index int64 0 22.3k | modelId stringlengths 8 111 | label list | readme stringlengths 0 385k |
|---|---|---|---|
1,261 | m-newhauser/distilbert-political-tweets | [
"Democrat",
"Republican"
] | ---
language:
- en
license: lgpl-3.0
library_name: transformers
tags:
- text-classification
- transformers
- pytorch
- generated_from_keras_callback
metrics:
- accuracy
- f1
datasets:
- m-newhauser/senator-tweets
widget:
- text: "This pandemic has shown us clearly the vulgarity of our healthcare system. Highest costs i... |
1,262 | m3hrdadfi/albert-fa-base-v2-clf-digimag | [
"بازی ویدیویی",
"راهنمای خرید",
"سلامت و زیبایی",
"علم و تکنولوژی",
"عمومی",
"هنر و سینما",
"کتاب و ادبیات"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
1,263 | m3hrdadfi/albert-fa-base-v2-clf-persiannews | [
"اجتماعی",
"اقتصادی",
"بین الملل",
"سیاسی",
"علمی فناوری",
"فرهنگی هنری",
"ورزشی",
"پزشکی"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
1,264 | m3hrdadfi/albert-fa-base-v2-sentiment-binary | [
"Negative",
"Positive"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
1,265 | m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-binary | [
"negative",
"positive"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
1,266 | m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-multi | [
"angry",
"delighted",
"furious",
"happy",
"neutral"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
1,267 | m3hrdadfi/albert-fa-base-v2-sentiment-digikala | [
"no_idea",
"not_recommended",
"recommended"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
1,268 | m3hrdadfi/albert-fa-base-v2-sentiment-multi | [
"Negative",
"Neutral",
"Positive"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
1,269 | m3hrdadfi/albert-fa-base-v2-sentiment-snappfood | [
"HAPPY",
"SAD"
] | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
1,270 | m3hrdadfi/bert-fa-base-uncased-farstail | [
"contradiction",
"entailment",
"neutral"
] | ---
language: fa
license: apache-2.0
---
# FarsTail + ParsBERT
Please follow the [FarsTail](https://github.com/dml-qom/FarsTail) repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transfo... |
1,271 | m3hrdadfi/bert-fa-base-uncased-wikinli | [
"contradiction",
"entailment"
] | ---
language: fa
license: apache-2.0
---
# ParsBERT + Sentence Transformers
Please follow the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo for the latest information about previous and current models.
```bibtex
@misc{SentenceTransformerWiki,
author = {Mehrdad Farahani},
title =... |
1,272 | m3hrdadfi/zabanshenas-roberta-base-mix | [
"ace",
"afr",
"als",
"amh",
"ang",
"ara",
"arg",
"arz",
"asm",
"ast",
"ava",
"aym",
"azb",
"aze",
"bak",
"bar",
"bcl",
"be-tarask",
"bel",
"ben",
"bho",
"bjn",
"bod",
"bos",
"bpy",
"bre",
"bul",
"bxr",
"cat",
"cbk",
"cdo",
"ceb",
"ces",
"che",
"chr... | ---
language:
- multilingual
- ace
- afr
- als
- amh
- ang
- ara
- arg
- arz
- asm
- ast
- ava
- aym
- azb
- aze
- bak
- bar
- bcl
- bel
- ben
- bho
- bjn
- bod
- bos
- bpy
- bre
- bul
- bxr
- cat
- cbk
- cdo
- ceb
- ces
- che
- chr
- chv
- ckb
- cor
- cos
- crh
- csb
- cym
- dan
- deu
- diq
- div
- dsb
- dty
- egl
- ... |
1,273 | m3tafl0ps/autonlp-NLPIsFun-251844 | [
"negative",
"positive"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- m3tafl0ps/autonlp-data-NLPIsFun
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 251844
## Validation Metrics
- Loss: 0.38616305589675903
- Accuracy: 0.8356545961002786
- Precision: 0.8253968253968254
-... |
1,274 | madhurjindal/autonlp-Gibberish-Detector-492513457 | [
"clean",
"mild gibberish",
"noise",
"word salad"
] | ---
tags: [autonlp]
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- madhurjindal/autonlp-data-Gibberish-Detector
co2_eq_emissions: 5.527544460835904
---
# Problem Description
The ability to process and understand user input is crucial for various applications, such as chatbots or downstream tasks. However... |
1,275 | madlag/bert-large-uncased-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ## BERT-large finetuned on MNLI.
