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---
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
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