| --- |
| license: apache-2.0 |
| datasets: |
| - huuuyeah/meetingbank |
| language: |
| - en |
| metrics: |
| - rouge |
| base_model: |
| - google/bigbird-pegasus-large-bigpatent |
| pipeline_tag: summarization |
| library_name: transformers |
| --- |
| # MeetingScript |
|
|
| > A BigBird‐Pegasus model fine‑tuned for meeting transcription summarization on the MeetingBank dataset. |
|
|
| 📦 **Model Files** |
| - **Weights & config**: `pytorch_model.bin`, `config.json` |
| - **Tokenizer**: `tokenizer.json`, `tokenizer_config.json`, `merges.txt`, `special_tokens_map.json` |
| - **Generation defaults**: `generation_config.json` |
|
|
| 🔗 **Hub:** https://github.com/kevin0437/Meeting_scripts |
| |
| --- |
| |
| ## Model Description |
| |
| **MeetingScript** is a sequence‑to‑sequence model based on |
| [google/bigbird-pegasus-large-bigpatent](https://huggingface.co/google/bigbird-pegasus-large-bigpatent) |
| and fine‑tuned on the [MeetingBank](https://huggingface.co/datasets/huuuyeah/meetingbank) corpus of meeting transcripts paired with human‐written summaries. |
| It is designed to take long meeting transcripts (up to 4096 tokens) and produce concise, coherent summaries. |
| |
| --- |
| |
| ## Evaluation Results |
| |
| Evaluated on the held‑out test split of MeetingBank (≈ 600 transcripts), using beam search (4 beams, max_length=600): |
|
|
| | Metric | F1 Score (%) | |
| |-------------|-------------:| |
| | **ROUGE‑1** | 51.5556 | |
| | **ROUGE‑2** | 38.5378 | |
| | **ROUGE‑L** | 48.0786 | |
| | **ROUGE‑Lsum** | 48.0142 | |
|
|
| --- |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| import torch |
| |
| # 1) Load from the Hub |
| tokenizer = AutoTokenizer.from_pretrained("Shaelois/MeetingScript") |
| model = AutoModelForSeq2SeqLM.from_pretrained("Shaelois/MeetingScript") |
| |
| # 2) Summarize a long transcript |
| transcript = """ |
| Alice: Good morning everyone, let’s get started… |
| Bob: I updated the design mockups… |
| … (thousands of words) … |
| """ |
| inputs = tokenizer( |
| transcript, |
| max_length=4096, |
| truncation=True, |
| return_tensors="pt" |
| ).to("cuda") |
| |
| summary_ids = model.generate( |
| **inputs, |
| num_beams=4, |
| max_length=150, |
| early_stopping=True |
| ) |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
| print("📝 Summary:", summary) |
| ``` |
|
|
| --- |
|
|
| ## Training Data |
| Dataset: MeetingBank |
| Splits: Train (5000+), Validation (600+), Test (600+) |
| Preprocessing: Sliding‑window chunking for sequences > 4096 tokens |
