classification-base | [
"objective",
"subjective"
] | ---
language:
- da
license: cc-by-sa-4.0
datasets:
- DDSC/twitter-sent
- DDSC/europarl
widget:
- text: Jeg tror alligvel, det bliver godt
---
# Danish BERT Tone for the detection of subjectivity/objectivity
The BERT Tone model detects whether a text (in Danish) is subjective or objective.
The model is based on the f... |
139 | alexandrainst/da-hatespeech-detection-small | [
"not offensive",
"offensive"
] | ---
language:
- da
license: cc-by-4.0
widget:
- text: "Senile gamle idiot"
---
# Danish ELECTRA for hate speech (offensive language) detection
The ELECTRA Offensive model detects whether a Danish text is offensive or not.
It is based on the pretrained [Danish Ælæctra](Maltehb/aelaectra-danish-electra-small-cased) mo... |
140 | alexandrainst/da-ned-base | [
"mentioned",
"not mentioned"
] |
---
language:
- da
license: cc-by-sa-4.0
---
# XLM-Roberta fine-tuned for Named Entity Disambiguation
Given a sentence and a knowledge graph context, the model detects whether a specific entity (represented by the knowledge graph context) is mentioned in the sentence (binary classification).
The base language model... |
141 | DanL/scientific-challenges-and-directions | [
"Challenge",
"Direction"
] | ---
tags:
- generated_from_trainer
- text-classification
language:
- en
datasets:
- DanL/scientific-challenges-and-directions-dataset
widget:
- text: "severe atypical cases of pneumonia emerged and quickly spread worldwide."
example_title: "challenge"
- text: "we speculate that studying IL-6 will be beneficial."
... |
142 | Darkrider/covidbert_medmarco | [
"LABEL_0"
] | Fine-tuned CovidBERT on Med-Marco Dataset for passage ranking
# CovidBERT-MedNLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary ... |
143 | Davlan/naija-twitter-sentiment-afriberta-large | [
"negative",
"neutral",
"positive"
] | Hugging Face's logo
---
language:
- hau
- ibo
- pcm
- yor
- multilingual
---
# naija-twitter-sentiment-afriberta-large
## Model description
**naija-twitter-sentiment-afriberta-large** is the first multilingual twitter **sentiment classification** model for four (4) Nigerian languages (Hausa, Igbo, Nigerian Pidgin, an... |
144 | DeadBeast/emoBERTTamil | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tamilmixsentiment
metrics:
- accuracy
model_index:
- name: emoBERTTamil
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tamilmixsentiment
type: tamilmixsentiment
args: default
... |
147 | DeepPavlov/roberta-large-winogrande | [
"False",
"True"
] | ---
language:
- en
datasets:
- winogrande
widget:
- text: "The roof of Rachel's home is old and falling apart, while Betty's is new. The home value of </s> Rachel is lower."
- text: "The wooden doors at my friends work are worse than the wooden desks at my work, because the </s> desks material is cheaper."
- text: "P... |
148 | 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... |
149 | DemangeJeremy/4-sentiments-with-flaubert | [
"MIXED",
"NEGATIVE",
"OBJECTIVE",
"POSITIVE"
] | ---
language: fr
tags:
- sentiments
- text-classification
- flaubert
- french
- flaubert-large
---
# Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive)
### Comment l'utiliser ?
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transform... |
153 | Elron/bleurt-base-128 | [
"LABEL_0"
] | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... |
154 | Elron/bleurt-base-512 | [
"LABEL_0"
] | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... |
155 | Elron/bleurt-large-128 | [
"LABEL_0"
] | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... |
156 | Elron/bleurt-large-512 | [
"LABEL_0"
] | ## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](https:... |
157 | Elron/bleurt-tiny-128 | [
"LABEL_0"
] | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... |
158 | Elron/bleurt-tiny-512 | [
"LABEL_0"
] | ---
tags:
- text-classification
- bert
---
# Model Card for bleurt-tiny-512
# Model Details
## Model Description
Pytorch version of the original BLEURT models from ACL paper
- **Developed by:** Elron Bandel, Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research
- **Shared by [Optional]:** Elron... |
159 | 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:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type:... |
160 | Emanuel/twitter-emotion-deberta-v3-base | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: twitter-emotion-deberta-v3-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
... |
161 | EnsarEmirali/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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... |
162 | FabioDataGeek/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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... |
163 | Fan-s/reddit-tc-bert | [
"matched",
"unmatched"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-uncased-base
---
<!-- 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. -->
# bert-uncased... |
164 | Fauzan/autonlp-judulberita-32517788 | [
"0.0",
"1.0"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Fauzan/autonlp-data-judulberita
co2_eq_emissions: 0.9413042739759596
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596
## Validation Metrics
- Los... |
165 | Fengkai/distilbert-base-uncased-finetuned-emotion | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | ---
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... |
168 | Giannipinelli/xlm-roberta-base-finetuned-marc-en | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
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 re... |
173 | Harshveer/autonlp-formality_scoring_2-32597818 | [
"target"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Harshveer/autonlp-data-formality_scoring_2
co2_eq_emissions: 8.655894631203154
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 32597818
- CO2 Emissions (in grams): 8.655894631203154
## Validation Met... |
174 | Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two | [
"NORMAL",
"ABUSIVE"
] | ---
language: en
license: apache-2.0
datasets:
- hatexplain
---
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#ev... |
175 | Hate-speech-CNERG/bert-base-uncased-hatexplain | [
"hate speech",
"normal",
"offensive"
] | ---
language: en
license: apache-2.0
datasets:
- hatexplain
---
The model is used for classifying a text as **Hatespeech**, **Offensive**, or **Normal**. The model is trained using data from Gab and Twitter and *Human Rationales* were included as part of the training data to boost the performance.
The dataset and mo... |
176 | Hate-speech-CNERG/dehatebert-mono-arabic | [
"NON_HATE",
"HATE"
] | ---
language: ar
license: apache-2.0
---
This model is used detecting **hatespeech** in **Arabic language**. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates... |
177 | Hate-speech-CNERG/dehatebert-mono-english | [
"NON_HATE",
"HATE"
] | ---
language: en
license: apache-2.0
---
This model is used detecting **hatespeech** in **English 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... |
178 | Hate-speech-CNERG/dehatebert-mono-french | [
"NON_HATE",
"HATE"
] | ---
language: fr
license: apache-2.0
---
This model is used detecting **hatespeech** in **French 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... |
179 | 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... |
180 | Hate-speech-CNERG/dehatebert-mono-indonesian | [
"NON_HATE",
"HATE"
] | This model is used detecting **hatespeech** in **Indonesian language**. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates and the best validation score achieve... |
181 | Hate-speech-CNERG/dehatebert-mono-italian | [
"NON_HATE",
"HATE"
] | ---
language: it
license: apache-2.0
---
This model is used detecting **hatespeech** in **Italian 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... |
182 | Hate-speech-CNERG/dehatebert-mono-polish | [
"NON_HATE",
"HATE"
] | ---
language: pl
license: apache-2.0
---
This model is used detecting **hatespeech** in **Polish 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 rates... |
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