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--- |
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pipeline_tag: summarization |
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datasets: |
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- 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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library_name: transformers |
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widget: |
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- text: | |
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John: Hey! I've been thinking about getting a PlayStation 5. Do you think it is worth it? |
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Dan: Idk man. R u sure ur going to have enough free time to play it? |
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John: Yeah, that's why I'm not sure if I should buy one or not. I've been working so much lately idk if I'm gonna be able to play it as much as I'd like. |
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- text: | |
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Sarah: Do you think it's a good idea to invest in Bitcoin? |
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Emily: I'm skeptical. The market is very volatile, and you could lose money. |
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Sarah: True. But there's also a high upside, right? |
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- text: | |
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Madison: Hello Lawrence are you through with the article? |
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Lawrence: Not yet sir. |
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Lawrence: But i will be in a few. |
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Madison: Okay. But make it quick. |
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Madison: The piece is needed by today |
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Lawrence: Sure thing |
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Lawrence: I will get back to you once i am through." |
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model-index: |
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- name: bart-finetuned-samsum |
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results: |
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- task: |
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name: Text Summarization |
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type: summarization |
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dataset: |
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name: SamSum |
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type: samsum |
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metrics: |
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- name: Validation ROUGE-1 |
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type: rouge-1 |
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value: 53.8804 |
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- name: Validation ROUGE-2 |
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type: rouge-2 |
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value: 29.2329 |
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- name: Validation ROUGE-L |
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type: rougeL |
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value: 44.774 |
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- name: Validation ROUGE-L Sum |
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type: rougeLsum |
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value: 49.8255 |
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- name: Test ROUGE-1 |
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type: rouge-1 |
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value: 52.8156 |
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- name: Test ROUGE-2 |
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type: rouge-2 |
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value: 28.1259 |
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- name: Test ROUGE-L |
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type: rougeL |
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value: 43.7147 |
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- name: Test ROUGE-L Sum |
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type: rougeLsum |
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value: 48.5712 |
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--- |
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# Description |
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This model is a specialized adaptation of the <b>facebook/bart-large-xsum</b>, fine-tuned for enhanced performance on dialogue summarization using the <b>SamSum</b> dataset. |
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## Development |
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- Kaggle Notebook: [Text Summarization with Large Language Models](https://www.kaggle.com/code/lusfernandotorres/text-summarization-with-large-language-models) |
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## Usage |
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```python |
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from transformers import pipeline |
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model = pipeline("summarization", model="luisotorres/bart-finetuned-samsum") |
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conversation = '''Sarah: Do you think it's a good idea to invest in Bitcoin? |
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Emily: I'm skeptical. The market is very volatile, and you could lose money. |
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Sarah: True. But there's also a high upside, right? |
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''' |
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model(conversation) |
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``` |
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## Training Parameters |
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```python |
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evaluation_strategy = "epoch", |
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save_strategy = 'epoch', |
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load_best_model_at_end = True, |
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metric_for_best_model = 'eval_loss', |
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seed = 42, |
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learning_rate=2e-5, |
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per_device_train_batch_size=4, |
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per_device_eval_batch_size=4, |
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gradient_accumulation_steps=2, |
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weight_decay=0.01, |
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save_total_limit=2, |
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num_train_epochs=4, |
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predict_with_generate=True, |
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fp16=True, |
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report_to="none" |
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``` |
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## Reference |
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This model is based on the original <b>BART</b> architecture, as detailed in: |
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Lewis et al. (2019). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. [arXiv:1910.13461](https://arxiv.org/abs/1910.13461) |
