Update README.md

README.md

@@ -1,3 +1,75 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
+tags:
+- stereotype
+language:
+- it
+---
+# Stereotype detection at aequa-tech
+
+## Model Description
+
+- **Developed by:** [aequa-tech](https://aequa-tech.com/)
+- **Funded by:** [NGI-Search](https://www.ngi.eu/ngi-projects/ngi-search/)
+- **Language(s) (NLP):** Italian
+- **License:** apache-2.0
+- **Finetuned from model:** [AlBERTo](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alberto)
+
+This model is a fine-tuned version of [AlBERTo](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alberto) Italian model on **stereotypes detection**:
+
+# Training Details
+
+## Training Data
+
+- [HaSpeeDe 2020](https://live.european-language-grid.eu/catalogue/corpus/7498)
+- [Sarcastic Hate Speech dataset](https://github.com/simonasnow/Sarcastic-Hate-Speech)
+- Racial stereotypes corpus available upon request to the authors of [A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads](https://aclanthology.org/2023.findings-eacl.51.pdf)
+- [Debunker-Assistant corpus](https://github.com/AequaTech/DebunkerAssistant/tree/main/evaluation/training_datasets)
+
+## Training Hyperparameters
+
+- learning_rate: 2e-5
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam
+
+
+# Evaluation
+
+## Testing Data
+It was tested on HaSpeeDe test sets (tweets and news headlines) obtaining the following results:
+
+## Metrics and Results
+Tweets:
+- macro F1: 0.75
+- accuracy: 0.75
+- precision of positive class: 0.66
+- recall of positive class: 0.94
+- F1 of positive class: 0.78
+
+News Headlines:
+- macro F1: 0.72
+- accuracy: 0.77
+- precision of positive class: 0.73
+- recall of positive class: 0.52
+- F1 of positive class: 0.61
+
+# Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.1.2
+- Datasets 2.19.0
+- Accelerate 0.30.0
+
+# How to use this model:
+```
+model = AutoModelForSequenceClassification.from_pretrained('aequa-tech/stereotype-it',num_labels=2) 
+tokenizer = AutoTokenizer.from_pretrained("m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0") 
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+classifier("text")
+```