modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
andi611/distilbert-base-uncased-qa-boolq | [
"False",
"True"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- boolq
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-boolq
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: boolq
type: boolq
args: default
metric:
name: Accuracy
type: accuracy
value: 0.7314984709480122
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-boolq
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2071
- Accuracy: 0.7315
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6506 | 1.0 | 531 | 0.6075 | 0.6681 |
| 0.575 | 2.0 | 1062 | 0.5816 | 0.6978 |
| 0.4397 | 3.0 | 1593 | 0.6137 | 0.7253 |
| 0.2524 | 4.0 | 2124 | 0.8124 | 0.7466 |
| 0.126 | 5.0 | 2655 | 1.1437 | 0.7370 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
| 1,900 |
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 achieved is 0.844494 for a learning rate of 2e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| 1,019 |
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 transformers import AutoModelForSequenceClassification , AutoTokenizer, pipeline
>>> model_name = "liam168/c2-roberta-base-finetuned-dianping-chinese"
>>> class_num = 2
>>> ts_texts = ["我喜欢下雨。", "我讨厌他."]
>>> model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=class_num)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> classifier(ts_texts[0])
>>> classifier(ts_texts[1])
[{'label': 'positive', 'score': 0.9973447918891907}]
[{'label': 'negative', 'score': 0.9972558617591858}]
```
| 921 |
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 modèle [`flaubert/flaubert_base_cased`](https://huggingface.co/flaubert/flaubert_base_cased) et à été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM.
Dans leur papier, les équipes de reciTAL et de la Sorbonne ont proposé comme ouverture de réaliser un modèle de détection de topic sur les articles de presse.
Les topics ont été extrait à partir des URL et nous avons effectué une étape de regroupement de topics pour éliminer ceux avec un trop faible volume et ceux qui paraissaient redondants.
Nous avons finalement utilisé la liste de topics avec les regroupements suivants:
* __Economie__: economie, argent, emploi, entreprises, economie-francaise, immobilier, crise-financiere, evasion-fiscale, economie-mondiale, m-voiture, smart-cities, automobile, logement, flottes-d-entreprise, import, crise-de-l-euro, guide-des-impots, le-club-de-l-economie, telephonie-mobile
* __Opinion__: idees, les-decodeurs, tribunes
* __Politique__: politique, election-presidentielle-2012, election-presidentielle-2017, elections-americaines, municipales, referendum-sur-le-brexit, elections-legislatives-2017, elections-regionales, donald-trump, elections-regionales-2015, europeennes-2014, elections-cantonales-2011, primaire-parti-socialiste, gouvernement-philippe, elections-departementales-2015, chroniques-de-la-presidence-trump, primaire-de-la-gauche, la-republique-en-marche, elections-americaines-mi-mandat-2018, elections, elections-italiennes, elections-senatoriales
* __Societe__: societe, sante, attaques-a-paris, immigration-et-diversite, religions, medecine, francaises-francais, mobilite
* __Culture__: televisions-radio, musiques, festival, arts, scenes, festival-de-cannes, mode, bande-dessinee, architecture, vins, photo, m-mode, fashion-week, les-recettes-du-monde, tele-zapping, critique-litteraire, festival-d-avignon, m-gastronomie-le-lieu, les-enfants-akira, gastronomie, culture, livres, cinema, actualite-medias, blog, m-gastronomie
* __Sport__: sport, football, jeux-olympiques, ligue-1, tennis, coupe-du-monde, mondial-2018, rugby, euro-2016, jeux-olympiques-rio-2016, cyclisme, ligue-des-champions, basket, roland-garros, athletisme, tour-de-france, euro2012, jeux-olympiques-pyeongchang-2018, coupe-du-monde-rugby, formule-1, voile, top-14, ski, handball, sports-mecaniques, sports-de-combat, blog-du-tour-de-france, sport-et-societe, sports-de-glisse, tournoi-des-6-nations
* __Environement__: planete, climat, biodiversite, pollution, energies, cop21
* __Technologie__: pixels, technologies, sciences, cosmos, la-france-connectee, trajectoires-digitales
* __Education__: campus, education, bac-lycee, enseignement-superieur, ecole-primaire-et-secondaire, o21, orientation-scolaire, brevet-college
* __Justice__: police-justice, panama-papers, affaire-penelope-fillon, documents-wikileaks, enquetes, paradise-papers
Les thèmes ayant moins de 100 articles n'ont pas été pris en compte.
Nous avons également mis de côté les articles faisant référence à des topics geographiques, ce qui a donné lieu à un nouveau modèle de classification.
Après nettoyage, la base MLSUM a été réduite à 293 995 articles. Le corps d'un article en moyenne comporte 694 tokens.
Nous avons entrainé le modèle sur 20% de la base nettoyée. En moyenne, le nombre d'articles par classe est de ~4K.
## Entrainement
Nous avons benchmarké différents modèles en les entrainant sur différentes parties des articles (titre, résumé, corps et titre+résumé) et avec des échantillons d'apprentissage de tailles différentes.
Les modèles ont été entrainé sur le cloud Azure avec des Tesla V100.
## Modèle
Le modèle partagé sur HF est le modéle qui prend en entrée le corps d'un article. Nous l'avons entrainé sur 20% du jeu de donnée nettoyé.
## Résulats
*Les lignes correspondent aux labels prédits et les colonnes aux véritables topics. Les pourcentages sont calculés sur les colonnes.*
_Nous garantissons pas les résultats sur le long terme. Modèle réalisé dans le cadre d'un POC._
## Utilisation
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TextClassificationPipeline
model_name = 'lincoln/flaubert-mlsum-topic-classification'
loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name)
nlp = TextClassificationPipeline(model=loaded_model, tokenizer=loaded_tokenizer)
nlp("Le Bayern Munich prend la grenadine.", truncation=True)
```
## Citation
```bibtex
@article{scialom2020mlsum,
title={MLSUM: The Multilingual Summarization Corpus},
author={Thomas Scialom and Paul-Alexis Dray and Sylvain Lamprier and Benjamin Piwowarski and Jacopo Staiano},
year={2020},
eprint={2004.14900},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 5,264 |
uer/roberta-base-finetuned-ifeng-chinese | [
"International news",
"Taiwan - Hong Kong - Macau politics",
"mainland China politics",
"military news",
"society news"
] | ---
language: zh
widget:
- text: "这本书真的很不错"
---
# Chinese RoBERTa-Base Models for Text Classification
## Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://arxiv.org/abs/1909.05658). You can download the 5 Chinese RoBERTa-Base classification models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo) (in UER-py format), or via HuggingFace from the links below:
| Dataset | Link |
| :-----------: | :-------------------------------------------------------: |
| **JD full** | [**roberta-base-finetuned-jd-full-chinese**][jd_full] |
| **JD binary** | [**roberta-base-finetuned-jd-binary-chinese**][jd_binary] |
| **Dianping** | [**roberta-base-finetuned-dianping-chinese**][dianping] |
| **Ifeng** | [**roberta-base-finetuned-ifeng-chinese**][ifeng] |
| **Chinanews** | [**roberta-base-finetuned-chinanews-chinese**][chinanews] |
## How to use
You can use this model directly with a pipeline for text classification (take the case of roberta-base-finetuned-chinanews-chinese):
```python
>>> from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline
>>> model = AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')
>>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')
>>> text_classification = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> text_classification("北京上个月召开了两会")
[{'label': 'mainland China politics', 'score': 0.7211663722991943}]
```
## Training data
5 Chinese text classification datasets are used. JD full, JD binary, and Dianping datasets consist of user reviews of different sentiment polarities. Ifeng and Chinanews consist of first paragraphs of news articles of different topic classes. They are collected by [Glyph](https://github.com/zhangxiangxiao/glyph) project and more details are discussed in corresponding [paper](https://arxiv.org/abs/1708.02657).
## Training procedure
Models are fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune three epochs with a sequence length of 512 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved. We use the same hyper-parameters on different models.
Taking the case of roberta-base-finetuned-chinanews-chinese
```
python3 run_classifier.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
--vocab_path models/google_zh_vocab.txt \
--train_path datasets/glyph/chinanews/train.tsv \
--dev_path datasets/glyph/chinanews/dev.tsv \
--output_model_path models/chinanews_classifier_model.bin \
--learning_rate 3e-5 --epochs_num 3 --batch_size 32 --seq_length 512
```
Finally, we convert the pre-trained model into Huggingface's format:
```
python3 scripts/convert_bert_text_classification_from_uer_to_huggingface.py --input_model_path models/chinanews_classifier_model.bin \
--output_model_path pytorch_model.bin \
--layers_num 12
```
### BibTeX entry and citation info
```
@article{devlin2018bert,
title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
journal={arXiv preprint arXiv:1810.04805},
year={2018}
}
@article{liu2019roberta,
title={Roberta: A robustly optimized bert pretraining approach},
author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
journal={arXiv preprint arXiv:1907.11692},
year={2019}
}
@article{zhang2017encoding,
title={Which encoding is the best for text classification in chinese, english, japanese and korean?},
author={Zhang, Xiang and LeCun, Yann},
journal={arXiv preprint arXiv:1708.02657},
year={2017}
}
@article{zhao2019uer,
title={UER: An Open-Source Toolkit for Pre-training Models},
author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
journal={EMNLP-IJCNLP 2019},
pages={241},
year={2019}
}
```
[jd_full]:https://huggingface.co/uer/roberta-base-finetuned-jd-full-chinese
[jd_binary]:https://huggingface.co/uer/roberta-base-finetuned-jd-binary-chinese
[dianping]:https://huggingface.co/uer/roberta-base-finetuned-dianping-chinese
[ifeng]:https://huggingface.co/uer/roberta-base-finetuned-ifeng-chinese
[chinanews]:https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese | 5,141 |
Toshifumi/bert-base-multilingual-cased-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: bert-base-multilingual-cased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9195
- name: F1
type: f1
value: 0.9204823251325381
---
<!-- 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-base-multilingual-cased-finetuned-emotion
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2369
- Accuracy: 0.9195
- F1: 0.9205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9212 | 1.0 | 250 | 0.3466 | 0.8965 | 0.8966 |
| 0.2893 | 2.0 | 500 | 0.2369 | 0.9195 | 0.9205 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1,827 |
Supreeth/Toxic-XLM_RoBERTa | null | ---
license: afl-3.0
---
| 25 |
ArnavL/roberta-base-imdb-0 | null | Entry not found | 15 |
Jorgeutd/sagemaker-roberta-base-emotion | [
"anger",
"fear",
"joy",
"love",
"sadness",
"surprise"
] |
---
language: en
widget:
- text: "I am really upset that I have to call up to three times to the number on the back of my insurance card for my call to be answer"
tags:
- sagemaker
- roberta-base
- text classification
license: apache-2.0
datasets:
- emotion
model-index:
- name: sagemaker-roberta-base-emotion
results:
- task:
name: Multi Class Text Classification
type: text-classification
dataset:
name: "emotion"
type: emotion
metrics:
- name: Validation Accuracy
type: accuracy
value: 94.1
- name: Validation F1
type: f1
value: 94.13
---
## roberta-base
This model is a fine-tuned model that was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
- Problem type: Multi Class Text Classification (emotion detection).
It achieves the following results on the evaluation set:
- Loss: 0.1613253802061081
- f1: 0.9413321705151999
## Hyperparameters
```json
{
"epochs": 10,
"train_batch_size": 16,
"learning_rate": 3e-5,
"weight_decay":0.01,
"load_best_model_at_end": true,
"model_name":"roberta-base",
"do_eval": True,
"load_best_model_at_end":True
}
```
## Validation Metrics
| key | value |
| --- | ----- |
| eval_accuracy | 0.941 |
| eval_f1 | 0.9413321705151999 |
| eval_loss | 0.1613253802061081|
| eval_recall | 0.941 |
| eval_precision | 0.9419519436781406 |
| 1,425 |
SimonZvara/Memes-CS_1.0 | [
"LABEL_0"
] | Model used by Memes-CS (Metric for Evaluating Model Efficiency in Summarization).
