---
library_name: transformers
license: apache-2.0
datasets:
- sartajbhuvaji/gutenberg
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---

```python
from transformers import EncoderDecoderModel, BertTokenizer
from datasets import load_dataset
from transformers import pipeline
import torch

# Load the encoder-decoder model and tokenizer from Hugging Face
encoder_decoder_model = EncoderDecoderModel.from_pretrained('sartajbhuvaji/gutenberg-bert-encoder-decoder')
tokenizer = BertTokenizer.from_pretrained("sartajbhuvaji/gutenberg-bert-encoder-decoder")

# Define the number of labels for your classification task
num_labels = 10 

# Load the custom classification head 
classification_model = EncoderDecoderForClassification(encoder_decoder_model, num_labels)
classification_model.load_state_dict(torch.load("gutenberg-classification-head.pth"))

# Now create a text classification pipeline
classifier = pipeline("text-classification", model=classification_model, tokenizer=tokenizer)

# Test the pipeline with a single sentence
result = classifier("This is a great book!")
print(result)

# Load sample dataset
dataset = load_dataset("sartajbhuvaji/gutenberg", split="100")
df = dataset.to_pandas()

# Test the pipeline on a document from a DataFrame (assuming `df` is a pandas DataFrame with text data)
doc_id = 1
doc_text = df.loc[df['DocID'] == doc_id, 'Text'].values[0]
result = classifier(doc_text[:1024])  
print(result)
```