|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- esnli |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- rouge |
|
- bleu |
|
model-index: |
|
- name: t5-small-e-snli-generation-label_and_explanation-selected-b48 |
|
results: |
|
- task: |
|
name: Sequence-to-sequence Language Modeling |
|
type: text2text-generation |
|
dataset: |
|
name: esnli |
|
type: esnli |
|
config: plain_text |
|
split: validation |
|
args: plain_text |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8657793131477342 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8658628497423001 |
|
- name: Rouge1 |
|
type: rouge |
|
value: 0.6049779979620054 |
|
- name: Bleu |
|
type: bleu |
|
value: 0.4039391893498565 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# t5-small-e-snli-generation-label_and_explanation-selected-b48 |
|
|
|
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the esnli dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.9091 |
|
- Accuracy: 0.8658 |
|
- F1: 0.8659 |
|
- Bertscore F1: 0.9337 |
|
- Rouge1: 0.6050 |
|
- Rouge2: 0.3983 |
|
- Rougel: 0.5492 |
|
- Rougelsum: 0.5513 |
|
- Bleu: 0.4039 |
|
|
|
## 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: 0.001 |
|
- train_batch_size: 48 |
|
- eval_batch_size: 48 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.05 |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Bertscore F1 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:| |
|
| 1.7285 | 0.17 | 2000 | 1.9945 | 0.7799 | 0.7792 | 0.9249 | 0.5631 | 0.3517 | 0.5091 | 0.5116 | 0.3617 | |
|
| 1.3318 | 0.35 | 4000 | 1.9494 | 0.7980 | 0.7971 | 0.9295 | 0.5766 | 0.3656 | 0.5218 | 0.5234 | 0.3785 | |
|
| 1.2662 | 0.52 | 6000 | 1.8983 | 0.8322 | 0.8331 | 0.9289 | 0.5769 | 0.3656 | 0.5205 | 0.5225 | 0.3727 | |
|
| 1.2285 | 0.7 | 8000 | 1.9078 | 0.8391 | 0.8396 | 0.9313 | 0.5833 | 0.3734 | 0.5304 | 0.5321 | 0.3884 | |
|
| 1.1973 | 0.87 | 10000 | 1.9246 | 0.8485 | 0.8470 | 0.9303 | 0.5888 | 0.3782 | 0.5322 | 0.5339 | 0.3868 | |
|
| 1.1715 | 1.05 | 12000 | 1.9262 | 0.8561 | 0.8565 | 0.9331 | 0.6020 | 0.3950 | 0.5464 | 0.5479 | 0.4039 | |
|
| 1.1368 | 1.22 | 14000 | 1.9155 | 0.8621 | 0.8612 | 0.9313 | 0.6027 | 0.3918 | 0.5442 | 0.5463 | 0.3889 | |
|
| 1.1281 | 1.4 | 16000 | 1.9091 | 0.8658 | 0.8659 | 0.9337 | 0.6050 | 0.3983 | 0.5492 | 0.5513 | 0.4039 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.4 |
|
- Pytorch 2.0.0+cu117 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.2 |
|
|
