Update README.md

README.md

@@ -32,7 +32,7 @@ The *feel-it-italian-sentiment* model performs **sentiment analysis** on Italian
 
 ## Data
 
-Our data has been collected by annotating tweets from a broad range of topics. In total, we have 2037 tweets annotated with an emotion label. More details can be found in our paper (preprint available soon).
+Our data has been collected by annotating tweets from a broad range of topics. In total, we have 2037 tweets annotated with an emotion label. More details can be found in our paper (https://aclanthology.org/2021.wassa-1.8/).
 
 ## Performance
 
@@ -48,29 +48,10 @@ The results show that training on FEEL-IT can provide better results on the SENT
 ## Usage
 
 ```python
-import torch
-import numpy as np 
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-sentiment")
-model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-sentiment")
-
-sentence = 'Oggi sono proprio contento!'
-inputs = tokenizer(sentence, return_tensors="pt")
-
-# Call the model and get the logits
-labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
-outputs = model(**inputs, labels=labels)
-loss, logits = outputs[:2]
-logits = logits.squeeze(0)
-
-# Extract probabilities
-proba = torch.nn.functional.softmax(logits, dim=0)
-
-# Unpack the tensor to obtain negative and positive probabilities
-negative, positive = proba
-print(f"Probabilities: Negative {np.round(negative.item(),4)} - Positive {np.round(positive.item(),4)}")
+from transformers import pipeline
+classifier = pipeline("text-classification",model='MilaNLProc/feel-it-italian-sentiment',top_k=2)
+prediction = classifier("Oggi sono proprio contento!", )
+print(prediction)
 ```
 
 ## Citation