metadata
license: apache-2.0
base_model: google/flan-t5-small
tags:
- summarization
- meeting-summarization
- text-generation-inference
- transformers
datasets:
- qmsum
language:
- en
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
This model was trained for meeting summarization tasks.