|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: led-base-16384-biolaysum-both-wiki_simple |
|
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. --> |
|
|
|
# led-base-16384-biolaysum-both-wiki_simple |
|
|
|
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.1579 |
|
- Rouge1: 0.4549 |
|
- Rouge2: 0.1558 |
|
- Rougel: 0.2432 |
|
- Rougelsum: 0.2434 |
|
|
|
## 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: 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: 4 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| |
|
| 2.2754 | 0.69 | 5000 | 2.2223 | 0.4507 | 0.1525 | 0.2422 | 0.2425 | |
|
| 2.0405 | 1.37 | 10000 | 2.1579 | 0.4549 | 0.1558 | 0.2432 | 0.2434 | |
|
| 1.9239 | 2.06 | 15000 | 2.1239 | 0.4543 | 0.1525 | 0.2396 | 0.2399 | |
|
| 1.9041 | 2.75 | 20000 | 2.1120 | 0.4575 | 0.1547 | 0.2413 | 0.2415 | |
|
| 1.7812 | 3.43 | 25000 | 2.1006 | 0.4532 | 0.1518 | 0.2400 | 0.2401 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.0 |
|
- Pytorch 1.13.1 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.12.1 |
|
|