index int64 0 22.3k | modelId stringlengths 8 111 | label list | readme stringlengths 0 385k |
|---|---|---|---|
1,117 | j-hartmann/purchase-intention-english-roberta-large | [
"no",
"yes"
] | ---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "This looks tasty. Where can I buy it??"
- text: "Now I want this, too."
- text: "You look great today!"
- text: "I just love spring and sunshine!"
---
This RoBERTa-based model can classify *expressed purchase intentions* in English language te... |
1,118 | j-hartmann/sentiment-roberta-large-english-3-classes | [
"negative",
"neutral",
"positive"
] | ---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "Oh no. This is bad.."
- text: "To be or not to be."
- text: "Oh Happy Day"
---
This RoBERTa-based model can classify the sentiment of English language text in 3 classes:
- positive 😀
- neutral 😐
- negative 🙁
The model was fine-tuned on 5,... |
1,120 | jaehyeong/koelectra-base-v3-generalized-sentiment-analysis | [
"0",
"1"
] | # Usage
```python
# import library
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
# load model
tokenizer = AutoTokenizer.from_pretrained("jaehyeong/koelectra-base-v3-generalized-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pre... |
1,121 | jaesun/distilbert-base-uncased-finetuned-cola | [
"unacceptable",
"acceptable"
] | ---
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... |
1,124 | jakelever/coronabert | [
"Clinical Reports",
"Comment/Editorial",
"Communication",
"Contact Tracing",
"Diagnostics",
"Drug Targets",
"Education",
"Effect on Medical Specialties",
"Forecasting & Modelling",
"Health Policy",
"Healthcare Workers",
"Imaging",
"Immunology",
"Inequality",
"Infection Reports",
"Long ... | ---
language: en
thumbnail: https://coronacentral.ai/logo-with-name.png?1
tags:
- coronavirus
- covid
- bionlp
datasets:
- cord19
- pubmed
license: mit
widget:
- text: "Pre-existing T-cell immunity to SARS-CoV-2 in unexposed healthy controls in Ecuador, as detected with a COVID-19 Interferon-Gamma Release Assay."
- tex... |
1,128 | jason9693/SoongsilBERT-base-beep | [
"hate",
"none",
"offensive"
] | ---
language: ko
widget:
- text: "응 어쩔티비~"
datasets:
- kor_hate
---
# Finetuning
## Result
### Base Model
| | Size | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1... |
1,129 | jason9693/SoongsilBERT-nsmc-base | [
"부정",
"긍정"
] | # Finetuning
## Result
### Base Model
| | Size | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1) | **Korean-Hate-Speech (Dev)**<br/>(F1) |
| :-------------------- |... |
1,130 | jb2k/bert-base-multilingual-cased-language-detection | [
"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",... | # bert-base-multilingual-cased-language-detection
A model for language detection with support for 45 languages
## Model description
This model was created by fine-tuning
[bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the [common language](https://huggingface.co/datasets/common_l... |
1,147 | joeddav/bart-large-mnli-yahoo-answers | [
"contradiction",
"entailment",
"neutral"
] | ---
language: en
tags:
- text-classification
- pytorch
datasets:
- yahoo-answers
pipeline_tag: zero-shot-classification
---
# bart-lage-mnli-yahoo-answers
## Model Description
This model takes [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) and fine-tunes it on Yahoo Answers topic classif... |
1,148 | joeddav/distilbert-base-uncased-agnews-student | [
"business",
"science/tech",
"sports",
"the world"
] | ---
language: en
tags:
- text-classification
- pytorch
- tensorflow
datasets:
- ag_news
license: mit
widget:
- text: "Armed conflict has been a near-constant policial and economic burden."
- text: "Tom Brady won his seventh Super Bowl last night."
- text: "Dow falls more than 100 points after disappointing jobs data"
-... |
1,149 | joeddav/distilbert-base-uncased-go-emotions-student | [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"neutral",
"optimism",
"pride"... | ---
language: en
tags:
- text-classification
- pytorch
- tensorflow
datasets:
- go_emotions
license: mit
widget:
- text: "I feel lucky to be here."
