Update README.md

README.md

@@ -9,32 +9,59 @@ tags:
 language:
 - it
 ---
+# Model Details
 
-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 the following datasets annotated with the presence of irony:
+## 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 **irony detection**:
+
+# Training Details
+
+## Training Data
 
 - [IronITA 2018](https://live.european-language-grid.eu/catalogue/corpus/7372)
 - [Sarcastic Hate Speech dataset](https://github.com/simonasnow/Sarcastic-Hate-Speech)
 - SENTIPOLC [2014](https://live.european-language-grid.eu/catalogue/corpus/7480)/[2016](https://live.european-language-grid.eu/catalogue/corpus/7479)
 
+## 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 IronITA test set obtaining the following results:
 
+## Metrics and Results
+
 - macro F1: 0.79
 - accuracy: 0.79
 - precision of positive class: 0.77
 - recall of positive class: 0.84
 - F1 of positive class: 0.80
 
-Training hyperparameters:
-
-- learning_rate: 2e-5
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam
-
-Framework versions:
+# Framework versions
 
 - Transformers 4.30.2
 - Pytorch 2.1.2
 - Datasets 2.19.0
-- Accelerate=0.30.0- 
+- Accelerate=0.30.0
+
+# How to use this model:
+```
+model = AutoModelForSequenceClassification.from_pretrained('aequa-tech/irony',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("")
+```