from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("CodeXRyu/meeting-summarizer-v2")
model = AutoModelForSeq2SeqLM.from_pretrained("CodeXRyu/meeting-summarizer-v2")

# Example usage
meeting_text = "Your meeting transcript here..."
inputs = tokenizer.encode(meeting_text, return_tensors="pt", max_length=256, truncation=True)
outputs = model.generate(inputs, max_length=64, num_beams=4, early_stopping=True)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(summary)

Training Configuration

Max Input Length: 256 tokens
Max Output Length: 64 tokens
Training: Fine-tuned on meeting summarization data

This model was trained for meeting summarization tasks.