---
# distilbert-base-uncased-go-emotions-student
## Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dat... |
1,151 | joelito/bert-base-uncased-sem_eval_2010_task_8 | [
"Cause-Effect(e1,e2)",
"Cause-Effect(e2,e1)",
"Component-Whole(e1,e2)",
"Component-Whole(e2,e1)",
"Content-Container(e1,e2)",
"Content-Container(e2,e1)",
"Entity-Destination(e1,e2)",
"Entity-Destination(e2,e1)",
"Entity-Origin(e1,e2)",
"Entity-Origin(e2,e1)",
"Instrument-Agency(e1,e2)",
"Instr... | # bert-base-uncased-sem_eval_2010_task_8
Task: sem_eval_2010_task_8
Base Model: bert-base-uncased
Trained for 3 epochs
Batch-size: 6
Seed: 42
Test F1-Score: 0.8 |
1,152 | jonc/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... |
1,153 | joniponi/bert-finetuned-sem_eval-english | [
"admin",
"aides",
"bathroom",
"bill",
"cc",
"clean",
"communication",
"covid",
"depts",
"doctor",
"family",
"food",
"health",
"nice",
"nurse",
"rude",
"stay",
"visit"
] | ---
Epoch Training Loss Validation Loss F1 Roc Auc Accuracy
1 0.115400 0.099458 0.888763 0.920410 0.731760
2 0.070400 0.080343 0.911700 0.943234 0.781116 |
1,155 | joshuacalloway/csc575finalproject | [
"negative",
"positive",
"noimpact",
"mixed"
] | |
1,157 | jpabbuehl/sagemaker-distilbert-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: sagemaker-distilbert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
... |
1,158 | jpcorb20/toxic-detector-distilroberta | [
"toxic",
"severe_toxic",
"obscene",
"threat",
"insult",
"identity_hate"
] | # Distilroberta for toxic comment detection
See my GitHub repo [toxic-comment-server](https://github.com/jpcorb20/toxic-comment-server)
The model was trained from [DistilRoberta](https://huggingface.co/distilroberta-base) on [Kaggle Toxic Comments](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challeng... |
1,160 | julien-c/distilbert-sagemaker-1609802168 | [
"neg",
"pos"
] |
---
tags:
- sagemaker
datasets:
- imdb
---
## distilbert-sagemaker-1609802168
Trained from SageMaker HuggingFace extension.
Fine-tuned from [distilbert-base-uncased](/distilbert-base-uncased) on [imdb](/datasets/imdb) 🔥
#### Eval
| key | value |
| --- | ----- |
| eval_loss | 0.19187863171100616 |
| eval_accurac... |
1,161 | 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
---
<... |
1,162 | juliensimon/autonlp-imdb-demo-hf-16622767 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- juliensimon/autonlp-data-imdb-demo-hf
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16622767
## Validation Metrics
- Loss: 0.20029613375663757
- Accuracy: 0.9256
- Precision: 0.9090909090909091
- Rec... |
1,163 | juliensimon/autonlp-imdb-demo-hf-16622775 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- juliensimon/autonlp-data-imdb-demo-hf
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16622775
## Validation Metrics
- Loss: 0.18653589487075806
- Accuracy: 0.9408
- Precision: 0.9537643207855974
- Rec... |
1,164 | juliensimon/autonlp-song-lyrics-18753417 | [
"Dance",
"Heavy Metal",
"Hip Hop",
"Indie",
"Pop",
"Rock"
] | ---
tags:
- autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- juliensimon/autonlp-data-song-lyrics
co2_eq_emissions: 112.75546781635975
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18753417
- CO2 Emissions (in grams): 112.75546781635975
## Validation Me... |
1,165 | juliensimon/autonlp-song-lyrics-18753423 | [
"Dance",
"Heavy Metal",
"Hip Hop",
"Indie",
"Pop",
"Rock"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- juliensimon/autonlp-data-song-lyrics
co2_eq_emissions: 55.552987716859484
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18753423
- CO2 Emissions (in grams): 55.552987716859484
## Validation Metri... |
1,167 | junzai/demo | [
"equivalent",
"not_equivalent"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_finetuning_test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:... |