Part of my bachelor's thesis.
Šimon Zvára | 125 |
waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-rua_wl_3_classes | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 72.4 | 72.2 |
| test | 72.8 | 72.5 | | 367 |
Anupama/distilbert-base-uncased-finetuned-emotion | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.922
- name: F1
type: f1
value: 0.9219009840141562
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2226
- Accuracy: 0.922
- F1: 0.9219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8435 | 1.0 | 250 | 0.3324 | 0.897 | 0.8930 |
| 0.2578 | 2.0 | 500 | 0.2226 | 0.922 | 0.9219 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,804 |
textattack/distilbert-base-uncased-MRPC | null | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8578431372549019, as measured by the
eval set accuracy, found after 1 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
| 628 |
Jackett/subject_classifier_extended | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5"
] | Label mappings
{'LABEL_0':'Biology','LABEL_1':'Physics','LABEL_2':'Chemistry','LABEL_3':'Maths','LABEL_4':'Social Science','LABEL_5':'English'}
Training data distribution
Physics - 7000
Maths - 7000
Biology - 7000
Chemistry - 7000
English - 5254
Social Science - 7000 | 270 |
arghya007/roberta-scarcasm-discriminator | null | Entry not found | 15 |
BNZSA/distilbert-base-uncased-country-NER-address | [
"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_10013",
"LABEL_10014",
"LABEL_10015",
"LABEL_10016",
"LABEL_10017",
"LABEL_10018",
"LABEL_10019",
"LABEL_1002",
"LABEL_10020",
"LABEL_10021",
"LABEL_10022",
"LABEL_10023",
"LABEL_10024",
"LABEL_10025",
"LABEL_10026",
"LABEL_10027",
"LABEL_10028",
"LABEL_10029",
"LABEL_1003",
"LABEL_10030",
"LABEL_10031",
"LABEL_10032",
"LABEL_10033",
"LABEL_10034",
"LABEL_10035",
"LABEL_10036",
"LABEL_10037",
"LABEL_10038",
"LABEL_10039",
"LABEL_1004",
"LABEL_10040",
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] | ---
license: gpl-3.0
---
| 25 |
M-FAC/bert-mini-finetuned-mrpc | null | # BERT-mini model finetuned with M-FAC
This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC.
Hyperparameters used by M-FAC optimizer:
```bash
learning rate = 1e-4
number of gradients = 512
dampening = 1e-6
```
## Results
We share the best model out of 5 runs with the following score on MRPC validation set:
```bash
f1 = 86.51
accuracy = 81.12
```
Mean and standard deviation for 5 runs on MRPC validation set:
| | F1 | Accuracy |
|:----:|:-----------:|:----------:|
| Adam | 84.57 ± 0.36| 76.57 ± 0.80|
| M-FAC | 85.06 ± 1.63 | 78.87 ± 2.33 |
Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script:
```bash
CUDA_VISIBLE_DEVICES=0 python run_glue.py \
--seed 1234 \
--model_name_or_path prajjwal1/bert-mini \
--task_name mrpc \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 1e-4 \
--num_train_epochs 5 \
--output_dir out_dir/ \
--optim MFAC \
--optim_args '{"lr": 1e-4, "num_grads": 512, "damp": 1e-6}'
```
We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE).
Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC).
A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials).
## BibTeX entry and citation info
```bibtex
@article{frantar2021m,
title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information},
author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan},
journal={Advances in Neural Information Processing Systems},
volume={35},
year={2021}
}
```
| 2,785 |
ZiweiG/ziwei-bertimdb-prob | null | Entry not found | 15 |
nickmuchi/deberta-v3-base-finetuned-finance-text-classification | [
"bearish",
"bullish",
"neutral"
] | ---
license: mit
tags:
- generated_from_trainer
- financial-sentiment-analysis
- sentiment-analysis
- sentence_50agree
- financial
- stocks
- sentiment
datasets:
- financial_phrasebank
- Kaggle Self label
- nickmuchi/financial-classification
widget:
- text: "The USD rallied by 3% last night as the Fed hiked interest rates"
example_title: "Bullish Sentiment"
- text: "Covid-19 cases have been increasing over the past few months impacting earnings for global firms"
example_title: "Bearish Sentiment"
- text: "the USD has been trending lower"
example_title: "Mildly Bearish Sentiment"
- text: "The USD rallied by 3% last night as the Fed hiked interest rates however, higher interest rates will increase mortgage costs for homeowners"
example_title: "Neutral"
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-finetuned-finance-text-classification
results: []
---
# deberta-v3-base-finetuned-finance-text-classification
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: [sentiment-classification-selflabel-dataset](https://www.kaggle.com/percyzheng/sentiment-classification-selflabel-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.7687
- Accuracy: 0.8913
- F1: 0.8912
- Precision: 0.8927
- Recall: 0.8913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 285 | 0.4187 | 0.8399 | 0.8407 | 0.8687 | 0.8399 |
| 0.5002 | 2.0 | 570 | 0.3065 | 0.8755 | 0.8733 | 0.8781 | 0.8755 |
| 0.5002 | 3.0 | 855 | 0.4148 | 0.8775 | 0.8775 | 0.8778 | 0.8775 |
| 0.1937 | 4.0 | 1140 | 0.4249 | 0.8696 | 0.8699 | 0.8719 | 0.8696 |
| 0.1937 | 5.0 | 1425 | 0.5121 | 0.8834 | 0.8824 | 0.8831 | 0.8834 |
| 0.0917 | 6.0 | 1710 | 0.6113 | 0.8775 | 0.8779 | 0.8839 | 0.8775 |
| 0.0917 | 7.0 | 1995 | 0.7296 | 0.8775 | 0.8776 | 0.8793 | 0.8775 |
| 0.0473 | 8.0 | 2280 | 0.7034 | 0.8953 | 0.8942 | 0.8964 | 0.8953 |
| 0.0275 | 9.0 | 2565 | 0.6995 | 0.8834 | 0.8836 | 0.8846 | 0.8834 |
| 0.0275 | 10.0 | 2850 | 0.7736 | 0.8755 | 0.8755 | 0.8789 | 0.8755 |
| 0.0186 | 11.0 | 3135 | 0.7173 | 0.8814 | 0.8814 | 0.8840 | 0.8814 |
| 0.0186 | 12.0 | 3420 | 0.7659 | 0.8854 | 0.8852 | 0.8873 | 0.8854 |
| 0.0113 | 13.0 | 3705 | 0.8415 | 0.8854 | 0.8855 | 0.8907 | 0.8854 |
| 0.0113 | 14.0 | 3990 | 0.7577 | 0.8953 | 0.8951 | 0.8966 | 0.8953 |
| 0.0074 | 15.0 | 4275 | 0.7687 | 0.8913 | 0.8912 | 0.8927 | 0.8913 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 3,846 |
ZiweiG/ziwei-bert-imdb | null | Entry not found | 15 |
cardiffnlp/bertweet-base-emoji | [
"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_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | 0 | |
kssteven/ibert-roberta-large-mnli | [
"CONTRADICTION",
"NEUTRAL",
"ENTAILMENT"
] | Entry not found | 15 |
philschmid/tiny-distilbert-classification | [
"NEGATIVE",
"POSITIVE"
] | # Test model
> ## This model is used to run tests for the Hugging Face DLCs | 77 |
IsaacRodgz/Tamil-Hate-Speech | [
"Hate-Speech",
"Non-Hate-Speech"
] | Entry not found | 15 |
Farshid/distilbert-base-uncased_allagree3 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased_allagree3
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
args: sentences_allagree
metrics:
- name: Accuracy
type: accuracy
value: 0.9778761061946902
- name: F1
type: f1
value: 0.9780006392634297
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_allagree3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0937
- Accuracy: 0.9779
- F1: 0.9780
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6418 | 1.0 | 57 | 0.3340 | 0.8805 | 0.8768 |
| 0.1821 | 2.0 | 114 | 0.1088 | 0.9690 | 0.9691 |
| 0.0795 | 3.0 | 171 | 0.0822 | 0.9823 | 0.9823 |
| 0.0385 | 4.0 | 228 | 0.0939 | 0.9646 | 0.9646 |
| 0.0218 | 5.0 | 285 | 0.1151 | 0.9735 | 0.9737 |
| 0.0149 | 6.0 | 342 | 0.1126 | 0.9690 | 0.9694 |
| 0.006 | 7.0 | 399 | 0.0989 | 0.9779 | 0.9780 |
| 0.0093 | 8.0 | 456 | 0.1009 | 0.9779 | 0.9780 |
| 0.0063 | 9.0 | 513 | 0.0899 | 0.9779 | 0.9780 |
| 0.0039 | 10.0 | 570 | 0.0937 | 0.9779 | 0.9780 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.3.2
- Tokenizers 0.12.1
| 2,432 |
AhmedBou/TuniBert | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Luyu/bert-base-mdoc-hdct | [
"LABEL_0"
] | ---
language:
- en
tags:
- text reranking
license: apache-2.0
datasets:
- MS MARCO document ranking
---
# BERT Reranker for MS-MARCO Document Ranking
## Model description
A text reranker trained for HDCT retriever on MS MARCO document dataset.
## Intended uses & limitations
It is possible to work with other retrievers like BM25 but using aligned HDCT works the best.
#### How to use
See our [project repo page](https://github.com/luyug/Reranker).
## Eval results
MRR @10: 0.434 on Dev.
MRR @10: 0.382 on Eval.
### BibTeX entry and citation info
```bibtex
@inproceedings{gao2021lce,
title={Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline},
author={Luyu Gao and Zhuyun Dai and Jamie Callan},
year={2021},
booktitle={The 43rd European Conference On Information Retrieval (ECIR)},
}
``` | 881 |
Narrativa/distilroberta-finetuned-stereotype-detection | [
"neutral",
"stereotype",
"anti-stereotype"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
- stereotype
- gender
- gender_bias
widget:
- text: "Cauterize is not just for fans of the guitarist or his other projects, but those that love music that is both aggressive and infectious and gave the album 4 out of 5 stars ."
metrics:
- accuracy
model-index:
- name: distilRoberta-stereotype
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.989151002901476
---
<!-- 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. -->
# distilRoberta-stereotype
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0651
- Accuracy: 0.9892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0783 | 1.0 | 5615 | 0.0703 | 0.9847 |
| 0.0468 | 2.0 | 11230 | 0.0573 | 0.9863 |
| 0.0316 | 3.0 | 16845 | 0.0580 | 0.9882 |
| 0.0172 | 4.0 | 22460 | 0.0591 | 0.9885 |
| 0.0098 | 5.0 | 28075 | 0.0651 | 0.9892 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
Created by: [Narrativa](https://www.narrativa.com/)
About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI | 2,194 |
Prompsit/paraphrase-bert-en | [
"Not Paraphrase",
"Paraphrase"
] | ---
pipeline_tag: text-classification
inference: false
language: en
tags:
- transformers
---
# Prompsit/paraphrase-bert-en
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "bert-base-uncased".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government of Spain.
# How to use it
The model answer the following question: Is "phrase B" a paraphrase of "phrase A".
Please note that we're considering phrases instead of sentences. Therefore, we must take into account that the model doesn't expect to find punctuation marks or long pieces of text.
Resulting probabilities correspond to classes:
* 0: Not a paraphrase
* 1: It's a paraphrase
So, considering the phrase "may be addressed" and a candidate paraphrase like "could be included", you can use the model like this:
```
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Prompsit/paraphrase-bert-en")
model = AutoModelForSequenceClassification.from_pretrained("Prompsit/paraphrase-bert-en")
input = tokenizer('may be addressed','could be included',return_tensors='pt')
logits = model(**input).logits
soft = torch.nn.Softmax(dim=1)
print(soft(logits))
```
Code output is:
```
tensor([[0.1592, 0.8408]], grad_fn=<SoftmaxBackward>)
```
As the probability of 1 (=It's a paraphrase) is 0.84 and the probability of 0 (=It is not a paraphrase) is 0.15, we can conclude, for our previous example, that "could be included" is a paraphrase of "may be addressed".
