Update README.md

README.md

@@ -1,3 +1,37 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+This BERT was fined-tuned on +672k tweets from twitter/X. The classification accuracy obtained is 98%. \
+The number of labels is 3: {0: Negative, 1: Neutral, 2: Positive}
+
+This is an example to use it
+```bash
+from transformers import AutoTokenizer
+from transformers import pipeline
+from transformers import AutoModelForSequenceClassification
+import torch
+
+checkpoint = 'kumo24/bert-sentiment'
+tokenizer=AutoTokenizer.from_pretrained(checkpoint)
+id2label = {0: "negative", 1: "neutral", 2: "positive"}
+label2id = {"negative": 0, "neutral": 1, "positive": 2}
+    
+
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+model = AutoModelForSequenceClassification.from_pretrained(checkpoint, 
+                                                       num_labels=3,
+                                                       id2label=id2label, 
+                                                       label2id=label2id)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+
+sentiment_task = pipeline("sentiment-analysis", 
+                          model=model, 
+                          tokenizer=tokenizer)
+
+print(sentiment_task("Michigan Wolverines are Champions, Go Blue!"))
+```