File size: 1,387 Bytes
74d757e 227fafc 74d757e 890b687 74d757e 8f8153d be7c2d8 8f8153d 1d10b17 c120b2a 52d0c34 1d10b17 52d0c34 4a4f30b 52d0c34 4a4f30b 1d10b17 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 |
---
tags:
- summarization
datasets:
- gigaword
license: mit
thumbnail: https://en.wikipedia.org/wiki/Bart_Simpson#/media/File:Bart_Simpson_200px.png
---
# BART for Gigaword
- This model was created by fine-tuning the `facebook/bart-large-cnn` weights (also on HuggingFace) for the Gigaword dataset. The model was fine-tuned on the Gigaword training set for 3 epochs, and the model with the highest ROUGE-1 score on the training set batches was kept.
- The BART Tokenizer for CNN-Dailymail was used in the fine-tuning process and that is the tokenizer that will be loaded automatically when doing:
```
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("a1noack/bart-large-gigaword")
```
# Summary generation
- This model achieves ROUGE-1 / ROUGE-2 / ROUGE-L of 37.28 / 18.58 / 34.53 on the Gigaword test set; this is pretty good when compared to PEGASUS, `google/pegasus-gigaword`, which achieves 39.12 / 19.86 / 36.24.
- To achieve these results, generate text using the code below. `text_list` is a list of input text string.
```
input_ids_list = tokenizer(text_list, truncation=True, max_length=128,
return_tensors='pt', padding=True)['input_ids']
output_ids_list = model.generate(input_ids_list, min_length=0)
outputs_list = tokenizer.batch_decode(output_ids_list, skip_special_tokens=True,
clean_up_tokenization_spaces=False)
``` |