Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- sartajbhuvaji/gutenberg
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+tags:
+- classification
+---
+
+```python
+from transformers import BertConfig, BertForSequenceClassification, BertTokenizer
+from datasets import load_dataset
+from transformers import pipeline
+import pandas as pd
+
+model = BertForSequenceClassification.from_pretrained("sartajbhuvaji/gutenberg-bert-base-uncased")
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Create a text classification pipeline
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, device='cuda')
+
+# Test the pipeline
+result = classifier("This is a great book!")
+print(result) #[{'label': 'LABEL_8', 'score': 0.2576160430908203}]
+
+# Test the pipeline on a document
+dataset = load_dataset("sartajbhuvaji/gutenberg", split="100")
+df = dataset.to_pandas()
+
+doc_id = 1
+doc_text = df.loc[df['DocID'] == doc_id, 'Text'].values[0]
+
+result = classifier(doc_text[:512])  # Truncate to 512 tokens
+print(result) # [{'label': 'LABEL_2', 'score': 0.28877997398376465}]
+```