Update README.md

README.md

@@ -1,7 +1,6 @@
 This is the first classification of sentiment analysis for (redacted) task
 
-### How to use this code
-
+### How to import
 ```python
 import torch
 from transformers import BertForSequenceClassification, BertTokenizer, BertConfig
@@ -9,16 +8,14 @@ from transformers import BertForSequenceClassification, BertTokenizer, BertConfi
 tokenizer = BertTokenizer.from_pretrained("nfhakim/sentiment-analysis-c1")
 config = BertConfig.from_pretrained("nfhakim/sentiment-analysis-c1")
 model = BertForSequenceClassification.from_pretrained("nfhakim/sentiment-analysis-c1", config=config)
+```
 
-text = 'INSERT TEXT - MINIMUM 3 SENTENCES FOR BETTER PREDICITONS'
-subwords = tokenizer.encode(text)
-subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)
-
-i2w = {0: 'positive',  1: 'non-positive'}
-
-logits = model(subwords)[0]
-label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()
+### How to use
+```python
+from transformers import pipeline
+nlp = pipeline("text-classification", model="nfhakim/sentiment-analysis-c1")
 
-print(f'Text: {text} | Label : {i2w[label]} ({torch.nn.functional.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')
+results = nlp("Your input text here")
 ```
 
+