|
--- |
|
license: apache-2.0 |
|
base_model: google/flan-t5-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: flan-t5-base-samsum |
|
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. --> |
|
|
|
# flan-t5-base-samsum |
|
|
|
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.3717 |
|
- Rouge1: 47.4483 |
|
- Rouge2: 23.6821 |
|
- Rougel: 40.0391 |
|
- Rougelsum: 43.5912 |
|
- Gen Len: 17.0745 |
|
|
|
## 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: 5e-05 |
|
- 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: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| 1.4543 | 1.0 | 1842 | 1.3866 | 46.7875 | 22.9635 | 39.0803 | 42.9982 | 17.5531 | |
|
| 1.3399 | 2.0 | 3684 | 1.3731 | 47.3389 | 24.0053 | 39.9638 | 43.7068 | 17.3297 | |
|
| 1.2788 | 3.0 | 5526 | 1.3717 | 47.4483 | 23.6821 | 40.0391 | 43.5912 | 17.0745 | |
|
| 1.2239 | 4.0 | 7368 | 1.3752 | 47.658 | 24.1589 | 40.0986 | 43.9581 | 17.4676 | |
|
| 1.1997 | 5.0 | 9210 | 1.3755 | 47.3891 | 23.7333 | 39.8186 | 43.563 | 17.3932 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.0 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.0 |
|
- Tokenizers 0.19.1 |
|
|
