|
--- |
|
base_model: google/pegasus-xsum |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: sumarize_model_pegasus_v1 |
|
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. --> |
|
|
|
# sumarize_model_pegasus_v1 |
|
|
|
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.3379 |
|
- Rouge1: 0.6034 |
|
- Rouge2: 0.4459 |
|
- Rougel: 0.5685 |
|
- Rougelsum: 0.5681 |
|
- Gen Len: 32.8647 |
|
|
|
## 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: 3.419313942464226e-05 |
|
- 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: 20 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
|
| No log | 1.0 | 239 | 1.4418 | 0.6747 | 0.5033 | 0.6338 | 0.6335 | 43.9549 | |
|
| No log | 2.0 | 478 | 1.3434 | 0.6869 | 0.5148 | 0.646 | 0.6459 | 44.938 | |
|
| 1.8531 | 3.0 | 717 | 1.2791 | 0.6843 | 0.5141 | 0.6451 | 0.645 | 44.7556 | |
|
| 1.8531 | 4.0 | 956 | 1.2358 | 0.6868 | 0.5168 | 0.6473 | 0.647 | 44.4305 | |
|
| 1.4419 | 5.0 | 1195 | 1.2654 | 0.6858 | 0.5172 | 0.6467 | 0.6464 | 43.7857 | |
|
| 1.4419 | 6.0 | 1434 | 1.2838 | 0.6686 | 0.4999 | 0.6291 | 0.6288 | 39.9549 | |
|
| 1.4368 | 7.0 | 1673 | 1.3379 | 0.6034 | 0.4459 | 0.5685 | 0.5681 | 32.8647 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