# Evaluation results
We have used as test dataset 16500 pairs of phrases human tagged.
Metrics obtained are:
```
metrics={
'test_loss': 0.5660144090652466,
'test_accuracy': 0.8170742794799527,
'test_precision': 0.7043977055449331,
'test_recall': 0.5978578383641675,
'test_f1': 0.6467696629213483,
'test_matthews_correlation': 0.5276716223607356,
'test_runtime': 19.3345,
'test_samples_per_second': 568.88,
'test_steps_per_second': 17.792
}
``` | 2,114 |
transformersbook/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_declined",
"carry_on",
"change_accent",
"change_ai_name",
"change_language",
"change_speed",
"change_user_name",
"change_volume",
"confirm_reservation",
"cook_time",
"credit_limit",
"credit_limit_change",
"credit_score",
"current_location",
"damaged_card",
"date",
"definition",
"direct_deposit",
"directions",
"distance",
"do_you_have_pets",
"exchange_rate",
"expiration_date",
"find_phone",
"flight_status",
"flip_coin",
"food_last",
"freeze_account",
"fun_fact",
"gas",
"gas_type",
"goodbye",
"greeting",
"how_busy",
"how_old_are_you",
"improve_credit_score",
"income",
"ingredient_substitution",
"ingredients_list",
"insurance",
"insurance_change",
"interest_rate",
"international_fees",
"international_visa",
"jump_start",
"last_maintenance",
"lost_luggage",
"make_call",
"maybe",
"meal_suggestion",
"meaning_of_life",
"measurement_conversion",
"meeting_schedule",
"min_payment",
"mpg",
"new_card",
"next_holiday",
"next_song",
"no",
"nutrition_info",
"oil_change_how",
"oil_change_when",
"oos",
"order",
"order_checks",
"order_status",
"pay_bill",
"payday",
"pin_change",
"play_music",
"plug_type",
"pto_balance",
"pto_request",
"pto_request_status",
"pto_used",
"recipe",
"redeem_rewards",
"reminder",
"reminder_update",
"repeat",
"replacement_card_duration",
"report_fraud",
"report_lost_card",
"reset_settings",
"restaurant_reservation",
"restaurant_reviews",
"restaurant_suggestion",
"rewards_balance",
"roll_dice",
"rollover_401k",
"routing",
"schedule_maintenance",
"schedule_meeting",
"share_location",
"shopping_list",
"shopping_list_update",
"smart_home",
"spelling",
"spending_history",
"sync_device",
"taxes",
"tell_joke",
"text",
"thank_you",
"time",
"timer",
"timezone",
"tire_change",
"tire_pressure",
"todo_list",
"todo_list_update",
"traffic",
"transactions",
"transfer",
"translate",
"travel_alert",
"travel_notification",
"travel_suggestion",
"uber",
"update_playlist",
"user_name",
"vaccines",
"w2",
"weather",
"what_are_your_hobbies",
"what_can_i_ask_you",
"what_is_your_name",
"what_song",
"where_are_you_from",
"whisper_mode",
"who_do_you_work_for",
"who_made_you",
"yes"
] | ---
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
metrics:
- name: Accuracy
type: accuracy
value: 0.9174193548387096
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb).
It achieves the following results on the evaluation set:
- Loss: 0.7773
- Accuracy: 0.9174
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2923 | 1.0 | 318 | 3.2893 | 0.7423 |
| 2.6307 | 2.0 | 636 | 1.8837 | 0.8281 |
| 1.5483 | 3.0 | 954 | 1.1583 | 0.8968 |
| 1.0153 | 4.0 | 1272 | 0.8618 | 0.9094 |
| 0.7958 | 5.0 | 1590 | 0.7773 | 0.9174 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.0
- Tokenizers 0.10.3
| 2,246 |
MoritzLaurer/covid-policy-roberta-21 | [
"Anti-Disinformation Measures",
"COVID-19 Vaccines",
"Closure and Regulation of Schools",
"Curfew",
"Declaration of Emergency",
"External Border Restrictions",
"Health Monitoring",
"Health Resources",
"Health Testing",
"Hygiene",
"Internal Border Restrictions",
"Lockdown",
"New Task Force, Bureau or Administrative Configuration",
"Other",
"Other Policy Not Listed Above",
"Public Awareness Measures",
"Quarantine",
"Restriction and Regulation of Businesses",
"Restriction and Regulation of Government Services",
"Restrictions of Mass Gatherings",
"Social Distancing"
] | ---
language:
- en
tags:
- text-classification
metrics:
- accuracy (balanced)
- F1 (weighted)
widget:
- text: "All non-essential work activity will stop in Spain from tomorrow until 9 April but there is some confusion as to which jobs can continue under the new lockdown restrictions"
---
# Covid-Policy-RoBERTa-21
This model is currently in development at the Centre for European Policy Studies (CEPS).
The model is not yet recommended for use. A more detailed description will follow.
If you are interested in using deep learning to identify 20 different types policy measures against COVID-19 in text (NPIs, "non-pharmaceutical interventions") don't hesitate to [contact me](https://www.ceps.eu/ceps-staff/moritz-laurer/). | 729 |
totoro4007/cryptoroberta-base-finetuned | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
adamnik/bert-entailment-detection | null | ---
license: mit
---
| 21 |
monologg/koelectra-base-finetuned-nsmc | [
"negative",
"positive"
] | Entry not found | 15 |
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 and the best validation score achieved is 0.723254 for a learning rate of 2e-5. Training code can be found at this [url](https://github.com/punyajoy/DE-LIMIT)
### For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
~~~
| 1,057 |
hf-internal-testing/tiny-plbart | null | Entry not found | 15 |
DLochmelis33/22s-dl-sentiment-1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: 22s-dl-sentiment-1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.9542333333333334
---
<!-- 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. -->
# 22s-dl-sentiment-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2574
- Accuracy: 0.9542
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
| 1,453 |
PGT/graphnystromformer-artificial-balanced-max500-490000-0 | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | Entry not found | 15 |
boychaboy/SNLI_bert-large-uncased | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
edwardgowsmith/pt-finegrained-few-shot | null | Entry not found | 15 |
lewtun/bert-large-uncased-wwm-finetuned-boolq | null | Entry not found | 15 |
pedropei/aspect-level-certainty | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
prajjwal1/albert-base-v2-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli). | 479 |
navteca/ms-marco-MiniLM-L-12-v2 | [
"LABEL_0"
] | ---
language: en
license: mit
pipeline_tag: text-classification
tags:
- sentence-transformers
---
# Cross-Encoder for MS Marco
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Training Data
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
## Usage
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
| 2,597 |
James-kc-min/AGT_Roberta2 | null | Hugging Face's logo
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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: AGT_Roberta
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. -->
# AGT_Roberta
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.0
- Tokenizers 0.12.1
| 1,584 |
RogerKam/roberta_fine_tuned_sentiment_newsmtsc | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_fine_tuned_sentiment_newsmtsc
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. -->
# roberta_fine_tuned_sentiment_newsmtsc
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6134
- Accuracy: 0.7713
- F1 Score: 0.7710
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.0+cu111
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,180 |
amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061 | [
"negative",
"neutral",
"positive"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- amansolanki/autonlp-data-Tweet-Sentiment-Extraction
co2_eq_emissions: 3.651199395353127
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 20114061
- CO2 Emissions (in grams): 3.651199395353127
## Validation Metrics
- Loss: 0.5046541690826416
- Accuracy: 0.8036219581211093
- Macro F1: 0.807095210403678
- Micro F1: 0.8036219581211093
- Weighted F1: 0.8039634739225368
- Macro Precision: 0.8076842795233988
- Micro Precision: 0.8036219581211093
- Weighted Precision: 0.8052135235094771
- Macro Recall: 0.8075241470527056
- Micro Recall: 0.8036219581211093
- Weighted Recall: 0.8036219581211093
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,447 |
fabriceyhc/bert-base-uncased-ag_news | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: bert-base-uncased-ag_news
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
<!-- 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-base-uncased-ag_news
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3284
- Accuracy: 0.9375
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 7425
- training_steps: 74250
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5773 | 0.13 | 2000 | 0.3627 | 0.8875 |
| 0.3101 | 0.27 | 4000 | 0.2938 | 0.9208 |
| 0.3076 | 0.4 | 6000 | 0.3114 | 0.9092 |
| 0.3114 | 0.54 | 8000 | 0.4545 | 0.9008 |
| 0.3154 | 0.67 | 10000 | 0.3875 | 0.9083 |
| 0.3095 | 0.81 | 12000 | 0.3390 | 0.9142 |
| 0.2948 | 0.94 | 14000 | 0.3341 | 0.9133 |
| 0.2557 | 1.08 | 16000 | 0.4573 | 0.9092 |
| 0.258 | 1.21 | 18000 | 0.3356 | 0.9217 |
| 0.2455 | 1.35 | 20000 | 0.3348 | 0.9283 |
| 0.2361 | 1.48 | 22000 | 0.3218 | 0.93 |
| 0.254 | 1.62 | 24000 | 0.3814 | 0.9033 |
| 0.2528 | 1.75 | 26000 | 0.3628 | 0.9158 |
| 0.2282 | 1.89 | 28000 | 0.3302 | 0.9308 |
| 0.224 | 2.02 | 30000 | 0.3967 | 0.9225 |
| 0.174 | 2.15 | 32000 | 0.3669 | 0.9333 |
| 0.1848 | 2.29 | 34000 | 0.3435 | 0.9283 |
| 0.19 | 2.42 | 36000 | 0.3552 | 0.93 |
| 0.1865 | 2.56 | 38000 | 0.3996 | 0.9258 |
| 0.1877 | 2.69 | 40000 | 0.3749 | 0.9258 |
| 0.1951 | 2.83 | 42000 | 0.3963 | 0.9258 |
| 0.1702 | 2.96 | 44000 | 0.3655 | 0.9317 |
| 0.1488 | 3.1 | 46000 | 0.3942 | 0.9292 |
| 0.1231 | 3.23 | 48000 | 0.3998 | 0.9267 |
| 0.1319 | 3.37 | 50000 | 0.4292 | 0.9242 |
| 0.1334 | 3.5 | 52000 | 0.4904 | 0.9192 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
| 3,204 |
projecte-aina/roberta-base-ca-cased-sts | [
"SIMILARITY"
] | ---
language:
- ca
pipeline_tag: text-classification
license: apache-2.0
tags:
- "catalan"
- "semantic textual similarity"
- "sts-ca"
- "CaText"
- "Catalan Textual Corpus"
datasets:
- "projecte-aina/sts-ca"
metrics:
- "pearson"
model-index:
- name: roberta-base-ca-cased-sts
results:
- task:
type: text-classification
dataset:
type: projecte-aina/sts-ca
name: sts-ca
metrics:
- type: pearson
value: 0.7973
---
# Catalan BERTa (RoBERTa-base) finetuned for Semantic Textual Similarity.
The **roberta-base-ca-cased-sts** is a Semantic Textual Similarity (STS) model for the Catalan language fine-tuned from the [BERTa](https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).
## Datasets
We used the STS dataset in Catalan called [STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) for training and evaluation.
