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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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Rita: I'm so bloody tired. Falling asleep at work. :-( |
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Tina: I know what you mean. |
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Tina: I keep on nodding off at my keyboard hoping that the boss doesn't notice.. |
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Rita: The time just keeps on dragging on and on and on.... |
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Rita: I keep on looking at the clock and there's still 4 hours of this drudgery to go. |
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Tina: Times like these I really hate my work. |
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Rita: I'm really not cut out for this level of boredom. |
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Tina: Neither am I. |
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- text: | |
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Beatrice: I am in town, shopping. They have nice scarfs in the shop next to the church. Do you want one? |
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Leo: No, thanks |
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Beatrice: But you don't have a scarf. |
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Leo: Because I don't need it. |
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Beatrice: Last winter you had a cold all the time. A scarf could help. |
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Leo: I don't like them. |
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Beatrice: Actually, I don't care. You will get a scarf. |
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Leo: How understanding of you! |
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Beatrice: You were complaining the whole winter that you're going to die. I've had enough. |
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Leo: Eh. |
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- text: | |
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Jack: Cocktails later? |
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May: YES!!! |
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May: You read my mind... |
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Jack: Possibly a little tightly strung today? |
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May: Sigh... without question. |
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Jack: Thought so. |
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May: A little drink will help! |
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Jack: Maybe two! |
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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: 53.6163 |
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- name: Validation ROUGE-2 |
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type: rouge-2 |
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value: 28.914 |
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- name: Validation ROUGE-L |
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type: rougeL |
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value: 44.1443 |
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- name: Validation ROUGE-L Sum |
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type: rougeLsum |
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value: 49.2995 |
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--- |
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# Description |
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This model was trained by fine-tuning the [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) model using [these parameters](#training-parameters) and the [samsum dataset](https://huggingface.co/datasets/samsum). |
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## Development |
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- Jupyter Notebook: [Text Summarization With BART](https://github.com/adedamola26/text-summarization/blob/main/Text_Summarization_with_BART.ipynb) |
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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="adedamolade26/bart-finetuned-samsum") |
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conversation = '''Jack: Cocktails later? |
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May: YES!!! |
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May: You read my mind... |
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Jack: Possibly a little tightly strung today? |
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May: Sigh... without question. |
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Jack: Thought so. |
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May: A little drink will help! |
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Jack: Maybe two! |
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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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## References |
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Model Training process was adapted from Luis Fernando Torres's [Kaggle Notebook](https://www.kaggle.com/code/lusfernandotorres/text-summarization-with-large-language-models): 📝 Text Summarization with Large Language Models |
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