The [reference finetuned model](https://github.com/google-research/bert) has an accuracy of 86.05, we get 86.7:
```
{'eval_loss': 0.3984006643295288, 'eval_accuracy': 0.8667345899133979}
``` |
1,276 | marcelcastrobr/sagemaker-distilbert-emotion-2 | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: sagemaker-distilbert-emotion-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
... |
1,277 | marcelcastrobr/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:
... |
1,278 | marcolatella/Hps_seed1 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: Hps_seed1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
... |
1,279 | marcolatella/emotion_trained | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1... |
1,280 | marcolatella/emotion_trained_1234567 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- ... |
1,281 | marcolatella/emotion_trained_31415 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- na... |
1,282 | marcolatella/emotion_trained_42 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name:... |
1,288 | marcolatella/prova_Classi2 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: prova_Classi2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1... |
1,289 | marcolatella/tweet_eval_bench | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
model-index:
- name: prova_Classi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- nam... |
1,293 | marma/bert-base-swedish-cased-sentiment | [
"NEGATIVE",
"POSITIVE"
] | Experimental sentiment analysis based on ~20k of App Store reviews in Swedish.
### Usage
```python
from transformers import pipeline
>>> sa = pipeline('sentiment-analysis', model='marma/bert-base-swedish-cased-sentiment')
>>> sa('Det här är ju fantastiskt!')
[{'label': 'POSITIVE', 'score': 0.9974609613418579}]
>>> s... |
1,294 | 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... |
1,295 | masapasa/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:
... |
1,296 | mateocolina/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... |
1,300 | mattmcclean/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... |
1,301 | maximedb/autonlp-vaccinchat-22134694 | [
"chitchat_ask_bye",
"chitchat_ask_hi",
"chitchat_ask_hi_de",
"chitchat_ask_hi_en",
"chitchat_ask_hi_fr",
"chitchat_ask_hoe_gaat_het",
"chitchat_ask_name",
"chitchat_ask_thanks",
"faq_ask_aantal_gevaccineerd",
"faq_ask_aantal_gevaccineerd_wereldwijd",
"faq_ask_afspraak_afzeggen",
"faq_ask_afspr... | ---
tags: autonlp
language: nl
widget:
- text: "I love AutoNLP 🤗"
datasets:
- maximedb/autonlp-data-vaccinchat
co2_eq_emissions: 14.525955245648218
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 22134694
- CO2 Emissions (in grams): 14.525955245648218
## Validation Metrics
... |
1,305 | mazancourt/politics-sentence-classifier | [
"other",
"problem",
"solution"
] | ---
tags: [autonlp, Text Classification, Politics]
language: fr
widget:
- text: "Il y a dans ce pays une fracture"
datasets:
- mazancourt/autonlp-data-politics-sentence-classifier
co2_eq_emissions: 1.06099358268878
---
# Prediction of sentence "nature" in a French political sentence
This model aims at predicting the ... |
1,306 | mdhugol/indonesia-bert-sentiment-classification | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Indonesian BERT Base Sentiment Classifier is a sentiment-text-classification model. The model was originally the pre-trained [IndoBERT Base Model (phase1 - uncased)](https://huggingface.co/indobenchmark/indobert-base-p1) model using [Prosa sentiment dataset](https://github.com/indobenchmark/indonlu/tree/master/dataset/... |
1,307 | mdraw/german-news-sentiment-bert | [
"negative",
"neutral",
"positive"
] | # German sentiment BERT finetuned on news data
Sentiment analysis model based on https://huggingface.co/oliverguhr/german-sentiment-bert, with additional training on German news texts about migration.