## Evaluation and results
We evaluated the _roberta-base-ca-cased-sts_ on the STS-ca test set against standard multilingual and monolingual baselines:
| Model | STS-ca (Pearson) |
|:------------|:----|
| roberta-base-ca-cased-sts | **79.73** |
| mBERT | 76.34 |
| XLM-RoBERTa | 75.40 |
| WikiBERT-ca | 77.18 |
For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
## How to use
To get the correct<sup>1</sup> model's prediction scores with values between 0.0 and 5.0, use the following code:
```python
from transformers import pipeline, AutoTokenizer
from scipy.special import logit
model = 'projecte-aina/roberta-base-ca-cased-sts'
tokenizer = AutoTokenizer.from_pretrained(model)
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer)
def prepare(sentence_pairs):
sentence_pairs_prep = []
for s1, s2 in sentence_pairs:
sentence_pairs_prep.append(f"{tokenizer.cls_token} {s1}{tokenizer.sep_token}{tokenizer.sep_token} {s2}{tokenizer.sep_token}")
return sentence_pairs_prep
sentence_pairs = [("El llibre va caure per la finestra.", "El llibre va sortir volant."),
("M'agrades.", "T'estimo."),
("M'agrada el sol i la calor", "A la Garrotxa plou molt.")]
predictions = pipe(prepare(sentence_pairs), add_special_tokens=False)
# convert back to scores to the original 1 and 5 interval
for prediction in predictions:
prediction['score'] = logit(prediction['score'])
print(predictions)
```
Expected output:
```
[{'label': 'SIMILARITY', 'score': 2.4280577200108384},
{'label': 'SIMILARITY', 'score': 2.132843521240822},
{'label': 'SIMILARITY', 'score': 1.615101695426227}]
```
<sup>1</sup> _**avoid using the widget** scores since they are normalized and do not reflect the original annotation values._
## Citing
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
| 3,903 |
textattack/distilbert-base-uncased-QNLI | null | Entry not found | 15 |
AlekseyDorkin/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: "lol"
---
# xlm-roberta-en-ru-emoji
- Problem type: Multi-class Classification | 399 |
adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717 | [
"Dance",
"Heavy Metal",
"Hip Hop",
"Indie",
"Pop",
"Rock"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- adrianmoses/autonlp-data-auto-nlp-lyrics-classification
co2_eq_emissions: 88.89388195672073
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 19333717
- CO2 Emissions (in grams): 88.89388195672073
## Validation Metrics
- Loss: 1.0499154329299927
- Accuracy: 0.6207088513638894
- Macro F1: 0.46250803661544765
- Micro F1: 0.6207088513638894
- Weighted F1: 0.5850362079928957
- Macro Precision: 0.6451479987704787
- Micro Precision: 0.6207088513638894
- Weighted Precision: 0.6285080101186085
- Macro Recall: 0.4405680478429344
- Micro Recall: 0.6207088513638894
- Weighted Recall: 0.6207088513638894
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,465 |
chihao/bert_cn_finetuning | [
"LABEL_0",
"LABEL_1"
] | Entry not found | 15 |
chitra/distilbert-negation | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
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) | 75 |
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 trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Text Classification [DigiMag, Persian News]
The task target is labeling texts in a supervised manner in both existing datasets `DigiMag` and `Persian News`.
### Persian News
A dataset of various news articles scraped from different online news agencies' websites. The total number of articles is 16,438, spread over eight different classes.
1. Economic
2. International
3. Political
4. Science Technology
5. Cultural Art
6. Sport
7. Medical
| Label | # |
|:------------------:|:----:|
| Social | 2170 |
| Economic | 1564 |
| International | 1975 |
| Political | 2269 |
| Science Technology | 2436 |
| Cultural Art | 2558 |
| Sport | 1381 |
| Medical | 2085 |
**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=1B6xotfXCcW9xS1mYSBQos7OCg0ratzKC)
## Results
The following table summarizes the F1 score obtained as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT |
|:-----------------:|:-----------------:|:-----------:|:-----:|
| Persian News | 97.01 | 97.19 | 95.79 |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo. | 2,676 |
poom-sci/bert-base-uncased-multi-emotion | [
"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",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | ---
language:
- en
tags:
- translation
license: apache-2.0
datasets:
- go_emotions
---
created for study | 105 |
adamnik/bert-event-detection | null | ---
license: mit
---
| 21 |
A-bhimany-u08/bert-base-cased-qqp | null |
---
inference: False
datasets:
- qqp
---
bert-base-cased model trained on quora question pair dataset. The task requires to predict whether the two given sentences (or questions) are `not_duplicate` (label 0) or `duplicate` (label 1). The model achieves 89% evaluation accuracy
| 318 |
Cameron/BERT-jigsaw-severetoxic | null | Entry not found | 15 |
M-FAC/bert-mini-finetuned-sst2 | null | # BERT-mini model finetuned with M-FAC
This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default Adam baseline, we finetune the model in the same framework as described here [https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) and just swap Adam optimizer with M-FAC.
Hyperparameters used by M-FAC optimizer:
```bash
learning rate = 1e-4
number of gradients = 1024
dampening = 1e-6
```
## Results
We share the best model out of 5 runs with the following score on SST-2 validation set:
```bash
accuracy = 84.74
```
Mean and standard deviation for 5 runs on SST-2 validation set:
| | Accuracy |
|:----:|:-----------:|
| Adam | 85.46 ± 0.58 |
| M-FAC | 84.20 ± 0.58 |
Results can be reproduced by adding M-FAC optimizer code in [https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) and running the following bash script:
```bash
CUDA_VISIBLE_DEVICES=0 python run_glue.py \
--seed 1234 \
--model_name_or_path prajjwal1/bert-mini \
--task_name sst2 \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--learning_rate 1e-4 \
--num_train_epochs 3 \
--output_dir out_dir/ \
--optim MFAC \
--optim_args '{"lr": 1e-4, "num_grads": 1024, "damp": 1e-6}'
```
We believe these results could be improved with modest tuning of hyperparameters: `per_device_train_batch_size`, `learning_rate`, `num_train_epochs`, `num_grads` and `damp`. For the sake of fair comparison and a robust default setup we use the same hyperparameters across all models (`bert-tiny`, `bert-mini`) and all datasets (SQuAD version 2 and GLUE).
Our code for M-FAC can be found here: [https://github.com/IST-DASLab/M-FAC](https://github.com/IST-DASLab/M-FAC).
A step-by-step tutorial on how to integrate and use M-FAC with any repository can be found here: [https://github.com/IST-DASLab/M-FAC/tree/master/tutorials](https://github.com/IST-DASLab/M-FAC/tree/master/tutorials).
## BibTeX entry and citation info
```bibtex
@article{frantar2021m,
title={M-FAC: Efficient Matrix-Free Approximations of Second-Order Information},
author={Frantar, Elias and Kurtic, Eldar and Alistarh, Dan},
journal={Advances in Neural Information Processing Systems},
volume={35},
year={2021}
}
```
| 2,733 |
SparkBeyond/roberta-large-sts-b | [
"LABEL_0"
] |
# Roberta Large STS-B
This model is a fine tuned RoBERTA model over STS-B.
It was trained with these params:
!python /content/transformers/examples/text-classification/run_glue.py \
--model_type roberta \
--model_name_or_path roberta-large \
--task_name STS-B \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /content/glue_data/STS-B/ \
--max_seq_length 128 \
--per_gpu_eval_batch_size=8 \
--per_gpu_train_batch_size=8 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /content/roberta-sts-b
## How to run
```python
import toolz
import torch
batch_size = 6
def roberta_similarity_batches(to_predict):
batches = toolz.partition(batch_size, to_predict)
similarity_scores = []
for batch in batches:
sentences = [(sentence_similarity["sent1"], sentence_similarity["sent2"]) for sentence_similarity in batch]
batch_scores = similarity_roberta(model, tokenizer,sentences)
similarity_scores = similarity_scores + batch_scores[0].cpu().squeeze(axis=1).tolist()
return similarity_scores
def similarity_roberta(model, tokenizer, sent_pairs):
batch_token = tokenizer(sent_pairs, padding='max_length', truncation=True, max_length=500)
res = model(torch.tensor(batch_token['input_ids']).cuda(), attention_mask=torch.tensor(batch_token["attention_mask"]).cuda())
return res
similarity_roberta(model, tokenizer, [('NEW YORK--(BUSINESS WIRE)--Rosen Law Firm, a global investor rights law firm, announces it is investigating potential securities claims on behalf of shareholders of Vale S.A. ( VALE ) resulting from allegations that Vale may have issued materially misleading business information to the investing public',
'EQUITY ALERT: Rosen Law Firm Announces Investigation of Securities Claims Against Vale S.A. – VALE')])
```
| 1,949 |
emrecan/convbert-base-turkish-mc4-cased-allnli_tr | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- tr
tags:
- zero-shot-classification
- nli
- pytorch
pipeline_tag: zero-shot-classification
license: apache-2.0
datasets:
- nli_tr
metrics:
- accuracy
widget:
- text: "Dolar yükselmeye devam ediyor."
candidate_labels: "ekonomi, siyaset, spor"
- text: "Senaryo çok saçmaydı, beğendim diyemem."
