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yunu919/
mbart-large-dialogue-summarization

Name: mbart-large-dialogue-summarization
Rating: 2.5 (9 reviews)

dialogue-summarization

seq2seq

facebook/mbart-large-50-many-to-many-mmt

finetune

mit

endpoints_compatible

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mBART-50 Dialogue Summarization

This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt for English dialogue summarization.

It takes an English multi-speaker dialogue as input and generates an English abstractive summary.

Model Details

Base model: facebook/mbart-large-50-many-to-many-mmt
Task: Dialogue Summarization
Input language: English
Output language: English
Framework: Hugging Face Transformers
Model type: Seq2Seq Transformer
Fine-tuned by: Yunwoo Lee

Intended Use

This model is intended for:

English dialogue summarization
Research on abstractive summarization
Educational and experimental use with sequence-to-sequence models
Benchmarking or reproducing dialogue summarization pipelines

This model is not intended for high-stakes or production use without additional evaluation.

Training Data

The model was fine-tuned on a JSON-formatted dialogue summarization dataset with the following fields in the raw data:

dialogue
summary
summary_de
summary_zh

For this training run, only the following columns were used:

dialogue → source text
summary → target summary

After cleaning and filtering, the dataset sizes were:

Train: 14,730
Validation: 818
Test: 819

Preprocessing

The following preprocessing steps were applied:

Removed placeholder tokens such as <file_gif> and similar markers
Normalized whitespace
Preserved useful speaker information in the dialogue
Dropped empty or invalid rows after cleaning

Tokenization Setup

Source max length: 768
Target max length: 64
Source language code: en_XX
Target language code: en_XX

Training Procedure

The model was trained with the following configuration:

Epochs: 5
Learning rate: 2e-5
Optimizer: Adafactor
Per-device train batch size: 4
Gradient accumulation steps: 6
Effective batch size: 24
Per-device eval batch size: 4
Beam size: 5
Mixed precision: bf16
Best model selection metric: rougeLsum

Hardware

Training was run on:

GPU: NVIDIA A100-SXM4-40GB
Environment: Google Colab
PyTorch: 2.10.0+cu128

Evaluation

Evaluation was performed using ROUGE.

Validation Results

Metric	Score
ROUGE-1	50.9603
ROUGE-2	26.7346
ROUGE-L	42.1370
ROUGE-Lsum	46.6278
Loss	1.4443

Test Results

Metric	Score
ROUGE-1	48.9213
ROUGE-2	24.1386
ROUGE-L	40.3879
ROUGE-Lsum	44.7998
Loss	1.4613

Recommended Generation Settings

The model was evaluated using beam search.
For best results, use generation settings close to the evaluation setup:

num_beams=5
max_length=64
forced_bos_token_id=en_XX

How to Use

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "yunu919/mbart-large-dialogue-summarization"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

dialogue = """Hannah: Hey, do you have Betty's number?
Amanda: Lemme check
Amanda: Sorry, can't find it.
Amanda: Ask Larry"""

inputs = tokenizer(
    dialogue,
    return_tensors="pt",
    truncation=True,
    max_length=768,
)

outputs = model.generate(
    **inputs,
    num_beams=5,
    max_length=64,
    forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"],
)

summary = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(summary)