Create README.md

README.md

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+---
+language:
+- id
+tags:
+- indobert
+- indobenchmark
+- indonlu
+---
+This is the second classification of sentiment analysis for (redacted) task
+
+### How to import
+```python
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer, BertConfig, pipeline
+
+# Load the tokenizer and model
+tokenizer = BertTokenizer.from_pretrained("nfhakim/police-sentiment-c1-v2")
+config = BertConfig.from_pretrained("nfhakim/police-sentiment-c1-v2")
+model = BertForSequenceClassification.from_pretrained("nfhakim/police-sentiment-c1-v2", config=config)
+```
+
+### How to use
+```python
+# Initialize the pipeline
+nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Define a function to handle input text
+def classify_text(text):
+    # Tokenize the text and truncate to the first 512 tokens if necessary
+    inputs = tokenizer(text, truncation=True, max_length=512, return_tensors="pt")
+
+    # Use the model to classify the text
+    results = nlp(inputs['input_ids'])
+    return results
+
+# Example usage
+input_text = "Your input text here"
+output = classify_text(input_text)
+print(output)
+
+```