candidate_labels: "olumlu, olumsuz"
---
<!-- 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. -->
# convbert-base-turkish-mc4-cased_allnli_tr
This model is a fine-tuned version of [dbmdz/convbert-base-turkish-mc4-cased](https://huggingface.co/dbmdz/convbert-base-turkish-mc4-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5541
- Accuracy: 0.8111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7338 | 0.03 | 1000 | 0.6722 | 0.7236 |
| 0.603 | 0.07 | 2000 | 0.6465 | 0.7399 |
| 0.5605 | 0.1 | 3000 | 0.5801 | 0.7728 |
| 0.55 | 0.14 | 4000 | 0.5994 | 0.7626 |
| 0.529 | 0.17 | 5000 | 0.5720 | 0.7697 |
| 0.5196 | 0.2 | 6000 | 0.5692 | 0.7769 |
| 0.5117 | 0.24 | 7000 | 0.5725 | 0.7785 |
| 0.5044 | 0.27 | 8000 | 0.5532 | 0.7787 |
| 0.5016 | 0.31 | 9000 | 0.5546 | 0.7812 |
| 0.5031 | 0.34 | 10000 | 0.5461 | 0.7870 |
| 0.4949 | 0.37 | 11000 | 0.5725 | 0.7826 |
| 0.4894 | 0.41 | 12000 | 0.5419 | 0.7933 |
| 0.4796 | 0.44 | 13000 | 0.5278 | 0.7914 |
| 0.4795 | 0.48 | 14000 | 0.5193 | 0.7953 |
| 0.4713 | 0.51 | 15000 | 0.5534 | 0.7771 |
| 0.4738 | 0.54 | 16000 | 0.5098 | 0.8039 |
| 0.481 | 0.58 | 17000 | 0.5244 | 0.7958 |
| 0.4634 | 0.61 | 18000 | 0.5215 | 0.7972 |
| 0.465 | 0.65 | 19000 | 0.5129 | 0.7985 |
| 0.4624 | 0.68 | 20000 | 0.5062 | 0.8047 |
| 0.4597 | 0.71 | 21000 | 0.5114 | 0.8029 |
| 0.4571 | 0.75 | 22000 | 0.5070 | 0.8073 |
| 0.4602 | 0.78 | 23000 | 0.5115 | 0.7993 |
| 0.4552 | 0.82 | 24000 | 0.5085 | 0.8052 |
| 0.4538 | 0.85 | 25000 | 0.5118 | 0.7974 |
| 0.4517 | 0.88 | 26000 | 0.5036 | 0.8044 |
| 0.4517 | 0.92 | 27000 | 0.4930 | 0.8062 |
| 0.4413 | 0.95 | 28000 | 0.5307 | 0.7964 |
| 0.4483 | 0.99 | 29000 | 0.5195 | 0.7938 |
| 0.4036 | 1.02 | 30000 | 0.5238 | 0.8029 |
| 0.3724 | 1.05 | 31000 | 0.5125 | 0.8082 |
| 0.3777 | 1.09 | 32000 | 0.5099 | 0.8075 |
| 0.3753 | 1.12 | 33000 | 0.5172 | 0.8053 |
| 0.367 | 1.15 | 34000 | 0.5188 | 0.8053 |
| 0.3819 | 1.19 | 35000 | 0.5218 | 0.8046 |
| 0.363 | 1.22 | 36000 | 0.5202 | 0.7993 |
| 0.3794 | 1.26 | 37000 | 0.5240 | 0.8048 |
| 0.3749 | 1.29 | 38000 | 0.5026 | 0.8054 |
| 0.367 | 1.32 | 39000 | 0.5198 | 0.8075 |
| 0.3759 | 1.36 | 40000 | 0.5298 | 0.7993 |
| 0.3701 | 1.39 | 41000 | 0.5072 | 0.8091 |
| 0.3742 | 1.43 | 42000 | 0.5071 | 0.8098 |
| 0.3706 | 1.46 | 43000 | 0.5317 | 0.8037 |
| 0.3716 | 1.49 | 44000 | 0.5034 | 0.8052 |
| 0.3717 | 1.53 | 45000 | 0.5258 | 0.8012 |
| 0.3714 | 1.56 | 46000 | 0.5195 | 0.8050 |
| 0.3781 | 1.6 | 47000 | 0.5004 | 0.8104 |
| 0.3725 | 1.63 | 48000 | 0.5124 | 0.8113 |
| 0.3624 | 1.66 | 49000 | 0.5040 | 0.8094 |
| 0.3657 | 1.7 | 50000 | 0.4979 | 0.8111 |
| 0.3669 | 1.73 | 51000 | 0.4968 | 0.8100 |
| 0.3636 | 1.77 | 52000 | 0.5075 | 0.8079 |
| 0.36 | 1.8 | 53000 | 0.4985 | 0.8110 |
| 0.3624 | 1.83 | 54000 | 0.5125 | 0.8070 |
| 0.366 | 1.87 | 55000 | 0.4918 | 0.8117 |
| 0.3655 | 1.9 | 56000 | 0.5051 | 0.8109 |
| 0.3609 | 1.94 | 57000 | 0.5083 | 0.8105 |
| 0.3672 | 1.97 | 58000 | 0.5129 | 0.8085 |
| 0.3545 | 2.0 | 59000 | 0.5467 | 0.8109 |
| 0.2938 | 2.04 | 60000 | 0.5635 | 0.8049 |
| 0.29 | 2.07 | 61000 | 0.5781 | 0.8041 |
| 0.2992 | 2.11 | 62000 | 0.5470 | 0.8077 |
| 0.2957 | 2.14 | 63000 | 0.5765 | 0.8073 |
| 0.292 | 2.17 | 64000 | 0.5472 | 0.8106 |
| 0.2893 | 2.21 | 65000 | 0.5590 | 0.8085 |
| 0.2883 | 2.24 | 66000 | 0.5535 | 0.8064 |
| 0.2923 | 2.28 | 67000 | 0.5508 | 0.8095 |
| 0.2868 | 2.31 | 68000 | 0.5679 | 0.8098 |
| 0.2892 | 2.34 | 69000 | 0.5660 | 0.8057 |
| 0.292 | 2.38 | 70000 | 0.5494 | 0.8088 |
| 0.286 | 2.41 | 71000 | 0.5653 | 0.8085 |
| 0.2939 | 2.45 | 72000 | 0.5673 | 0.8070 |
| 0.286 | 2.48 | 73000 | 0.5600 | 0.8092 |
| 0.2844 | 2.51 | 74000 | 0.5508 | 0.8095 |
| 0.2913 | 2.55 | 75000 | 0.5645 | 0.8088 |
| 0.2859 | 2.58 | 76000 | 0.5677 | 0.8095 |
| 0.2892 | 2.62 | 77000 | 0.5598 | 0.8113 |
| 0.2898 | 2.65 | 78000 | 0.5618 | 0.8096 |
| 0.2814 | 2.68 | 79000 | 0.5664 | 0.8103 |
| 0.2917 | 2.72 | 80000 | 0.5484 | 0.8122 |
| 0.2907 | 2.75 | 81000 | 0.5522 | 0.8116 |
| 0.2896 | 2.79 | 82000 | 0.5540 | 0.8093 |
| 0.2907 | 2.82 | 83000 | 0.5469 | 0.8104 |
| 0.2882 | 2.85 | 84000 | 0.5471 | 0.8122 |
| 0.2878 | 2.89 | 85000 | 0.5532 | 0.8108 |
| 0.2858 | 2.92 | 86000 | 0.5511 | 0.8115 |
| 0.288 | 2.96 | 87000 | 0.5491 | 0.8111 |
| 0.2834 | 2.99 | 88000 | 0.5541 | 0.8111 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
| 7,095 |
lighteternal/fact-or-opinion-xlmr-el | null | ---
language:
- en
- el
- multilingual
tags:
- text-classification
- fact-or-opinion
- transformers
widget:
- text: "Ξεχωρίζει η καθηλωτική ερμηνεία του πρωταγωνιστή."
- text: "Η Ελλάδα είναι χώρα της Ευρώπης."
- text: "Tolkien was an English writer"
- text: "Tolkien is my favorite writer."
pipeline_tag: text-classification
license: apache-2.0
---
# Fact vs. opinion binary classifier, trained on a mixed EN-EL annotated corpus.
### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
This is an XLM-Roberta-base model with a binary classification head. Given a sentence, it can classify it either as a fact or an opinion based on its content.
You can use this model in any of the XLM-R supported languages for the same task, taking advantage of its 0-shot learning capabilities. However, the model was trained only using English and Greek sentences.
Legend of HuggingFace API labels:
* Label 0: Opinion/Subjective sentence
* Label 1: Fact/Objective sentence
## Dataset training info
The original dataset (available here: https://github.com/1024er/cbert_aug/tree/crayon/datasets/subj) contained aprox. 9000 annotated sentences (classified as subjective or objective). It was translated to Greek using Google Translate. The Greek version was then concatenated with the original English one to create the mixed EN-EL dataset.
The model was trained for 5 epochs, using batch size = 8. Detailed metrics and hyperparameters available on the "Metrics" tab.
## Evaluation Results on test set
| accuracy | precision | recall | f1 |
| ----------- | ----------- | ----------- | ----------- |
|0.952 | 0.945 | 0.960 | 0.952 |
## Acknowledgement
The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
| 1,847 |
dexay/reDs3others | [
"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",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | Entry not found | 15 |
Cameron/BERT-jigsaw-identityhate | null | Entry not found | 15 |
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:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.937
---
# twitter-emotion-deberta-v3-base
This model is a fine-tuned version of [DeBERTa-v3](https://huggingface.co/microsoft/deberta-v3-base). It achieves the following results on the evaluation set:
- Loss: 0.1474
- Accuracy: 0.937
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 80
- eval_batch_size: 80
- lr_scheduler_type: linear
- num_epochs: 6.0
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.15.1
- Tokenizers 0.10.3 | 921 |
NYTK/sentiment-hts2-xlm-roberta-hungarian | null | ---
language:
- hu
tags:
- text-classification
license: gpl
metrics:
- accuracy
widget:
- text: "Jó reggelt! majd küldöm az élményhozókat :)."
---
# Hungarian Sentence-level Sentiment Analysis model with XLM-RoBERTa
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: XLM-RoBERTa base
- Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
- Labels: 1, 2
## Limitations
- max_seq_length = 128
## Results
| Model | HTS2 | HTS5 |
| ------------- | ------------- | ------------- |
| huBERT | 85.55 | 68.99 |
| XLM-RoBERTa| **85.56** | 85.56 |
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {yang-bart,
title = {Improving Performance of Sentence-level Sentiment Analysis with Data Augmentation Methods},
booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)},
year = {2021},
publisher = {IEEE},
address = {Online},
author = {{Laki, László and Yang, Zijian Győző}}
pages = {417--422}
}
``` | 1,153 |
SetFit/deberta-v3-large__sst2__train-8-2 | [
"negative",
"positive"
] | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-2
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. -->
# deberta-v3-large__sst2__train-8-2
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6794
- Accuracy: 0.6063
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6942 | 1.0 | 3 | 0.7940 | 0.25 |
| 0.6068 | 2.0 | 6 | 0.9326 | 0.25 |
| 0.6553 | 3.0 | 9 | 0.7979 | 0.25 |
| 0.475 | 4.0 | 12 | 0.7775 | 0.25 |
| 0.377 | 5.0 | 15 | 0.7477 | 0.25 |
| 0.3176 | 6.0 | 18 | 0.6856 | 0.75 |
| 0.2708 | 7.0 | 21 | 0.6554 | 0.75 |
| 0.2855 | 8.0 | 24 | 0.8129 | 0.5 |
| 0.148 | 9.0 | 27 | 0.7074 | 0.75 |
| 0.0947 | 10.0 | 30 | 0.7090 | 0.75 |
| 0.049 | 11.0 | 33 | 0.7885 | 0.75 |
| 0.0252 | 12.0 | 36 | 0.9203 | 0.75 |
| 0.0165 | 13.0 | 39 | 1.0937 | 0.75 |
| 0.0084 | 14.0 | 42 | 1.2502 | 0.75 |
| 0.0059 | 15.0 | 45 | 1.3726 | 0.75 |
| 0.0037 | 16.0 | 48 | 1.4784 | 0.75 |
| 0.003 | 17.0 | 51 | 1.5615 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 2,400 |
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"
- text: "A new moon has been discovered in Jupter's orbit."
---
# distilbert-base-uncased-agnews-student
## Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled AG's News dataset using [this
script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation).
It is the result of the demo notebook
[here](https://colab.research.google.com/drive/1mjBjd0cR8G57ZpsnFCS3ngGyo5nCa9ya?usp=sharing), where more details
about the model can be found.
- Teacher model: [roberta-large-mnli](https://huggingface.co/roberta-large-mnli)
- Teacher hypothesis template: `"This text is about {}."`
## Intended Usage
The model can be used like any other model trained on AG's News, but will likely not perform as well as a model
trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
can be distilled to a more efficient student.
| 1,290 |
pparasurama/raceBERT | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | Entry not found | 15 |
DaNLP/da-bert-hatespeech-classification | [
"Personangreb",
"Spam & indhold",
"Sprogbrug",
"Særlig opmærksomhed"
] | ---
language:
- da
tags:
- bert
- pytorch
- hatespeech
license: cc-by-sa-4.0
datasets:
- social media
metrics:
- f1
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` (offensive language)
* `Spam & indhold` (spam)
This model is intended to be used after the [BERT HateSpeech detection model](https://huggingface.co/DaNLP/da-bert-hatespeech-detection).
It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data.
See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details.
Here is how to use the model:
```python
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("DaNLP/da-bert-hatespeech-classification")
tokenizer = BertTokenizer.from_pretrained("DaNLP/da-bert-hatespeech-classification")
```
## Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.
| 1,341 |
LeoFeng/ChineseSequenceClassification | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6",
"LABEL_7",
"LABEL_8",
"LABEL_9"
] | 利用THUC dataset 訓練的文章分類器，共支援14種種類 | 32 |
sangrimlee/bert-base-multilingual-cased-nsmc | null | ---
language: ko
---
# BERT multilingual basecased finetuned with NSMC
This model is a fine-tune checkpoint of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased), fine-tuned on [NSMC(Naver Sentiment Movie Corpus)](https://github.com/e9t/nsmc).
## Usage
You can use this model directly with a pipeline for sentiment-analysis:
```python
>>> from transformers import pipeline
>>> classifier = pipeline(
"sentiment-analysis", model="sangrimlee/bert-base-multilingual-cased-nsmc"
)
>>> classifier("흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나.")
>>> classifier("액션이 없는데도 재미 있는 몇안되는 영화")
[{'label': 'negative', 'score': 0.9642567038536072}]
[{'label': 'positive', 'score': 0.9970554113388062}]
```
| 724 |
Inari/deberta-v3-large-snli_mnli_fever_anli_R1_R2_R3-nli | [
"contradiction",
"entailment",
"neutral"
] | ---
language:
- en
tags:
- text-classification
metrics:
- accuracy
datasets:
- snli-1.0
- multi-nli-1.0
- nli-fever
- anli-v1.0
widget:
- text: "British mountaineer Alison Hargreaves becomes the first woman to climb Mount Everest alone and without oxygen tanks. [SEP] Alison is a female."