1,168 | junzai/demotest | [
"equivalent",
"not_equivalent"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_finetuning_test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:... |
1,169 | justin871030/bert-base-uncased-goemotions-ekman-finetuned | [
"anger",
"disgust",
"fear",
"joy",
"neutral",
"sadness",
"surprise"
] | ---
language: en
tags:
- go-emotion
- text-classification
- pytorch
datasets:
- go_emotions
metrics:
- f1
widget:
- text: "Thanks for giving advice to the people who need it! 👌🙏"
license: mit
---
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-... |
1,170 | justin871030/bert-base-uncased-goemotions-group-finetuned | [
"ambiguous",
"negative",
"neutral",
"positive"
] | ---
language: en
tags:
- go-emotion
- text-classification
- pytorch
datasets:
- go_emotions
metrics:
- f1
widget:
- text: "Thanks for giving advice to the people who need it! 👌🙏"
license: mit
---
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-... |
1,171 | justin871030/bert-base-uncased-goemotions-original-finetuned | [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"neutral",
"optimism",
"pride"... | ---
language: en
tags:
- go-emotion
- text-classification
- pytorch
datasets:
- go_emotions
metrics:
- f1
widget:
- text: "Thanks for giving advice to the people who need it! 👌🙏"
license: mit
---
## Model Description
1. Based on the uncased BERT pretrained model with a linear output layer.
2. Added several commonly-... |
1,172 | justinqbui/bertweet-covid-vaccine-tweets-finetuned | [
"false",
"misleading",
"true"
] | ---
tags:
model-index:
- name: bertweet-covid--vaccine-tweets-finetuned
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. -->
# bertweet-covid19-base-uncased-pretrain... |
1,173 | jwuthri/autonlp-shipping_status_2-27366103 | [
"0",
"1"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- jwuthri/autonlp-data-shipping_status_2
co2_eq_emissions: 32.912881644048
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 27366103
- CO2 Emissions (in grams): 32.912881644048
## Validation Metrics
- Lo... |
1,174 | jx88/xlm-roberta-base-finetuned-marc-en-j-run | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en-j-run
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, t... |
1,176 | k-partha/curiosity_bert_bio | [
"Sensing",
"Intuitive"
] | Labels Twitter biographies on [Openness](https://en.wikipedia.org/wiki/Openness_to_experience), strongly related to intellectual curiosity.
Intuitive: Associated with higher intellectual curiosity
Sensing: Associated with lower intellectual curiosity
Go to your Twitter profile, copy your biography and paste in... |
1,177 | k-partha/decision_bert_bio | [
"Feeling",
"Thinking"
] | Rates Twitter biographies on decision-making preference: Thinking or Feeling. Roughly corresponds to [agreeableness.](https://en.wikipedia.org/wiki/Agreeableness)
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
Trained on self-described personality lab... |
1,178 | k-partha/decision_style_bert_bio | [
"Prospecting",
"Judging"
] | Rates Twitter biographies on decision-making preference: Judging (focused, goal-oriented decision strategy) or Prospecting (open-ended, explorative strategy). Roughly corresponds to [conscientiousness](https://en.wikipedia.org/wiki/Conscientiousness)
Go to your Twitter profile, copy your biography and paste in the inf... |
1,179 | k-partha/extrabert_bio | [
"Introvert",
"Extravert"
] | Classifies Twitter biographies as either introverts or extroverts.
Go to your Twitter profile, copy your biography and paste in the inference widget, remove any URLs and press hit!
Trained on self-described personality labels. Interpret as a continuous score, not as a discrete label. Have fun!
