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--- |
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base_model: google/pegasus-cnn_dailymail |
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model-index: |
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- name: pegasus-samsum |
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results: [] |
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datasets: |
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- Samsung/samsum |
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language: |
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- en |
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metrics: |
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- rouge |
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pipeline_tag: summarization |
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library_name: transformers |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# pegasus-samsum |
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This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on |
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[SAMSum](https://huggingface.co/datasets/Samsung/samsum) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3839 |
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# Intended uses & limitations |
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## Intended uses: |
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* Dialogue summarization (e.g., chat logs, meetings) |
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* Text summarization for conversational datasets |
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## Limitations: |
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* May struggle with very long conversations or non-dialogue text. |
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# Training procedure |
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## Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 2 |
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## Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.6026 | 0.5431 | 500 | 1.4875 | |
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| 1.4737 | 1.0861 | 1000 | 1.4040 | |
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| 1.4735 | 1.6292 | 1500 | 1.3839 | |
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### Test results |
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| rouge1 | rouge2 | rougeL | rougeLsum Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.427614 | 0.200571 | 0.340648 | 0.340738 | |
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## How to use |
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You can use this model with the transformers library for dialogue summarization. Here's an example in Python: |
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```python |
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from transformers import pipeline |
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import torch |
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device = 0 if torch.cuda.is_available() else -1 |
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pipe = pipeline("summarization", |
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model="seddiktrk/pegasus-samsum", |
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device=device) |
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custom_dialogue = """\ |
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Seddik: Hey, have you tried using PEGASUS for summarization? |
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John: Yeah, I just started experimenting with it last week! |
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Seddik: It's pretty powerful, especially for abstractive summaries. |
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John: I agree! The results are really impressive. |
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Seddik: I was thinking of using it for my next project. Want to collaborate? |
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John: Absolutely! We could make some awesome improvements together. |
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Seddik: Perfect, let's brainstorm ideas this weekend. |
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John: Sounds like a plan! |
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""" |
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# Summarize dialogue |
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gen_kwargs = {"length_penalty": 0.8, "num_beams":8, "max_length": 128} |
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print(pipe(custom_dialogue, **gen_kwargs)[0]["summary_text"]) |
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``` |
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Example Output |
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``` |
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John started using PEG for summarization last week. Seddik is thinking of using it for his next project. |
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John and Seddik will brainstorm ideas this weekend. |
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``` |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
