gutenberg-bert-base-uncased

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}]