Barack Obama: Extrove... |
1,180 | kaixinwang/NLP | [
"NEGATIVE",
"POSITIVE"
] | ---
language:
- "Python"
thumbnail: "url to a thumbnail used in social sharing"
tags:
- "sentiment analysis"
- "STEM"
- "text classification"
---
Welcome! This is the model built for the sentiment analysis on the STEM course reviews at UCLA.
- Author: Kaixin Wang
- Email: kaixinwang@g.ucla.edu
- Time Update... |
1,181 | kamivao/autonlp-cola_gram-208681 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- kamivao/autonlp-data-cola_gram
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 208681
## Validation Metrics
- Loss: 0.37569838762283325
- Accuracy: 0.8365019011406845
- Precision: 0.8398058252427184
- ... |
1,182 | kamivao/autonlp-entity_selection-5771228 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- kamivao/autonlp-data-entity_selection
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 5771228
## Validation Metrics
- Loss: 0.17127291858196259
- Accuracy: 0.9206671174216813
- Precision: 0.95888857385... |
1,183 | kangnichaluo/cb | [
"LABEL_0",
"LABEL_1"
] | learning rate: 5e-5
training epochs: 5
batch size: 8
seed: 42
model: bert-base-uncased
trained on CB which is converted into two-way nli classification (predict entailment or not-entailment class) |
1,184 | kangnichaluo/mnli-1 | [
"LABEL_0",
"LABEL_1"
] | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 42
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) |
1,185 | kangnichaluo/mnli-2 | [
"LABEL_0",
"LABEL_1"
] | learning rate: 3e-5
training epochs: 3
batch size: 64
seed: 0
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) |
1,186 | kangnichaluo/mnli-3 | [
"LABEL_0",
"LABEL_1"
] | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 13
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) |
1,187 | kangnichaluo/mnli-4 | [
"LABEL_0",
"LABEL_1"
] | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 87
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) |
1,188 | kangnichaluo/mnli-5 | [
"LABEL_0",
"LABEL_1"
] | learning rate: 2e-5
training epochs: 3
batch size: 64
seed: 111
model: bert-base-uncased
trained on MNLI which is converted into two-way nli classification (predict entailment or not-entailment class) |
1,189 | kangnichaluo/mnli-cb | [
"LABEL_0",
"LABEL_1"
] | learning rate: 3e-5
training epochs: 5
batch size: 8
seed: 42
model: bert-base-uncased
The model is pretrained on MNLI (we use kangnichaluo/mnli-2 directly) and then finetuned on CB which is converted into two-way nli classification (predict entailment or not-entailment class) |
1,190 | kapilchauhan/bert-base-uncased-CoLA-finetuned-cola | [
"unacceptable",
"acceptable"
] | ---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-CoLA-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Mat... |
1,192 | kapilchauhan/distilbert-base-uncased-finetuned-cola | [
"unacceptable",
"acceptable"
] | ---
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... |
1,193 | kco4776/soongsil-bert-wellness | [
"감정",
"내원이유",
"모호함",
"배경",
"부가설명",
"상태",
"원인",
"일반대화",
"자가치료",
"증상",
"치료이력",
"현재상태"
] | ## References
- [Soongsil-BERT](https://github.com/jason9693/Soongsil-BERT) |
1,198 | 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-... |
1,201 | kingla6/distilbert-magazine-classifier | [
"engineering",
"humanities",
"prelaw",
"premed",
"science"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: distilbert-magazine-classifier
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... |
1,202 | kinit/slovakbert-sentiment-twitter | [
"-1",
"0",
"1"
] | ---
language:
- sk
tags:
- twitter
- sentiment-analysis
license: cc
metrics:
- f1
widget:
- text: "Najkrajšia vianočná reklama: Toto milé video vám vykúzli čarovnú atmosféru: Vianoce sa nezadržateľne blížia."
