metadata
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v35
results: []
text_shortening_model_v35
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: 2.1783
- Rouge1: 0.4993
- Rouge2: 0.2724
- Rougel: 0.4472
- Rougelsum: 0.4467
- Bert precision: 0.8744
- Bert recall: 0.8769
- Average word count: 8.6096
- Max word count: 20
- Min word count: 5
- Average token count: 14.97
- % shortened texts with length > 12: 3.6036
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.83 | 1.0 | 37 | 1.9642 | 0.457 | 0.2329 | 0.4049 | 0.4054 | 0.8677 | 0.8663 | 8.027 | 13 | 4 | 16.6607 | 2.7027 |
| 0.8629 | 2.0 | 74 | 1.6943 | 0.5268 | 0.3019 | 0.4695 | 0.4697 | 0.8758 | 0.8901 | 10.0571 | 19 | 5 | 17.8258 | 19.2192 |
| 0.7849 | 3.0 | 111 | 1.6564 | 0.5001 | 0.279 | 0.4553 | 0.4554 | 0.873 | 0.8805 | 8.9099 | 17 | 5 | 15.4865 | 5.1051 |
| 0.6116 | 4.0 | 148 | 1.7559 | 0.4638 | 0.2376 | 0.4183 | 0.4188 | 0.863 | 0.8665 | 8.4414 | 15 | 4 | 13.8829 | 0.9009 |
| 0.3976 | 5.0 | 185 | 1.6708 | 0.4999 | 0.2723 | 0.4481 | 0.4481 | 0.8744 | 0.8766 | 8.5556 | 16 | 5 | 14.6877 | 3.9039 |
| 0.2977 | 6.0 | 222 | 1.7196 | 0.4937 | 0.2699 | 0.4376 | 0.4379 | 0.8684 | 0.877 | 9.1652 | 20 | 5 | 15.3964 | 5.7057 |
| 0.2187 | 7.0 | 259 | 1.7942 | 0.5129 | 0.2905 | 0.4572 | 0.4575 | 0.8765 | 0.8803 | 8.7117 | 19 | 5 | 14.6306 | 3.9039 |
| 0.1603 | 8.0 | 296 | 1.8003 | 0.4822 | 0.2538 | 0.4237 | 0.4229 | 0.8688 | 0.8722 | 8.6306 | 19 | 5 | 15.4474 | 5.7057 |
| 0.1175 | 9.0 | 333 | 2.0138 | 0.5024 | 0.2798 | 0.4486 | 0.4475 | 0.8742 | 0.8791 | 8.7988 | 19 | 5 | 16.1471 | 6.6066 |
| 0.0859 | 10.0 | 370 | 2.1783 | 0.4993 | 0.2724 | 0.4472 | 0.4467 | 0.8744 | 0.8769 | 8.6096 | 20 | 5 | 14.97 | 3.6036 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3