pegasus-samsum

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pegasus-samsum

This model is a fine-tuned version of google/pegasus-cnn_dailymail on SAMSum dataset. It achieves the following results on the evaluation set:

• Loss: 1.3839

Intended uses & limitations

Intended uses:

• Dialogue summarization (e.g., chat logs, meetings)
• Text summarization for conversational datasets

Limitations:

• May struggle with very long conversations or non-dialogue text.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 2

Training results

Test results

How to use

You can use this model with the transformers library for dialogue summarization. Here's an example in Python:

from transformers import pipeline
import torch

device = 0 if torch.cuda.is_available() else -1
pipe = pipeline("summarization",
                model="seddiktrk/pegasus-samsum",
                device=device)

custom_dialogue = """\
Seddik: Hey, have you tried using PEGASUS for summarization?
John: Yeah, I just started experimenting with it last week!
Seddik: It's pretty powerful, especially for abstractive summaries.
John: I agree! The results are really impressive.
Seddik: I was thinking of using it for my next project. Want to collaborate?
John: Absolutely! We could make some awesome improvements together.
Seddik: Perfect, let's brainstorm ideas this weekend.
John: Sounds like a plan!
"""

# Summarize dialogue
gen_kwargs = {"length_penalty": 0.8, "num_beams":8, "max_length": 128}
print(pipe(custom_dialogue, **gen_kwargs)[0]["summary_text"])

Example Output

John started using PEG for summarization last week. Seddik is thinking of using it for his next project.
John and Seddik will brainstorm ideas this weekend.

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1