- text: "A opäť sa objavili nebezpečné výrobky. Pozrite sa, či ich nemáte doma"
---
# Sentiment Analysis mod... |
1,203 | kittinan/exercise-feedback-classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | # Reddit exercise feedback classification
Model to classify Reddit's comments for exercise feedback. Current classes are good, correction, bad posture, not informative. If you want to use it locally,
### Usage:
```py
from transformers import pipeline
classifier = pipeline("text-classification", "kittinan/exercise-fee... |
1,204 | kloon99/KML_Software_License_v1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8"
] | {'C0': 'audit_rights',
'C1': 'licensee_indemnity',
'C2': 'licensor_indemnity',
'C3': 'license_grant',
'C4': 'eula_others',
'C5': 'licensee_infringement_indemnity',
'C6': 'licensor_exemption_liability',
'C7': 'licensor_limit_liabilty',
'C8': 'software_warranty'} |
1,205 | kornosk/bert-election2020-twitter-stance-biden-KE-MLM | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (KE-MLM)
Pre-trained weights for **KE-MLM model** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.a... |
1,206 | kornosk/bert-election2020-twitter-stance-biden | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (f-BERT)
Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.... |
1,207 | kornosk/bert-election2020-twitter-stance-trump-KE-MLM | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (KE-MLM)
Pre-trained weights for **KE-MLM model** in [Knowledge Enhance Masked Language Model for Stance Detection](https://ww... |
1,208 | kornosk/bert-election2020-twitter-stance-trump | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (f-BERT)
Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclw... |
1,210 | kurianbenoy/distilbert-base-uncased-finetuned-imdb | [
"neg",
"pos"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
... |
1,211 | kurianbenoy/distilbert-base-uncased-finetuned-sst-2-english-finetuned-imdb | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: ... |
1,212 | 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:... |
1,214 | l3cube-pune/hate-multi-roberta-hasoc-hindi | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
language: hi
tags:
- roberta
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Offensiv... |
1,216 | laboro-ai/distilbert-base-japanese-finetuned-livedoor | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8"
] | ---
language: ja
tags:
- distilbert
license: cc-by-nc-4.0
---
|
1,217 | lamhieu/distilbert-base-multilingual-cased-vietnamese-topicifier | [
"0",
"100 metres",
"A Song of Ice and Fire",
"A Tale for the Time Being",
"ARM Holdings",
"Abigail Johnson",
"Abiogenesis",
"Abortion",
"Abraham Lincoln",
"Abstract art",
"Abu Nuwas",
"Academic degree",
"Accent (sociolinguistics)",
"Achaemenid Empire",
"Acid-base reaction",
"Acoustic g... | ---
language:
- vi
tags:
- vietnamese
- topicifier
- multilingual
- tiny
license:
- mit
pipeline_tag: text-classification
widget:
- text: "Đam mê của tôi là nhiếp ảnh"
---
# distilbert-base-multilingual-cased-vietnamese-topicifier
## About
Fine-tuning from `distilbert-base-multilingual-cased` with a tiny dataset abo... |
1,218 | lannelin/bert-imdb-1hidden | [
"neg",
"pos"
] | ---
language:
- en
datasets:
- imdb
metrics:
- accuracy
---
# bert-imdb-1hidden
## Model description
A `bert-base-uncased` model was restricted to 1 hidden layer and
fine-tuned for sequence classification on the
imdb dataset loaded using the `datasets` library.
## Intended uses & limitations
#### How to use
```... |
1,219 | larskjeldgaard/senda | [
"negativ",
"neutral",
"positiv"
] | ---
language: da
tags:
- danish
- bert
- sentiment
- polarity
license: cc-by-4.0
widget:
- text: "Sikke en dejlig dag det er i dag"
---
# Danish BERT fine-tuned for Sentiment Analysis (Polarity)
This model detects polarity ('positive', 'neutral', 'negative') of danish texts.
