Meeting Summarizer

This model is a fine-tuned version of google/flan-t5-small for meeting summarization tasks.

Model Details

• Base Model: google/flan-t5-small
• Task: Abstractive Meeting Summarization
• Training Data: QMSum Dataset + Enhanced Training
• Parameters: ~60.5M parameters
• Max Input Length: 256 tokens
• Max Output Length: 64 tokens

Usage

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

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This model was trained for meeting summarization tasks.