This model is part of the project https://github.com/text-analytics-20/news-sentiment-development, which explores sen... |
1,308 | medA/autonlp-FR_another_test-565016091 | [
"BODY_SHAMING",
"HATE",
"HOMOPHOBIA",
"INSULT",
"MISOGYNY",
"MORAL_HARASSMENT",
"NEUTRAL",
"RACISM",
"SEXUAL_HARASSMENT",
"SUPPORTIVE",
"THREAT",
"TROLL"
] | ---
tags: autonlp
language: fr
widget:
- text: "I love AutoNLP 🤗"
datasets:
- medA/autonlp-data-FR_another_test
co2_eq_emissions: 70.54639641012226
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 565016091
- CO2 Emissions (in grams): 70.54639641012226
## Validation Metrics
... |
1,313 | mgrella/autonlp-bank-transaction-classification-5521155 | [
"Category.BILLS_SUBSCRIPTIONS_BILLS",
"Category.BILLS_SUBSCRIPTIONS_INTERNET_PHONE",
"Category.BILLS_SUBSCRIPTIONS_OTHER",
"Category.BILLS_SUBSCRIPTIONS_SUBSCRIPTIONS",
"Category.CREDIT_CARDS_CREDIT_CARDS",
"Category.EATING_OUT_COFFEE_SHOPS",
"Category.EATING_OUT_OTHER",
"Category.EATING_OUT_RESTAURAN... | ---
tags: autonlp
language: it
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mgrella/autonlp-data-bank-transaction-classification
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 5521155
## Validation Metrics
- Loss: 1.3173143863677979
- Accuracy: 0.8220706757594545
- Macro... |
1,321 | microsoft/deberta-base-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... |
1,322 | microsoft/deberta-large-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... |
1,323 | microsoft/deberta-v2-xlarge-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves... |
1,324 | microsoft/deberta-v2-xxlarge-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves... |
1,325 | microsoft/deberta-xlarge-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... |
1,326 | microsoft/tapex-base-finetuned-tabfact | [
"Entailed",
"Refused"
] | ---
language: en
tags:
- tapex
datasets:
- tab_fact
license: mit
---
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original ... |
1,327 | microsoft/tapex-large-finetuned-tabfact | [
"LABEL_0",
"LABEL_1"
] | ---
language: en
tags:
- tapex
- table-question-answering
datasets:
- tab_fact
license: mit
---
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, J... |
1,331 | milyiyo/distilbert-base-uncased-finetuned-amazon-review | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-finetuned-amazon-review
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_... |
1,332 | milyiyo/electra-base-gen-finetuned-amazon-review | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: electra-base-gen-finetuned-amazon-review
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: a... |
1,333 | milyiyo/electra-small-finetuned-amazon-review | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: electra-small-finetuned-amazon-review
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_mu... |
1,334 | milyiyo/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
... |
1,335 | milyiyo/multi-minilm-finetuned-amazon-review | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: multi-minilm-finetuned-amazon-review
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
... |
1,336 | milyiyo/selectra-small-finetuned-amazon-review | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: selectra-small-finetuned-amazon-review
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_m... |
1,338 | ml6team/distilbert-base-dutch-cased-toxic-comments | [
"non-toxic",
"toxic"
] | ---
language:
- nl
tags:
- text-classification
- pytorch
widget:
- text: "Ik heb je lief met heel mijn hart"
example_title: "Non toxic comment 1"
- text: "Dat is een goed punt, zo had ik het nog niet bekeken."
example_title: "Non toxic comment 2"
- text: "Wat de fuck zei je net tegen me, klootzak?"
example_title... |
1,339 | ml6team/distilbert-base-german-cased-toxic-comments | [
"non_toxic",
"toxic"
] | ---
language:
- de
tags:
- distilbert
- german
- classification
datasets:
- germeval21
widget:
- text: "Das ist ein guter Punkt, so hatte ich das noch nicht betrachtet."
example_title: "Agreement (non-toxic)"
- text: "Wow, was ein geiles Spiel. Glückwunsch."