It is trained and tested on Tweets annotate... |
1,220 | laurauzcategui/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... |
1,221 | leetdavid/celera_relevance | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: celera_relevance
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. -->
# celera_relevance
... |
1,222 | leetdavid/importance_model | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: importance_model
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. -->
# importance_model
... |
1,223 | leetdavid/market_positivity | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: market_positivity
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. -->
# market_positivit... |
1,224 | leetdavid/market_positivity_model | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: market_positivity_model
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. -->
# market_pos... |
1,225 | leetdavid/relevance-model | [
"LABEL_0"
] | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: relevance-model
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. -->
# relevance-model
T... |
1,227 | lewiswatson/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:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default... |
1,228 | lewtun/distilbert-base-uncased-finetuned-emotion-test-01 | [
"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-test-01
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args:... |
1,229 | lewtun/minilm-finetuned-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
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
... |
1,230 | lewtun/results | [
"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: results
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy... |
1,231 | lewtun/roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi | [
"NEGATIVO",
"POSITIVO"
] | ---
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
... |
1,233 | lewtun/xlm-roberta-base-finetuned-marc-500-samples | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
tags:text-classification
--- |
1,234 | lewtun/xlm-roberta-base-finetuned-marc-de | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-de
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... |
1,235 | lewtun/xlm-roberta-base-finetuned-marc-en-dummy | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en-dummy
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, t... |
1,236 | lewtun/xlm-roberta-base-finetuned-marc-en-hslu | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en-hslu
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, th... |
1,237 | lewtun/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... |
1,238 | lewtun/xlm-roberta-base-finetuned-marc | [
"good",
"great",
"ok",
"poor",
"terrible"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc
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 remov... |
1,239 | lhoestq/distilbert-base-uncased-finetuned-absa-as | [
"NEGATIVE",
"POSITIVE"
] | Distilbert finetuned for Aspect-Based Sentiment Analysis (ABSA) with auxiliary sentence.
```bibtex
@inproceedings{sun-etal-2019-utilizing,
title = "Utilizing {BERT} for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence",
author = "Sun, Chi and
Huang, Luyao and
Qiu, Xipeng",
... |
1,240 | liam168/c2-roberta-base-finetuned-dianping-chinese | [
"negative",
"positive"
] | ---
language: zh
widget:
- text: "我喜欢下雨。"
- text: "我讨厌他。"
---
# liam168/c2-roberta-base-finetuned-dianping-chinese
## Model description
用中文对话情绪语料训练的模型，2分类：乐观和悲观。
## Overview
- **Language model**: BertForSequenceClassification
- **Model size**: 410M
- **Language**: Chinese
## Example
```python
>>> from transform... |
1,241 | liam168/c4-zh-distilbert-base-uncased | [
"Female",
"Sports",
"Literature",
"Campus"
] | ---
language: zh
tags:
- exbert
license: apache-2.0
widget:
- text: "女人做得越纯粹，皮肤和身材就越好"
- text: "我喜欢篮球"
---
# liam168/c4-zh-distilbert-base-uncased
## Model description
用 ["女性","体育","文学","校园"]4类数据训练的分类模型。
## Overview
- **Language model**: DistilBERT
- **Model size**: 280M
- **Language**: Chinese
## Example
```py... |
1,242 | lidiia/autonlp-trans_class_arg-32957902 | [
"0.0",
"1.0"
] | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lidiia/autonlp-data-trans_class_arg
co2_eq_emissions: 0.9756221672668951
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951
## Validation Metrics
-... |
1,243 | lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: en
tags:
- textual-entailment
- nli
- pytorch
datasets:
- mnli
license: mit
widget :
- text: "EpCAM is overexpressed in breast cancer. </s></s> EpCAM is downregulated in breast cancer."
