|
--- |
|
license: apache-2.0 |
|
base_model: sshleifer/distilbart-xsum-12-6 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: bart-abs-1509-0313-lr-3e-05-bs-4-maxep-6 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# bart-abs-1509-0313-lr-3e-05-bs-4-maxep-6 |
|
|
|
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.3615 |
|
- Rouge/rouge1: 0.4759 |
|
- Rouge/rouge2: 0.2278 |
|
- Rouge/rougel: 0.407 |
|
- Rouge/rougelsum: 0.4084 |
|
- Bertscore/bertscore-precision: 0.898 |
|
- Bertscore/bertscore-recall: 0.8937 |
|
- Bertscore/bertscore-f1: 0.8957 |
|
- Meteor: 0.4311 |
|
- Gen Len: 37.0 |
|
|
|
## 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: 3e-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 6 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:| |
|
| 2.3606 | 1.0 | 217 | 2.0792 | 0.44 | 0.2028 | 0.3696 | 0.3707 | 0.8966 | 0.8883 | 0.8923 | 0.3866 | 37.3636 | |
|
| 1.6544 | 2.0 | 434 | 1.9952 | 0.4521 | 0.2171 | 0.3902 | 0.3909 | 0.8991 | 0.8912 | 0.895 | 0.4007 | 34.6636 | |
|
| 1.2907 | 3.0 | 651 | 2.0614 | 0.4661 | 0.2212 | 0.399 | 0.4008 | 0.9006 | 0.8929 | 0.8966 | 0.4128 | 35.2636 | |
|
| 1.0179 | 4.0 | 868 | 2.1396 | 0.479 | 0.2295 | 0.4121 | 0.4137 | 0.9024 | 0.8933 | 0.8977 | 0.4142 | 34.7182 | |
|
| 0.8112 | 5.0 | 1085 | 2.2658 | 0.4737 | 0.2237 | 0.4046 | 0.405 | 0.8989 | 0.8931 | 0.8959 | 0.4126 | 35.4273 | |
|
| 0.6745 | 6.0 | 1302 | 2.3615 | 0.4759 | 0.2278 | 0.407 | 0.4084 | 0.898 | 0.8937 | 0.8957 | 0.4311 | 37.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.0 |
|
- Pytorch 2.4.0 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
