
# setfit-italian-hate-speech

This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

This model detects the hate speech for italian language:
* 1 --> is hate speech
* 0 --> isn't hate speech

## Dataset

`setfit-italian-hate-speech` is trained on [HaSpeeDe-FB](http://twita.di.unito.it/dataset/haspeede) dataset.

## Usage

To use this model for inference, first install the SetFit library:

```bash
python -m pip install setfit
```

You can then run inference as follows:

```python
from setfit import SetFitModel

# Download from Hub and run inference
model = SetFitModel.from_pretrained("nickprock/setfit-italian-hate-speech")
# Run inference
preds = model(["Lei è una brutta bugiarda!", "Mi piace la pizza"])
```

## BibTeX entry and citation info

```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```

### Dataset Citation

```bibtex
@inproceedings{VignaCDPT17,
  title = {Hate Me, Hate Me Not: Hate Speech Detection on Facebook},
  author = {Fabio Del Vigna and Andrea Cimino and Felice dell'Orletta and Marinella Petrocchi and Maurizio Tesconi},
  year = {2017},
  url = {http://ceur-ws.org/Vol-1816/paper-09.pdf},
  researchr = {https://researchr.org/publication/VignaCDPT17},
  cites = {0},
  citedby = {0},
  pages = {86-95},
  booktitle = {Proceedings of the First Italian Conference on Cybersecurity (ITASEC17), Venice, Italy, January 17-20, 2017},
  editor = {Alessandro Armando and Roberto Baldoni and Riccardo Focardi},
  volume = {1816},
  series = {CEUR Workshop Proceedings},
  publisher = {CEUR-WS.org},
}
```
{"language": ["it"], "license": "apache-2.0", "library_name": "transformers", "tags": ["setfit", "sentence-transformers", "text-classification", "hate speech"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
transformers pytorch safetensors bert feature-extraction setfit sentence-transformers text-classification hate speech it arxiv:2209.11055 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #setfit #sentence-transformers #text-classification #hate speech #it #arxiv-2209.11055 #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# setfit-italian-hate-speech

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

This model detects the hate speech for italian language:
* 1 --> is hate speech
* 0 --> isn't hate speech

## Dataset

'setfit-italian-hate-speech' is trained on HaSpeeDe-FB dataset.

## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows:



## BibTeX entry and citation info



### Dataset Citation


# setfit-italian-hate-speech

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

This model detects the hate speech for italian language:
* 1 --> is hate speech
* 0 --> isn't hate speech ## Dataset

'setfit-italian-hate-speech' is trained on HaSpeeDe-FB dataset. ## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows: ## BibTeX entry and citation info ### Dataset Citation
TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #setfit #sentence-transformers #text-classification #hate speech #it #arxiv-2209.11055 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # setfit-italian-hate-speech

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

This model detects the hate speech for italian language:
* 1 --> is hate speech
* 0 --> isn't hate speech ## Dataset

'setfit-italian-hate-speech' is trained on HaSpeeDe-FB dataset. ## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows: ## BibTeX entry and citation info ### Dataset Citation
TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #setfit #sentence-transformers #text-classification #hate speech #it #arxiv-2209.11055 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# setfit-italian-hate-speech

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

This model detects the hate speech for italian language:
* 1 --> is hate speech
* 0 --> isn't hate speech## Dataset

'setfit-italian-hate-speech' is trained on HaSpeeDe-FB dataset.## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows:## BibTeX entry and citation info### Dataset Citation