- text: "Mr Lopez Obrador has alleged electoral fraud cost him the presidency, despite a recount confirming Felipe Calderon as Mexico's president-elect. [SEP] Mr Lopez Obrador was born in mexico."
---
## deberta-v3-large-snli_mnli_fever_anli_R1_R2_R3-nli
#### Datasets
This model was trained on the snli-v1.0, multi-nli-1.0, nli-fever and anli-1.0-r1/anli-1.0-r2/anli-1.0-r3 datasets with the training weights of 1,1,1,10,20,10 respectively.
The training codes are mostly referenced from: https://github.com/facebookresearch/anli
#### Hyperparameters
learning_rate: 1e-5
max_length: 156
batch_size: 16
warmup_ratio: 0.1
weight_decay: 0.0
num_epochs: 2
#### Dev results
snli-v1.0 | multi-nli-1.0-m | multi-nli-1.0-mm | anli-1.0-r1 | anli-1.0-r2 | anli-1.0-r3
----------|-----------------|------------------|-------------|-------------|------------
0.938 | 0.914 | 0.912 | 0.796 | 0.627 | 0.610
#### Test results
snli-v1.0 | anli-1.0-r1 | anli-1.0-r2 | anli-1.0-r3
-----------|-------------|-------------|------------
0.929 | 0.775 | 0.636 | 0.612 | 1,358 |
MoritzLaurer/xtremedistil-l6-h256-mnli-fever-anli-ling-binary | [
"entailment",
"not_entailment"
] | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
datasets:
- multi_nli
- anli
- fever
- lingnli
pipeline_tag: zero-shot-classification
---
# xtremedistil-l6-h256-mnli-fever-anli-ling-binary
## Model description
This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [ANLI](https://github.com/facebookresearch/anli).
Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". This is specifically designed for zero-shot classification, where the difference between "neutral" and "contradiction" is irrelevant.
The base model is [xtremedistil-l6-h256-uncased from Microsoft](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased).
## Intended uses & limitations
#### How to use the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "MoritzLaurer/xtremedistil-l6-h256-mnli-fever-anli-ling-binary"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
hypothesis = "The movie was good."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "not_entailment"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Training data
This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [ANLI](https://github.com/facebookresearch/anli).
### Training procedure
xtremedistil-l6-h256-mnli-fever-anli-ling-binary was trained using the Hugging Face trainer with the following hyperparameters.
```
training_args = TrainingArguments(
num_train_epochs=5, # total number of training epochs
learning_rate=2e-05,
per_device_train_batch_size=32, # batch size per device during training
per_device_eval_batch_size=32, # batch size for evaluation
warmup_ratio=0.1, # number of warmup steps for learning rate scheduler
weight_decay=0.06, # strength of weight decay
fp16=True # mixed precision training
)
```
### Eval results
The model was evaluated using the binary test sets for MultiNLI, ANLI, LingNLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy.
dataset | mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c
--------|---------|----------|---------|----------|----------|------
accuracy | 0.897 | 0.898 | 0.861 | 0.607 | 0.62 | 0.827
speed (text/sec, GPU Tesla P100, 128 batch) | 1490 | 1485 | 760 | 1186 | 1062 | 1791
## Limitations and bias
Please consult the original paper and literature on different NLI datasets for potential biases.
### BibTeX entry and citation info
If you want to cite this model, please cite the original paper, the respective NLI datasets and include a link to this model on the Hugging Face hub.
### Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
### Debugging and issues
Note that the model was released recently and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 might solve some issues. | 4,083 |
boychaboy/MNLI_distilroberta-base | [
"contradiction",
"entailment",
"neutral"
] | Entry not found | 15 |
cardiffnlp/bertweet-base-stance-feminist | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | 0 | |
textattack/xlnet-large-cased-SST-2 | null | Entry not found | 15 |
turing-usp/FinBertPTBR | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: pt
license: apache-2.0
widget:
- text: "O futuro de DI caiu 20 bps nesta manhã"
example_title: "Example 1"
- text: "O Nubank decidiu cortar a faixa de preço da oferta pública inicial (IPO) após revés no humor dos mercados internacionais com as fintechs."
example_title: "Example 2"
- text: "O Ibovespa acompanha correção do mercado e fecha com alta moderada"
example_title: "Example 3"
---
# FinBertPTBR : Financial Bert PT BR
FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification.
## Usage
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("turing-usp/FinBertPTBR")
model = AutoModel.from_pretrained("turing-usp/FinBertPTBR")
```
## Authors
- [Vinicius Carmo](https://www.linkedin.com/in/vinicius-cleves/)
- [Julia Pocciotti](https://www.linkedin.com/in/juliapocciotti/)
- [Luísa Heise](https://www.linkedin.com/in/lu%C3%ADsa-mendes-heise/)
- [Lucas Leme](https://www.linkedin.com/in/lucas-leme-santos/)
| 1,224 |
uclanlp/plbart-c-cpp-defect-detection | null | Entry not found | 15 |
autoevaluate/multi-class-classification | [
"sadness",
"joy",
"love",
"anger",
"fear",
"surprise"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: multi-class-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9185
verified: true
- name: Precision Macro
type: precision
value: 0.8738350796775306
verified: true
- name: Precision Micro
type: precision
value: 0.9185
verified: true
- name: Precision Weighted
type: precision
value: 0.9179425177997311
verified: true
- name: Recall Macro
type: recall
value: 0.8650962919021573
verified: true
- name: Recall Micro
type: recall
value: 0.9185
verified: true
- name: Recall Weighted
type: recall
value: 0.9185
verified: true
- name: F1 Macro
type: f1
value: 0.8692821860210945
verified: true
- name: F1 Micro
type: f1
value: 0.9185
verified: true
- name: F1 Weighted
type: f1
value: 0.9181177508591364
verified: true
- name: loss
type: loss
value: 0.20905950665473938
verified: true
- name: matthews_correlation
type: matthews_correlation
value: 0.8920254536671932
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multi-class-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2009
- Accuracy: 0.928
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2643 | 1.0 | 1000 | 0.2009 | 0.928 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 2,894 |
gchhablani/bert-base-cased-finetuned-stsb | [
"LABEL_0"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: bert-base-cased-finetuned-stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.8897907271421561
---
<!-- 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-base-cased-finetuned-stsb
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4861
- Pearson: 0.8926
- Spearmanr: 0.8898
- Combined Score: 0.8912
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-stsb \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Combined Score | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:--------------:|:---------------:|:-------:|:---------:|
| 1.1174 | 1.0 | 360 | 0.8816 | 0.5000 | 0.8832 | 0.8800 |
| 0.3835 | 2.0 | 720 | 0.8901 | 0.4672 | 0.8915 | 0.8888 |
| 0.2388 | 3.0 | 1080 | 0.8912 | 0.4861 | 0.8926 | 0.8898 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
| 2,829 |
hamzab/roberta-fake-news-classification | null | ---
license: mit
widget:
- text: "Some ninja attacked the White House."
example_title: "Fake example 1"
language:
- en
tags:
- classification
datasets:
- "fake-and-real-news-dataset on kaggle"
---
## Overview
The model is a `roberta-base` fine-tuned on [fake-and-real-news-dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset). It has a 100% accuracy on that dataset.
The model takes a news article and predicts if it is true or fake.
The format of the input should be:
```
<title> TITLE HERE <content> CONTENT HERE <end>
```
## Using this model in your code
To use this model, first download it from the hugginface website:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification")
model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification")
```
Then, make a prediction like follows:
```python
import torch
def predict_fake(title,text):
input_str = "<title>" + title + "<content>" + text + "<end>"
input_ids = tokenizer.encode_plus(input_str, max_length=512, padding="max_length", truncation=True, return_tensors="pt")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
with torch.no_grad():
output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device))
return dict(zip(["Fake","Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])] ))
print(predict_fake(<HEADLINE-HERE>,<CONTENT-HERE>))
```
You can also use Gradio to test the model on real-time:
```python
import gradio as gr
iface = gr.Interface(fn=predict_fake, inputs=[gr.inputs.Textbox(lines=1,label="headline"),gr.inputs.Textbox(lines=6,label="content")], outputs="label").launch(share=True)
``` | 1,883 |
tinkoff-ai/response-quality-classifier-large | [
"relevance",
"specificity"
] | ---
license: mit
widget:
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]супер, вот только проснулся, у тебя как?"
example_title: "Dialog example 1"
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм"
example_title: "Dialog example 2"
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм, у тя как?"
example_title: "Dialog example 3"
language:
- ru
tags:
- conversational
---
This classification model is based on [sberbank-ai/ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large).
The model should be used to produce relevance and specificity of the last message in the context of a dialogue.
The labels explanation:
- `relevance`: is the last message in the dialogue relevant in the context of the full dialogue.
- `specificity`: is the last message in the dialogue interesting and promotes the continuation of the dialogue.
It is pretrained on a large corpus of dialog data in unsupervised manner: the model is trained to predict whether last response was in a real dialog, or it was pulled from some other dialog at random.
Then it was finetuned on manually labelled examples (dataset will be posted soon).
The model was trained with three messages in the context and one response. Each message was tokenized separately with ``` max_length = 32 ```.
The performance of the model on validation split (dataset will be posted soon) (with the best thresholds for validation samples):
| | threshold | f0.5 | ROC AUC |
|:------------|------------:|-------:|----------:|
| relevance | 0.59 | 0.86 | 0.83 |
| specificity | 0.61 | 0.85 | 0.86 |
How to use:
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/response-quality-classifier-large')
model = AutoModelForSequenceClassification.from_pretrained('tinkoff-ai/response-quality-classifier-large')
inputs = tokenizer('[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм, у тя как?', max_length=128, add_special_tokens=False, return_tensors='pt')
with torch.inference_mode():
logits = model(**inputs).logits
probas = torch.sigmoid(logits)[0].cpu().detach().numpy()
relevance, specificity = probas
```
The [app](https://huggingface.co/spaces/tinkoff-ai/response-quality-classifiers) where you can easily interact with this model.
The work was done during internship at Tinkoff by [egoriyaa](https://github.com/egoriyaa), mentored by [solemn-leader](https://huggingface.co/solemn-leader). | 2,575 |
anlausch/aq_bert_ibm | [
"LABEL_0"
] | ---
license: mit
---
Model trained on IBMArgRank30k for 2 epochs with a learning rate of 3e-5 (optimised via grid search) in a similar way as in Lauscher et al. 2020 (see below). The original model was Tensorflow-based. This model corresponds to a reimplementation with Transformers & PyTorch.
```
@inproceedings{lauscher-etal-2020-rhetoric,
title = "Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing",
author = "Lauscher, Anne and
Ng, Lily and
Napoles, Courtney and
Tetreault, Joel",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.402",
doi = "10.18653/v1/2020.coling-main.402",
pages = "4563--4574",
abstract = "Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory. However, a large-scale theory-based corpus and corresponding computational models are missing. We fill this gap by conducting an extensive analysis covering three diverse domains of online argumentative writing and presenting GAQCorpus: the first large-scale English multi-domain (community Q{\&}A forums, debate forums, review forums) corpus annotated with theory-based AQ scores. We then propose the first computational approaches to theory-based assessment, which can serve as strong baselines for future work. We demonstrate the feasibility of large-scale AQ annotation, show that exploiting relations between dimensions yields performance improvements, and explore the synergies between theory-based prediction and practical AQ assessment.",
}
``` | 1,943 |
mvonwyl/distilbert-base-uncased-imdb | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9214
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an imdb dataset where an evaluation of 5000 samples was created by splitting the training set.
It achieves the following results on the evaluation set:
- Loss: 0.6252
- Accuracy: 0.9214
## Model description
More information needed
## Intended uses & limitations
This model was trained for the introduction to Natural language processing course of [EPITA](https://www.epita.fr/).
