metadata
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v38
results: []
text_shortening_model_v38
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: 32.2806
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Bert precision: 0.6712
- Bert recall: 0.6737
- 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.9479 | 1.0 | 145 | 6.0655 | 0.1154 | 0.0035 | 0.0998 | 0.0997 | 0.6949 | 0.7234 | 7.8649 | 46 | 2 | 47.0901 | 8.4084 |
| 3.2977 | 2.0 | 290 | 7.9855 | 0.0026 | 0.0 | 0.0026 | 0.0026 | 0.6628 | 0.6805 | 3.0 | 3 | 3 | 62.0 | 0.0 |
| 2.7673 | 3.0 | 435 | 18.0330 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6716 | 0.677 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.7007 | 4.0 | 580 | 16.7534 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6617 | 0.6651 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.6519 | 5.0 | 725 | 19.3665 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6636 | 0.6599 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.6334 | 6.0 | 870 | 19.0112 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6583 | 0.6639 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5888 | 7.0 | 1015 | 20.8393 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6602 | 0.6737 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5665 | 8.0 | 1160 | 20.7588 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6503 | 0.6688 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.546 | 9.0 | 1305 | 23.6869 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6646 | 0.6703 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5334 | 10.0 | 1450 | 26.1563 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6693 | 0.6685 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5194 | 11.0 | 1595 | 26.2698 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6682 | 0.6743 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5152 | 12.0 | 1740 | 30.3763 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6582 | 0.6645 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.5005 | 13.0 | 1885 | 26.7690 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6693 | 0.6597 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.4942 | 14.0 | 2030 | 26.8399 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6655 | 0.6674 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.4766 | 15.0 | 2175 | 26.8788 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6689 | 0.671 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.4712 | 16.0 | 2320 | 29.2279 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6693 | 0.6669 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.46 | 17.0 | 2465 | 31.1020 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6675 | 0.6655 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.4493 | 18.0 | 2610 | 31.4642 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6655 | 0.6737 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.4419 | 19.0 | 2755 | 31.2733 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6593 | 0.6629 | 1.0 | 1 | 1 | 62.0 | 0.0 |
| 2.4323 | 20.0 | 2900 | 32.2806 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6712 | 0.6737 | 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