example_title: "Football (non-toxic)"
- text: "Halt deine... |
1,340 | ml6team/robbert-dutch-base-toxic-comments | [
"non-toxic",
"toxic"
] | ---
language:
- nl
tags:
- text-classification
- pytorch
widget:
- text: "Ik heb je lief met heel mijn hart"
example_title: "Non toxic comment 1"
- text: "Dat is een goed punt, zo had ik het nog niet bekeken."
example_title: "Non toxic comment 2"
- text: "Wat de fuck zei je net tegen me, klootzak?"
example_title... |
1,341 | mlkorra/OGBV-gender-bert-hi-en | [
"NGEN",
"GEN"
] | ## BERT Model for OGBV gendered text classification
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mlkorra/OGBV-gender-bert-hi-en")
model = AutoModelForSequenceClassification.from_pretrained("mlkorra/OGBV-gender-bert-h... |
1,342 | mmcquade11/autonlp-imdb-test-21134442 | [
"negative",
"positive"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mmcquade11/autonlp-data-imdb-test
co2_eq_emissions: 298.7849611952843
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134442
- CO2 Emissions (in grams): 298.7849611952843
## Validation Metrics
- Loss... |
1,343 | mmcquade11/autonlp-imdb-test-21134453 | [
"negative",
"positive"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mmcquade11/autonlp-data-imdb-test
co2_eq_emissions: 38.102565360610484
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134453
- CO2 Emissions (in grams): 38.102565360610484
## Validation Metrics
- Lo... |
1,344 | mnaylor/base-bert-finetuned-mtsamples | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | # BERT Base Fine-tuned on MTSamples
This model is [BERT-base](https://huggingface.co/bert-base-uncased) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp).
|
1,346 | mnaylor/bioclinical-bert-finetuned-mtsamples | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | # BioClinical BERT Fine-tuned on MTSamples
This model is simply [Alsentzer's Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp). |
1,350 | mofawzy/bert-arsentd-lev | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language:
- ar
datasets:
- ArSentD-LEV
tags:
- ArSentD-LEV
widget:
- text: "يهدي الله من يشاء"
- text: "الاسلوب قذر وقمامه"
---
# bert-arsentd-lev
Arabic version bert model fine tuned on ArSentD-LEV dataset
## Data
The model were fine-tuned on ~4000 sentence from twitter multiple dialect and five classes w... |
1,354 | morenolq/SumTO_FNS2020 | [
"LABEL_0"
] | This is the *best performing* model used in the paper: "End-to-end Training For Financial Report Summarization"
https://www.aclweb.org/anthology/2020.fnp-1.20/ |
1,355 | moshew/bert-small-aug-sst2-distilled | [
"0",
"1"
] | Accuracy = 92 |
1,356 | moshew/miny-bert-aug-sst2-distilled | [
"0",
"1"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- augmented_glue_sst2
metrics:
- accuracy
model-index:
- name: miny-bert-aug-sst2-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: augmented_glue_sst2
type: augmented_glue_sst2
... |
1,357 | moshew/minylm-L3-aug-sst2-distilled | [
"0",
"1"
] | {'test_accuracy': 0.911697247706422,
'test_loss': 0.24090610444545746,
'test_runtime': 0.4372,
'test_samples_per_second': 1994.475,
'test_steps_per_second': 16.011} |
1,358 | moshew/mpnet-base-sst2-distilled | [
"negative",
"positive"
] | {'test_accuracy': 0.9426605504587156,
'test_loss': 0.1693699210882187,
'test_runtime': 1.7713,
'test_samples_per_second': 492.29,
'test_steps_per_second': 3.952} |
1,360 | moussaKam/frugalscore_medium_bert-base_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,361 | moussaKam/frugalscore_medium_bert-base_mover-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,362 | moussaKam/frugalscore_medium_deberta_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,363 | moussaKam/frugalscore_medium_roberta_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,364 | moussaKam/frugalscore_small_bert-base_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,365 | moussaKam/frugalscore_small_bert-base_mover-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,366 | moussaKam/frugalscore_small_deberta_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,367 | moussaKam/frugalscore_small_roberta_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,368 | moussaKam/frugalscore_tiny_bert-base_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,369 | moussaKam/frugalscore_tiny_bert-base_mover-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,370 | moussaKam/frugalscore_tiny_deberta_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,371 | moussaKam/frugalscore_tiny_roberta_bert-score | [
"LABEL_0"
] | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... |
1,372 | mradau/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: tmpacdj0jf1
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. -->
# tmpacdj0jf1
This model was trained from s... |
1,373 | mradau/stress_score | [
"LABEL_0"
] | ---
tags:
- generated_from_keras_callback
model-index:
- name: tmp10l_qol1
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. -->
# tmp10l_qol1
This model was trained from s... |
1,376 | mrm8488/bert-mini-finetuned-age_news-classification | [
"World",
"Sports",
"Business",
"Sci/Tech"
] | ---
language: en
tags:
- news
- classification
- mini
datasets:
- ag_news
widget:
- text: Israel withdraws from Gaza camp Israel withdraws from Khan Younis refugee
camp in the Gaza Strip, after a four-day operation that left 11 dead.