## Training and evaluation data
The training/evaluation split was generated using a `seed` of 42 and a `test_size` of 0.2.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2875 | 1.0 | 625 | 0.2286 | 0.9102 |
| 0.1685 | 2.0 | 1250 | 0.2416 | 0.9128 |
| 0.1171 | 3.0 | 1875 | 0.3223 | 0.917 |
| 0.0493 | 4.0 | 2500 | 0.3667 | 0.9162 |
| 0.023 | 5.0 | 3125 | 0.4074 | 0.92 |
| 0.015 | 6.0 | 3750 | 0.4291 | 0.9236 |
| 0.0129 | 7.0 | 4375 | 0.5452 | 0.9194 |
| 0.0051 | 8.0 | 5000 | 0.5886 | 0.9146 |
| 0.0027 | 9.0 | 5625 | 0.6310 | 0.9186 |
| 0.002 | 10.0 | 6250 | 0.6252 | 0.9214 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 2,392 |
semy/hf-model-full-0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: hf-model-full-0
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. -->
# hf-model-full-0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4295
- Accuracy: 0.802
- F1: 0.802
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|
| 0.9446 | 1.0 | 563 | 0.4208 | 0.793 | 0.793 |
| 0.1259 | 2.0 | 1126 | 0.4295 | 0.802 | 0.802 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,444 |
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 and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [1] using the **average pooling strategy** and a **softmax loss**.
It is further fine-tuned Med-Marco Dataset. MacAvaney et.al in their [paper](https://arxiv.org/abs/2010.05987) titled “SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search” used MedSyn a lexicon of layperson and expert terminology for various medical conditions to filter for medical questions. One can also replace this by UMLs ontologies but the beauty of MedSyn is that the terms are more general human conversation lingo and not terms based on scientific literature.
Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba)
**Base model**: `deepset/covid_bert_base` from HuggingFace's `AutoModel`.
| 1,422 |
banjtheman/distilbert-base-uncased-helpful-amazon | null | ---
license: apache-2.0
---
## Overview
This model was trained with data from https://registry.opendata.aws/helpful-sentences-from-reviews/ to predict how "helpful" a review is.
The model was fine-tuned from the `distilbert-base-uncased` model
### Labels
LABEL_0 - Not helpful
LABEL_1 - Helpful
### How to use
The following code shows how to make a prediction with this model
```python
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
TextClassificationPipeline,
)
tokenizer = AutoTokenizer.from_pretrained("banjtheman/distilbert-base-uncased-helpful-amazon")
model = AutoModelForSequenceClassification.from_pretrained(
"banjtheman/distilbert-base-uncased-helpful-amazon"
)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
result = pipe("This was a Christmas gift for my grandson.")
print(result)
#[{'label': 'LABEL_0', 'score': 0.998775064945221}]
# This is NOT A HELPFUL comment
``` | 1,006 |
textattack/distilbert-base-uncased-RTE | null | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.6570397111913358, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
| 629 |
agnihotri/cuad_contract_type | [
"Affiliate Agreement",
"Agency Agreements",
"Co_Branding",
"Collaboration",
"Consulting Agreements",
"Development",
"Distributor",
"Endorsement Agreement",
"Franchise",
"Hosting",
"IP",
"Joint Venture",
"License_Agreements",
"Maintenance",
"Manufacturing",
"Marketing",
"Non_Compete_Non_Solicit",
"Outsourcing",
"Promotion",
"Reseller",
"Service",
"Sponsorship",
"Strategic Alliance",
"Supply",
"Transportation"
] | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- agnihotri/autotrain-data-contract_type
co2_eq_emissions: 0.07610944071640048
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 809725368
- CO2 Emissions (in grams): 0.07610944071640048
## Validation Metrics
- Loss: 0.05312908813357353
- Accuracy: 0.9911504424778761
- Macro F1: 0.9912087912087912
- Micro F1: 0.9911504424778761
- Weighted F1: 0.9908586988233007
- Macro Precision: 0.9942857142857143
- Micro Precision: 0.9911504424778761
- Weighted Precision: 0.9924146649810366
- Macro Recall: 0.99
- Micro Recall: 0.9911504424778761
- Weighted Recall: 0.9911504424778761
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/agnihotri/autotrain-contract_type-809725368
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("agnihotri/autotrain-contract_type-809725368", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("agnihotri/autotrain-contract_type-809725368", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,401 |
sam34738/bert-hinglish | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4",
"LABEL_5",
"LABEL_6"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-hinglish
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-hinglish
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3557 | 1.0 | 460 | 0.7714 |
| 0.6349 | 2.0 | 920 | 0.5475 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
| 1,339 |
Cameron/BERT-mdgender-convai-ternary | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
hackathon-pln-es/twitter_sexismo-finetuned-robertuito-exist2021 | null | ---
license: apache-2.0
tags:
-
datasets:
- EXIST Dataset
widget:
- text: "manejas muy bien para ser mujer"
- text: "En temas políticos hombres y mujeres son iguales"
- text: "Los ipad son unos equipos electrónicos"
metrics:
- accuracy
model-index:
- name: twitter_sexismo-finetuned-exist2021
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: EXIST Dataset
type: EXIST Dataset
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.80
- name: F2
type: F-measure
value: 0.89
---
# twitter_sexismo-finetuned-exist2021
This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on the EXIST dataset
It achieves the following results on the evaluation set:
- Loss: 0.47
- Accuracy: 0.80
- F1: 0.83
- F2: 0.89
## Model description
Model for the 'Somos NLP' Hackathon for detecting sexism in twitters in Spanish. Created by:
- **medardodt**
- **MariaIsabel**
- **ManRo**
- **lucel172**
- **robertou2**
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
The model has been trained to get the best F2 score.The F-measure is calculated as the harmonic mean of precision and recall, giving each the same weighting. It allows a model to be evaluated taking both the precision and recall into account using a single score, which is helpful when describing the performance of the model and in comparing models. The Fbeta-measure is a generalization of the F-measure that adds a configuration parameter called beta. A default beta value is 1.0, which is the same as the F-measure. A smaller beta value, such as 0.5, gives more weight to precision and less to recall, whereas a larger beta value, such as 2.0, gives less weight to precision and more weight to recall in the calculation of the score. It is a useful metric to use when both precision and recall are important but slightly more attention is needed on one or the other, such as when false negatives are more important than false positives, or the reverse.F2-measure puts more attention on minimizing false negatives. We want to detect the sexist comments.
### Training hyperparameters
The following hyperparameters were used during training:
- my_learning_rate = 5E-5
- my_adam_epsilon = 1E-8
- my_number_of_epochs = 8
- my_warmup = 3
- my_mini_batch_size = 32
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
Epoch T. Loss V. Loss Accuracy F1 Precision Recall F2
1 0.478700 0.443148 0.804386 0.830160 0.750689 0.928450 0.886467
2 0.298000 0.460549 0.823684 0.841107 0.784661 0.906303 0.879048
3 0.063600 0.706177 0.817544 0.829508 0.799368 0.862010 0.848708
4 0.078700 1.060862 0.816667 0.836078 0.774709 0.908007 0.877800
5 0.005900 1.069239 0.808772 0.821604 0.790551 0.855196 0.841435
6 0.008300 1.184729 0.808772 0.821604 0.790551 0.855196 0.841435
7 0.001400 1.238865 0.816667 0.829388 0.796238 0.865417 0.850636
8 0.000100 1.267197 0.815789 0.827303 0.799682 0.856899 0.844810
9 0.000100 1.267815 0.808772 0.818937 0.799028 0.839864 0.831366
10 0.000300 1.275827 0.807895 0.818257 0.797735 0.839864 0.831086
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.6
## Model in Action
Fast usage with pipelines:
``` python
###libraries required
!pip install transformers
from transformers import pipeline
### usage pipelines
model_checkpoint = "robertou2/twitter_sexismo-finetuned-robertuito-exist2021"
pipeline_nlp = pipeline("text-classification", model=model_checkpoint)
pipeline_nlp("mujer al volante peligro!")
#pipeline_nlp("¡me encanta el ipad!")
#pipeline_nlp (["mujer al volante peligro!", "Los hombre tienen más manias que las mujeres", "me encanta el ipad!"] )
# OUTPUT MODEL #
# LABEL_0: "NON SEXISM"or LABEL_1: "SEXISM" and score: probability of accuracy per model.
# [{'label': 'LABEL_1', 'score': 0.9967633485794067}]
# [{'label': 'LABEL_0', 'score': 0.9934417009353638}]
#[{‘label': 'LABEL_1', 'score': 0.9967633485794067},
# {'label': 'LABEL_1', 'score': 0.9755664467811584},
# {'label': 'LABEL_0', 'score': 0.9955045580863953}]
```
## More Information Process
### Retos
Uno de los principales retos que se encontró en este proceso ha sido disponer de un dataset en español. Se ha logrado conseguir (previa solicitud) el dataset utilizado en [EXIST:sEXism Identification in Social neTworks](http://nlp.uned.es/exist2021/), el cual fue un gran punto de partida para comenzar con el modelo. Lamentablemente este un dataset presenta limitaciones debido a licencias y políticas para ser compartido libremente.
Este dataset incorpora cualquier tipo de expresión sexista o fenómenos relacionados, incluidas las afirmaciones descriptivas o informadas donde el mensaje sexista es un informe o una descripción de un comportamiento sexista. se han utilizado los 3,541 tweets etiquetados en español. Luego se logró disponer de otro dataset en español [MeTwo: Machismo and Sexism Twitter Identification dataset](https://github.com/franciscorodriguez92/MeTwo). Este dataset contiene los id de cada tweet con su etiqueta respectiva, lo que nos permitió obtener el texto del tweet e incrementar el dataset original.
Un desafío ha sido iniciar los procesos de finetuned en las prueba, esto pues se dispone de diversas variables para validar y testear (desde modelos como: BETO o Roberta, hasta hiperparámetros: como learning rate), y solo se disponede un plazo acotado de dos semanas, además de la curva de aprendizaje. Para este desafío, se han basado las primeras pruebas en los parámetros presentados por de Paula et al. (2021), lo cual brindó un punto de partida y un reto a vencer, el **_0.790 de accuracy_** obtenidos por el trabajo previo en la identificación de tweets sexistas en español.
En este ámbito se realizaron diversas pruebas en paralelo para encontrar el mejor modelo. Luego de un proceso colaborativo de finetuned se ha logrado obtener un **83% de accuracy**.
### Trabajos Futuros
Se propone incrementar el dataset desarrollado. Para esto es posible descargar cantidades superiores de tweets en español y aplicar técnicas de active learning para obtener un grupo reducido de tweets a etiquetar vía crowdsourcing, y en donde estos datos etiquetados puedan servir para etiquetar el resto. También se pueden utilizar técnicas de Data Augmentation, para duplicar y extender el dataset. Realizar más pruebas con otros modelos y mejorar el modelo es otro reto que se propone como trabajos futuros.
### Posibles Aplicaciones
Primero es sumamente importante dar mayor visibilidad al problema de _sexismo en redes sociales_, principalmente en español. El proceso de Transfer Learning logra reutilizar y aprovechar modelos previamente entrenados, y lo que se desea es que nuevos grupos de investigación, estudiantes, etc. utilicen la base del actual modelo para desarrollar los propios y crear un mejor modelo. De esta manera, se podría construir una herramienta que pueda identificar en tiempo real los tweets sexistas y eliminarlos antes de su propagación.
### Referencias
1 de Paula, A. F. M., da Silva, R. F., & Schlicht, I. B. (2021). Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models. arXiv preprint arXiv:2111.04551.
