---
pipeline_tag: summarization
datasets:
  - samsum
language:
  - en
metrics:
  - rouge
library_name: transformers
widget:
  - text: |
      Rita: I'm so bloody tired. Falling asleep at work. :-(
      Tina: I know what you mean.
      Tina: I keep on nodding off at my keyboard hoping that the boss doesn't notice..
      Rita: The time just keeps on dragging on and on and on.... 
      Rita: I keep on looking at the clock and there's still 4 hours of this drudgery to go.
      Tina: Times like these I really hate my work.
      Rita: I'm really not cut out for this level of boredom.
      Tina: Neither am I.
  - text: |
      Beatrice: I am in town, shopping. They have nice scarfs in the shop next to the church. Do you want one?
      Leo: No, thanks
      Beatrice: But you don't have a scarf.
      Leo: Because I don't need it.
      Beatrice: Last winter you had a cold all the time. A scarf could help.
      Leo: I don't like them.
      Beatrice: Actually, I don't care. You will get a scarf.
      Leo: How understanding of you!
      Beatrice: You were complaining the whole winter that you're going to die. I've had enough.
      Leo: Eh.
  - text: |
      Jack: Cocktails later?
      May: YES!!!
      May: You read my mind...
      Jack: Possibly a little tightly strung today?
      May: Sigh... without question.
      Jack: Thought so.
      May: A little drink will help!
      Jack: Maybe two!

model-index:
  - name: bart-finetuned-samsum
    results:
      - task:
          name: Text Summarization
          type: summarization
        dataset:
          name: SamSum
          type: samsum
        metrics:
          - name: Validation ROUGE-1
            type: rouge-1
            value: 53.6163
          - name: Validation ROUGE-2
            type: rouge-2
            value: 28.914
          - name: Validation ROUGE-L
            type: rougeL
            value: 44.1443
          - name: Validation ROUGE-L Sum
            type: rougeLsum
            value: 49.2995
---

# Description

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).

## Development

- Jupyter Notebook: [Text Summarization With BART](https://github.com/adedamola26/text-summarization/blob/main/Text_Summarization_with_BART.ipynb)

## Usage

```python
from transformers import pipeline

model = pipeline("summarization", model="adedamolade26/bart-finetuned-samsum")

conversation = '''Jack: Cocktails later?
May: YES!!!
May: You read my mind...
Jack: Possibly a little tightly strung today?
May: Sigh... without question.
Jack: Thought so.
May: A little drink will help!
Jack: Maybe two!
'''
model(conversation)
```

## Training Parameters

```python
evaluation_strategy = "epoch",
save_strategy = 'epoch',
load_best_model_at_end = True,
metric_for_best_model = 'eval_loss',
seed = 42,
learning_rate=2e-5,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=2,
weight_decay=0.01,
save_total_limit=2,
num_train_epochs=4,
predict_with_generate=True,
fp16=True,
report_to="none"
```

## References

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