Update README.md

README.md

@@ -33,7 +33,7 @@ HATE-ITA is a binary hate speech classification model for Italian social media t
 
 ## 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.
+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
 
@@ -43,6 +43,15 @@ This model is the fine-tuned version of the [XLM-T](https://arxiv.org/abs/2104.1
 
 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:
 ```