---
# BiomedNLP-PubMedBERT finetuned on textual entailment (NLI)
The [microsoft/BiomedNLP-PubMedBERT-base-uncased-abst... |
1,245 | lighteternal/nli-xlm-r-greek | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- el
- en
tags:
- xlm-roberta-base
datasets:
- multi_nli
- snli
- allnli_greek
metrics:
- accuracy
pipeline_tag: zero-shot-classification
widget:
- text: "Η Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας."
candidate_labels: "τεχνολογία, πολιτική, αθλητισμός... |
1,246 | lincoln/flaubert-mlsum-topic-classification | [
"Culture",
"Economie",
"Education",
"Environement",
"Justice",
"Opinion",
"Politique",
"Societe",
"Sport",
"Technologie"
] | ---
language:
- fr
license: mit
datasets:
- MLSUM
pipeline_tag: "text-classification"
widget:
- text: La bourse de paris en forte baisse après que des canards ont envahit le parlement.
tags:
- text-classification
- flaubert
---
# Classification d'articles de presses avec Flaubert
Ce modèle se base sur le modèl... |
1,248 | lordtt13/emo-mobilebert | [
"angry",
"happy",
"others",
"sad"
] | ---
language: en
datasets:
- emo
---
## Emo-MobileBERT: a thin version of BERT LARGE, trained on the EmoContext Dataset from scratch
### Details of MobileBERT
The **MobileBERT** model was presented in [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by *Zhiqin... |
1,249 | lucasresck/bert-base-cased-ag-news | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
language:
- en
license: mit
tags:
- bert
- classification
datasets:
- ag_news
metrics:
- accuracy
- f1
- recall
- precision
widget:
- text: "Is it soccer or football?"
example_title: "Sports"
- text: "A new version of Ubuntu was released."
example_title: "Sci/Tech"
---
# bert-base-cased-ag-news
BERT model fin... |
1,250 | lucianpopa/autonlp-SST1-529214890 | [
"0",
"1",
"2",
"3",
"4"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lucianpopa/autonlp-data-SST1
co2_eq_emissions: 49.618294309910624
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624
## Validation Metrics
- L... |
1,251 | lucianpopa/autonlp-SST2-551215591 | [
"0",
"1"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lucianpopa/autonlp-data-SST2
co2_eq_emissions: 8.883161797287569
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 551215591
- CO2 Emissions (in grams): 8.883161797287569
## Validation Metrics
- Loss: 0.... |
1,252 | lucianpopa/autonlp-TREC-classification-522314623 | [
"0",
"1",
"2",
"3",
"4",
"5"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lucianpopa/autonlp-data-TREC-classification
co2_eq_emissions: 15.186006626915715
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 522314623
- CO2 Emissions (in grams): 15.186006626915715
## Validati... |
1,253 | luiz826/roberta-to-music-genre | [
"Alternative",
"Country",
"Eletronic Music",
"Gospel and Worship Songs",
"Hip-Hop",
"Jazz/Blues",
"Pop",
"R&B/Soul",
"Reggae",
"Rock"
] | This model was made for a project in the NLP group of the Technology and Artificial Intelligence League (TAIL).
We try to predict a music genre from the lyrics. |
1,254 | lumalik/vent-roberta-emotion | [
"Affection",
"Anger",
"Fear",
"Happiness",
"Sadness"
] | # Vent-roBERTa-emotion
This is a roBERTa pretrained on twitter and then trained for self-labeled emotion classification on the Vent dataset (see https://arxiv.org/abs/1901.04856). The Vent dataset contains 33 million posts annotated with one emotion by the user themselves. <br/>
The model was trained to recognize ... |
1,255 | lvargas/distilbert-base-uncased-finetuned-emotion2 | [
"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-emotion2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: defaul... |
1,257 | lvwerra/distilbert-imdb | [
"NEGATIVE",
"POSITIVE"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
... |
1,258 | lysandre/dum | [
"NEGATIVE",
"POSITIVE"
] | ---
language: en
license: apache-2.0
datasets:
- sst2
tags:
- OpenCLIP
---
# Sentiment Analysis
This is a BERT model fine-tuned for sentiment analysis. |
1,260 | lysandre/new-dummy-model | [
"NEGATIVE",
"POSITIVE"
] | # Dummy model
This is a dummy model. |
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