model-index:
- name: mrm8488/bert-mini-finetuned-age_news-classification
results... |
1,382 | mrm8488/deberta-v3-base-goemotions | [
"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_3",
"LABEL_4",
... | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: deberta-v3-base-goemotions
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. -->
# d... |
1,383 | mrm8488/deberta-v3-large-finetuned-mnli | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
widget:
- text: She was badly wounded already. Another spear would take her down.
model-index:
- name: deberta-v3-large-mnli-2
results:
- task:
type: text-classification
name: Text Classification
data... |
1,384 | mrm8488/deberta-v3-small-finetuned-cola | [
"acceptable",
"unacceptable"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
widget:
- text: They represented seriously to the dean Mary as a genuine linguist.
model-index:
- name: deberta-v3-small
results:
- task:
type: text-classification
name: Text Classification
... |
1,385 | mrm8488/deberta-v3-small-finetuned-mnli | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
model-index:
- name: ds_results
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- na... |
1,386 | mrm8488/deberta-v3-small-finetuned-mrpc | [
"equivalent",
"not_equivalent"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: deberta-v3-small
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metric... |
1,387 | mrm8488/deberta-v3-small-finetuned-qnli | [
"entailment",
"not_entailment"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: deberta-v3-small
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- type: acc... |
1,388 | mrm8488/deberta-v3-small-finetuned-sst2 | [
"negative",
"positive"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
model-index:
- name: deberta-v3-small
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
... |
1,389 | mrm8488/deberta-v3-small-goemotions | [
"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_3",
"LABEL_4",
... | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: deberta-v3-snall-goemotions
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. -->
# ... |
1,391 | mrm8488/distilroberta-finetuned-age_news-classification | [
"World",
"Sports",
"Business",
"Sci/Tech"
] | ---
language: en
tags:
- news
- classification
datasets:
- ag_news
widget:
- text: "Venezuela Prepares for Chavez Recall Vote Supporters and rivals warn of possible fraud; government says Chavez's defeat could produce turmoil in world oil market."
---
# distilroberta-base fine-tuned on age_news dataset for news classi... |
1,392 | mrm8488/distilroberta-finetuned-banking77 | [
"activate_my_card",
"age_limit",
"apple_pay_or_google_pay",
"atm_support",
"automatic_top_up",
"balance_not_updated_after_bank_transfer",
"balance_not_updated_after_cheque_or_cash_deposit",
"beneficiary_not_allowed",
"cancel_transfer",
"card_about_to_expire",
"card_acceptance",
"card_arrival",... | ---
language: en
tags:
- banking
- intent
- multiclass
datasets:
- banking77
widget:
- text: "How long until my transfer goes through?"
