|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- esnli |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- rouge |
|
- bleu |
|
model-index: |
|
- name: google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b64 |
|
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: 0.8691322901849218 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8686267742768865 |
|
- name: Rouge1 |
|
type: rouge |
|
value: 0.6062872493545299 |
|
- name: Bleu |
|
type: bleu |
|
value: 0.4012059786299585 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b64 |
|
|
|
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the esnli dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.8703 |
|
- Accuracy: 0.8691 |
|
- F1: 0.8686 |
|
- Bertscore F1: 0.9338 |
|
- Rouge1: 0.6063 |
|
- Rouge2: 0.3995 |
|
- Rougel: 0.5500 |
|
- Rougelsum: 0.5521 |
|
- Bleu: 0.4012 |
|
|
|
## 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: 64 |
|
- eval_batch_size: 64 |
|
- 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:| |
|
| 1.4692 | 0.23 | 2000 | 1.7872 | 0.8212 | 0.8203 | 0.9287 | 0.5787 | 0.3685 | 0.5239 | 0.5257 | 0.3856 | |
|
| 1.2505 | 0.47 | 4000 | 1.8808 | 0.8263 | 0.8264 | 0.9308 | 0.5870 | 0.3749 | 0.5321 | 0.5337 | 0.3904 | |
|
| 1.2003 | 0.7 | 6000 | 1.8477 | 0.8475 | 0.8481 | 0.9325 | 0.5984 | 0.3913 | 0.5452 | 0.5469 | 0.4004 | |
|
| 1.1624 | 0.93 | 8000 | 1.8244 | 0.8599 | 0.8587 | 0.9335 | 0.6029 | 0.3928 | 0.5441 | 0.5457 | 0.4024 | |
|
| 1.1155 | 1.16 | 10000 | 1.8499 | 0.8695 | 0.8688 | 0.9331 | 0.6083 | 0.4019 | 0.5519 | 0.5540 | 0.4022 | |
|
| 1.0913 | 1.4 | 12000 | 1.8703 | 0.8691 | 0.8686 | 0.9338 | 0.6063 | 0.3995 | 0.5500 | 0.5521 | 0.4012 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.4 |
|
- Pytorch 2.0.0+cu117 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.2 |
|
|
