---
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

```python
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.*