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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- text-summarization |
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- meeting-summarization |
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- t5 |
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- transformers |
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- qmsum |
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datasets: |
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- qmsum |
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metrics: |
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- rouge |
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pipeline_tag: summarization |
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--- |
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# Meeting Summarizer |
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This model is a fine-tuned version of `t5-small` for meeting summarization tasks. |
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## Model Details |
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- **Base Model**: t5-small |
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- **Task**: Abstractive Meeting Summarization |
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- **Training Data**: QMSum Dataset + Enhanced Training |
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- **Parameters**: t5-small architecture |
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## Training Configuration |
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- **Max Input Length**: 256 tokens |
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- **Max Output Length**: 64 tokens |
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- **Batch Size**: 16 |
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- **Learning Rate**: 5e-05 |
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- **Training Epochs**: 1 |
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- **Training Samples**: N/A |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("CodeXRyu/meeting-summarizer") |
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model = AutoModelForSeq2SeqLM.from_pretrained("CodeXRyu/meeting-summarizer") |
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def generate_summary(meeting_text, max_length=150): |
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# Prepare input |
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input_text = "summarize: " + meeting_text |
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inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors="pt") |
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# Generate summary |
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summary_ids = model.generate( |
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inputs["input_ids"], |
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max_length=max_length, |
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num_beams=4, |
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length_penalty=2.0, |
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early_stopping=True |
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) |
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return tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
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# Example usage |
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meeting_transcript = ''' |
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John: Good morning team. Let's discuss our Q3 results. |
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Sarah: Our sales exceeded targets by 15%, reaching $2.1M in revenue. |
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Mike: The new marketing campaign was very effective. |
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John: Great work everyone. Let's plan for Q4. |
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''' |
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summary = generate_summary(meeting_transcript) |
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print(summary) |
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``` |
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## Training Data |
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This model was trained on the QMSum dataset, which contains real meeting transcripts from multiple domains: |
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- Academic meetings |
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- Product development meetings |
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- Committee meetings |
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## Performance |
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The model achieves competitive ROUGE scores on meeting summarization benchmarks. |
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## Limitations |
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- Optimized for English meeting transcripts |
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- Performance may vary on very long meetings (>512 tokens input) |
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- Best suited for structured meeting formats with speaker labels |
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## Citation |
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If you use this model, please cite: |
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``` |
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@misc{meeting-summarizer-codexryu, |
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author = {CodeXRyu}, |
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title = {Meeting Summarizer}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/CodeXRyu/meeting-summarizer} |
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} |
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``` |
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