metadata
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v45
results: []
text_shortening_model_v45
This model is a fine-tuned version of facebook/bart-large-xsum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 26.8982
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Bert precision: 0.6649
- Bert recall: 0.672
- Average word count: 1.0
- Max word count: 1
- Min word count: 1
- Average token count: 62.0
- % shortened texts with length > 12: 0.0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3.3791 | 1.0 | 83 | 6.7318 | 0.0982 | 0.0 | 0.0972 | 0.0969 | 0.6855 | 0.6599 | 1.2937 | 2 | 1 | 16.7619 | 0.0 |
| 2.8727 | 2.0 | 166 | 10.3841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.674 | 0.6911 | 3.0 | 3 | 3 | 62.0 | 0.0 |
| 2.7805 | 3.0 | 249 | 10.0261 | 0.0345 | 0.0 | 0.0346 | 0.0345 | 0.6746 | 0.6819 | 2.0 | 2 | 2 | 62.0 | 0.0 |
| 2.7183 | 4.0 | 332 | 9.5191 | 0.0 | 0.0 | 0.0 | 0.0 | 0.673 | 0.6736 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.7086 | 5.0 | 415 | 10.4466 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6568 | 0.6648 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.6474 | 6.0 | 498 | 13.9665 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6641 | 0.6709 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.63 | 7.0 | 581 | 13.3621 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6457 | 0.6701 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5998 | 8.0 | 664 | 13.0602 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6618 | 0.6672 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5689 | 9.0 | 747 | 15.0760 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6591 | 0.6651 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5508 | 10.0 | 830 | 15.6936 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6649 | 0.6716 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5298 | 11.0 | 913 | 16.8446 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6604 | 0.6648 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5091 | 12.0 | 996 | 21.0673 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6721 | 0.6702 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5019 | 13.0 | 1079 | 25.5628 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6605 | 0.67 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.4826 | 14.0 | 1162 | 25.1203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6725 | 0.6666 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.4693 | 15.0 | 1245 | 26.8982 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6649 | 0.672 | 1.0 | 1 | 1 | 62.0 | 0.0 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3