|
|
--- |
|
|
library_name: peft |
|
|
license: apache-2.0 |
|
|
base_model: t5-small |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- xsum |
|
|
model-index: |
|
|
- name: text-summarization-T5 |
|
|
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. --> |
|
|
|
|
|
# text-summarization-T5 |
|
|
|
|
|
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 2.6883 |
|
|
|
|
|
## 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: 2e-05 |
|
|
- train_batch_size: 16 |
|
|
- eval_batch_size: 16 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 8 |
|
|
- total_train_batch_size: 128 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 2 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|
|:-------------:|:------:|:----:|:---------------:| |
|
|
| 3.8764 | 0.0627 | 100 | 3.6376 | |
|
|
| 3.6129 | 0.1255 | 200 | 3.2631 | |
|
|
| 3.3392 | 0.1882 | 300 | 3.0248 | |
|
|
| 3.207 | 0.2509 | 400 | 2.9294 | |
|
|
| 3.1548 | 0.3137 | 500 | 2.8725 | |
|
|
| 3.0969 | 0.3764 | 600 | 2.8333 | |
|
|
| 3.0718 | 0.4391 | 700 | 2.8018 | |
|
|
| 3.0476 | 0.5018 | 800 | 2.7803 | |
|
|
| 3.0431 | 0.5646 | 900 | 2.7651 | |
|
|
| 3.0216 | 0.6273 | 1000 | 2.7538 | |
|
|
| 3.0003 | 0.6900 | 1100 | 2.7440 | |
|
|
| 3.0018 | 0.7528 | 1200 | 2.7363 | |
|
|
| 2.9993 | 0.8155 | 1300 | 2.7289 | |
|
|
| 2.9833 | 0.8782 | 1400 | 2.7236 | |
|
|
| 2.9827 | 0.9410 | 1500 | 2.7181 | |
|
|
| 2.9737 | 1.0037 | 1600 | 2.7145 | |
|
|
| 2.968 | 1.0664 | 1700 | 2.7107 | |
|
|
| 2.967 | 1.1291 | 1800 | 2.7074 | |
|
|
| 2.9709 | 1.1919 | 1900 | 2.7042 | |
|
|
| 2.9593 | 1.2546 | 2000 | 2.7011 | |
|
|
| 2.9628 | 1.3173 | 2100 | 2.6987 | |
|
|
| 2.9573 | 1.3801 | 2200 | 2.6969 | |
|
|
| 2.955 | 1.4428 | 2300 | 2.6947 | |
|
|
| 2.9483 | 1.5055 | 2400 | 2.6934 | |
|
|
| 2.9546 | 1.5683 | 2500 | 2.6923 | |
|
|
| 2.9492 | 1.6310 | 2600 | 2.6910 | |
|
|
| 2.9493 | 1.6937 | 2700 | 2.6903 | |
|
|
| 2.9482 | 1.7564 | 2800 | 2.6896 | |
|
|
| 2.9524 | 1.8192 | 2900 | 2.6890 | |
|
|
| 2.9399 | 1.8819 | 3000 | 2.6886 | |
|
|
| 2.9347 | 1.9446 | 3100 | 2.6883 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.14.0 |
|
|
- Transformers 4.44.2 |
|
|
- Pytorch 2.4.1+cu121 |
|
|
- Datasets 3.2.0 |
|
|
- Tokenizers 0.19.1 |
