Create README.md

README.md

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+This is the first classification of sentiment analysis for (redacted) task
+
+### How to use this code
+
+```python
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer, BertConfig
+
+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 = 'Jakarta - Satnarkoba Polresta Bogor Kota mengungkap peredaran narkotika jenis ganja yang dikirim melalui jasa ekspedisi di Kota Bogor. Barang bukti 6 kilogram ganja yang diduga akan diedarkan pada malam tahun baru itu disita polisi.'
+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()
+
+print(f'Text: {text} | Label : {i2w[label]} ({torch.nn.functional.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')
+```
+