---
# distilroberta-base fine-tuned on banking77 dataset for intent classification
Test set accuray: 0.896
## How to use
```py
from transformers import AutoTokenizer, AutoModelForSeq... |
1,393 | mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis | [
"negative",
"neutral",
"positive"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
- financial
- stocks
- sentiment
widget:
- text: "Operating profit totaled EUR 9.4 mn , down from EUR 11.7 mn in 2004 ."
datasets:
- financial_phrasebank
metrics:
- accuracy
model-index:
- name: distilRoberta-financial-sentiment
results:
- task:
name: Tex... |
1,396 | mrm8488/electricidad-base-finetuned-muchocine | [
"1",
"2",
"3",
"4",
"5"
] | ---
language: es
datasets:
- muchocine
widget:
- text: "Una buena película, sin más."
tags:
- sentiment
- analysis
- spanish
---
# Electricidad-base fine-tuned for (Spanish) Sentiment Anlalysis 🎞️👍👎
[Electricidad](https://huggingface.co/mrm8488/electricidad-base-discriminator) base fine-tuned on [muchocine](https... |
1,399 | mrm8488/electricidad-small-finetuned-muchocine | [
"⭐",
"⭐ ⭐",
"⭐ ⭐ ⭐",
"⭐ ⭐ ⭐ ⭐",
"⭐ ⭐ ⭐ ⭐ ⭐"
] | ---
language: es
datasets:
- muchocine
widget:
- text: "Una buena película, sin más."
tags:
- sentiment
- analysis
- spanish
---
# Electricidad-small fine-tuned for (Spanish) Sentiment Anlalysis 🎞️👍👎
[Electricidad](https://huggingface.co/mrm8488/electricidad-small-discriminator) small fine-tuned on [muchocine](ht... |
1,401 | mrm8488/electricidad-small-finetuned-xnli-es | [
"entailment",
"neutral",
"contradiction"
] | ---
language: es
tags:
- spanish
- nli
- xnli
datasets:
- xnli
license: mit
widget:
- text: "Por favor, no piensen en darnos dinero. Por favor, considere piadosamente cuanto puede dar."
---
# electricidad-small-finetuned-xnli-es
|
1,404 | msavel-prnt/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
datasets:
- clinc_oos
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
... |
1,405 | mschwab/va_bert_classification | [
"VA",
"no VA"
] | ---
language:
- en
tags:
- sentence classification
- vossian antonomasia
license: "apache-2.0"
datasets:
- custom
widget:
- text: Bijan wants Jordan to be the Elizabeth Taylor of men's fragrances.
metrics:
- f1
- precision
- recall
---
## English Vossian Antonomasia Sentence Classifier
This page presents a fine-t... |
1,406 | muhtasham/autonlp-Doctor_DE-24595544 | [
"target"
] | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 92.87363201770962
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595544
- CO2 Emissions (in grams): 92.87363201770962
## Validation Metrics
- Lo... |
1,407 | muhtasham/autonlp-Doctor_DE-24595545 | [
"target"
] | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 203.30658367993382
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595545
- CO2 Emissions (in grams): 203.30658367993382
## Validation Metrics
- ... |
1,408 | muhtasham/autonlp-Doctor_DE-24595546 | [
"target"
] | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 210.5957437893554
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595546
- CO2 Emissions (in grams): 210.5957437893554
## Validation Metrics
- Lo... |
1,409 | muhtasham/autonlp-Doctor_DE-24595547 | [
"target"
] | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 396.5529429198159
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595547
- CO2 Emissions (in grams): 396.5529429198159
## Validation Metrics
- Lo... |
1,410 | muhtasham/autonlp-Doctor_DE-24595548 | [
"target"
] | ---
tags: autonlp
language: de
widget:
- text: "I love AutoNLP 🤗"
datasets:
- muhtasham/autonlp-data-Doctor_DE
co2_eq_emissions: 183.88911013564527
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 24595548
- CO2 Emissions (in grams): 183.88911013564527
## Validation Metrics
- ... |
1,411 | mujeensung/albert-base-v2_mnli_bc | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-base-v2_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
... |
1,412 | mujeensung/roberta-base_mnli_bc | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name:... |
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