Rodríguez-Sánchez, F., Carrillo-de-Albornoz, J., Plaza, L., Gonzalo, J., Rosso, P., Comet, M., & Donoso, T. (2021). Overview of exist 2021: sexism identification in social networks. Procesamiento del Lenguaje Natural, 67, 195-207. | 7,840 |
qanastek/51-languages-classifier | [
"af-ZA",
"am-ET",
"ar-SA",
"az-AZ",
"bn-BD",
"cy-GB",
"da-DK",
"de-DE",
"el-GR",
"en-US",
"es-ES",
"fa-IR",
"fi-FI",
"fr-FR",
"he-IL",
"hi-IN",
"hu-HU",
"hy-AM",
"id-ID",
"is-IS",
"it-IT",
"ja-JP",
"jv-ID",
"ka-GE",
"km-KH",
"kn-IN",
"ko-KR",
"lv-LV",
"ml-IN",
"mn-MN",
"ms-MY",
"my-MM",
"nb-NO",
"nl-NL",
"pl-PL",
"pt-PT",
"ro-RO",
"ru-RU",
"sl-SL",
"sq-AL",
"sv-SE",
"sw-KE",
"ta-IN",
"te-IN",
"th-TH",
"tl-PH",
"tr-TR",
"ur-PK",
"vi-VN",
"zh-CN",
"zh-TW"
] | ---
tags:
- Transformers
- text-classification
- multi-class-classification
languages:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
- jv-ID
- ka-GE
- km-KH
- kn-IN
- ko-KR
- lv-LV
- ml-IN
- mn-MN
- ms-MY
- my-MM
- nb-NO
- nl-NL
- pl-PL
- pt-PT
- ro-RO
- ru-RU
- sl-SL
- sq-AL
- sv-SE
- sw-KE
- ta-IN
- te-IN
- th-TH
- tl-PH
- tr-TR
- ur-PK
- vi-VN
- zh-CN
- zh-TW
multilinguality:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
- jv-ID
- ka-GE
- km-KH
- kn-IN
- ko-KR
- lv-LV
- ml-IN
- mn-MN
- ms-MY
- my-MM
- nb-NO
- nl-NL
- pl-PL
- pt-PT
- ro-RO
- ru-RU
- sl-SL
- sq-AL
- sv-SE
- sw-KE
- ta-IN
- te-IN
- th-TH
- tl-PH
- tr-TR
- ur-PK
- vi-VN
- zh-CN
- zh-TW
datasets:
- qanastek/MASSIVE
widget:
- text: "wake me up at five am this week"
- text: "je veux écouter la chanson de jacques brel encore une fois"
- text: "quiero escuchar la canción de arijit singh una vez más"
- text: "olly onde é que á um parque por perto onde eu possa correr"
- text: "פרק הבא בפודקאסט בבקשה"
- text: "亚马逊股价"
- text: "найди билет на поезд в санкт-петербург"
license: cc-by-4.0
---
**People Involved**
* [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1)
**Affiliations**
1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France.
## Model
XLM-Roberta : [https://huggingface.co/xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
Paper : [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/pdf/1911.02116.pdf)
## Demo: How to use in HuggingFace Transformers Pipeline
Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers```
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
model_name = 'qanastek/51-languages-classifier'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
res = classifier("פרק הבא בפודקאסט בבקשה")
print(res)
```
Outputs:
```python
[{'label': 'he-IL', 'score': 0.9998375177383423}]
```
## Training data
[MASSIVE](https://huggingface.co/datasets/qanastek/MASSIVE) is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
### Languages
Thee model is capable of distinguish 51 languages :
- `Afrikaans - South Africa (af-ZA)`
- `Amharic - Ethiopia (am-ET)`
- `Arabic - Saudi Arabia (ar-SA)`
- `Azeri - Azerbaijan (az-AZ)`
- `Bengali - Bangladesh (bn-BD)`
- `Chinese - China (zh-CN)`
- `Chinese - Taiwan (zh-TW)`
- `Danish - Denmark (da-DK)`
- `German - Germany (de-DE)`
- `Greek - Greece (el-GR)`
- `English - United States (en-US)`
- `Spanish - Spain (es-ES)`
- `Farsi - Iran (fa-IR)`
- `Finnish - Finland (fi-FI)`
- `French - France (fr-FR)`
- `Hebrew - Israel (he-IL)`
- `Hungarian - Hungary (hu-HU)`
- `Armenian - Armenia (hy-AM)`
- `Indonesian - Indonesia (id-ID)`
- `Icelandic - Iceland (is-IS)`
- `Italian - Italy (it-IT)`
- `Japanese - Japan (ja-JP)`
- `Javanese - Indonesia (jv-ID)`
- `Georgian - Georgia (ka-GE)`
- `Khmer - Cambodia (km-KH)`
- `Korean - Korea (ko-KR)`
- `Latvian - Latvia (lv-LV)`
- `Mongolian - Mongolia (mn-MN)`
- `Malay - Malaysia (ms-MY)`
- `Burmese - Myanmar (my-MM)`
- `Norwegian - Norway (nb-NO)`
- `Dutch - Netherlands (nl-NL)`
- `Polish - Poland (pl-PL)`
- `Portuguese - Portugal (pt-PT)`
- `Romanian - Romania (ro-RO)`
- `Russian - Russia (ru-RU)`
- `Slovanian - Slovania (sl-SL)`
- `Albanian - Albania (sq-AL)`
- `Swedish - Sweden (sv-SE)`
- `Swahili - Kenya (sw-KE)`
- `Hindi - India (hi-IN)`
- `Kannada - India (kn-IN)`
- `Malayalam - India (ml-IN)`
- `Tamil - India (ta-IN)`
- `Telugu - India (te-IN)`
- `Thai - Thailand (th-TH)`
- `Tagalog - Philippines (tl-PH)`
- `Turkish - Turkey (tr-TR)`
- `Urdu - Pakistan (ur-PK)`
- `Vietnamese - Vietnam (vi-VN)`
- `Welsh - United Kingdom (cy-GB)`
## Evaluation results
```plain
precision recall f1-score support
af-ZA 0.9821 0.9805 0.9813 2974
am-ET 1.0000 1.0000 1.0000 2974
ar-SA 0.9809 0.9822 0.9815 2974
az-AZ 0.9946 0.9845 0.9895 2974
bn-BD 0.9997 0.9990 0.9993 2974
cy-GB 0.9970 0.9929 0.9949 2974
da-DK 0.9575 0.9617 0.9596 2974
de-DE 0.9906 0.9909 0.9908 2974
el-GR 0.9997 0.9973 0.9985 2974
en-US 0.9712 0.9866 0.9788 2974
es-ES 0.9825 0.9842 0.9834 2974
fa-IR 0.9940 0.9973 0.9956 2974
fi-FI 0.9943 0.9946 0.9945 2974
fr-FR 0.9963 0.9923 0.9943 2974
he-IL 1.0000 0.9997 0.9998 2974
hi-IN 1.0000 0.9980 0.9990 2974
hu-HU 0.9983 0.9950 0.9966 2974
hy-AM 1.0000 0.9993 0.9997 2974
id-ID 0.9319 0.9291 0.9305 2974
is-IS 0.9966 0.9943 0.9955 2974
it-IT 0.9698 0.9926 0.9811 2974
ja-JP 0.9987 0.9963 0.9975 2974
jv-ID 0.9628 0.9744 0.9686 2974
ka-GE 0.9993 0.9997 0.9995 2974
km-KH 0.9867 0.9963 0.9915 2974
kn-IN 1.0000 0.9993 0.9997 2974
ko-KR 0.9917 0.9997 0.9956 2974
lv-LV 0.9990 0.9950 0.9970 2974
ml-IN 0.9997 0.9997 0.9997 2974
mn-MN 0.9987 0.9966 0.9976 2974
ms-MY 0.9359 0.9418 0.9388 2974
my-MM 1.0000 0.9993 0.9997 2974
nb-NO 0.9600 0.9533 0.9566 2974
nl-NL 0.9850 0.9748 0.9799 2974
pl-PL 0.9946 0.9923 0.9934 2974
pt-PT 0.9885 0.9798 0.9841 2974
ro-RO 0.9919 0.9916 0.9918 2974
ru-RU 0.9976 0.9983 0.9980 2974
sl-SL 0.9956 0.9939 0.9948 2974
sq-AL 0.9936 0.9896 0.9916 2974
sv-SE 0.9902 0.9842 0.9872 2974
sw-KE 0.9867 0.9953 0.9910 2974
ta-IN 1.0000 1.0000 1.0000 2974
te-IN 1.0000 0.9997 0.9998 2974
th-TH 1.0000 0.9983 0.9992 2974
tl-PH 0.9929 0.9899 0.9914 2974
tr-TR 0.9869 0.9872 0.9871 2974
ur-PK 0.9983 0.9929 0.9956 2974
vi-VN 0.9993 0.9973 0.9983 2974
zh-CN 0.9812 0.9832 0.9822 2974
zh-TW 0.9832 0.9815 0.9823 2974
accuracy 0.9889 151674
macro avg 0.9889 0.9889 0.9889 151674
weighted avg 0.9889 0.9889 0.9889 151674
```
Keywords : language identification ; language identification ; multilingual ; classification | 7,528 |
MilaNLProc/hate-ita | [
"hateful",
"non-hateful"
] | ---
language: it
license: mit
tags:
- text classification
- abusive language
- hate speech
- offensive language
widget:
- text: "Ci sono dei bellissimi capibara!"
example_title: "Hate Speech Classification 1"
- text: "Sei una testa di cazzo!!"
example_title: "Hate Speech Classification 2"
- text: "Ti odio!"
example_title: "Hate Speech Classification 3"
---
#
[Debora Nozza](http://dnozza.github.io/) •
[Federico Bianchi](https://federicobianchi.io/) •
[Giuseppe Attanasio](https://gattanasio.cc/)
# HATE-ITA Base
HATE-ITA is a binary hate speech classification model for Italian social media text.
<img src="https://raw.githubusercontent.com/MilaNLProc/hate-ita/main/hateita.png?token=GHSAT0AAAAAABTEBAJ4PNDWAMU3KKIGUOCSYWG4IBA" width="200">
## Abstract
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing has been successfully used for the purpose, many of the research efforts are directed toward the English language. This choice severely limits the classification power in non-English languages. In this paper, we test several learning frameworks for identifying hate speech in Italian text. We release **HATE-ITA, a set of multi-language models trained on a large set of English data and available Italian datasets**. HATE-ITA performs better than mono-lingual models and seems to adapt well also on language-specific slurs. We believe our findings will encourage research in other mid-to-low resource communities and provide a valuable benchmarking tool for the Italian community.
## Model
This model is the fine-tuned version of the [XLM-T](https://arxiv.org/abs/2104.12250) model.
| Model | Download |
| ------ | -------------------------|
| `hate-ita` | [Link](https://huggingface.co/MilaNLProc/hate-ita) |
| `hate-ita-xlm-r-base` | [Link](https://huggingface.co/MilaNLProc/hate-ita-xlm-r-base) |
| `hate-ita-xlm-r-large` | [Link](https://huggingface.co/MilaNLProc/hate-ita-xlm-r-large) |
## Results
This model had an F1 of 0.83 on the test set.
## Usage
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='MilaNLProc/hate-ita',top_k=2)
prediction = classifier("ti odio")
print(prediction)
```
## Citation
Please use the following BibTeX entry if you use this model in your project:
```
@inproceedings{nozza-etal-2022-hate-ita,
title = {{HATE-ITA}: Hate Speech Detection in Italian Social Media Text},
author = "Nozza, Debora and Bianchi, Federico and Attanasio, Giuseppe",
booktitle = "Proceedings of the 6th Workshop on Online Abuse and Harms",
year = "2022",
publisher = "Association for Computational Linguistics"
}
```
## Ethical Statement
While promising, the results in this work should not be interpreted as a definitive assessment of the performance of hate speech detection in Italian. We are unsure if our model can maintain a stable and fair precision across the different targets and categories. HATE-ITA might overlook some sensible details, which practitioners should treat with care. | 3,133 |
semy/finetuning-sentiment-model-sst | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-sst
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. -->
# finetuning-sentiment-model-sst
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.2
- Datasets 2.3.2
- Tokenizers 0.12.1
| 1,037 |
naem1023/electra-phrase-clause-classification | null | ---
license: artistic-2.0
---
| 30 |
any0019/text_style_classifier | null | Entry not found | 15 |
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