---
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-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: 0.8732981101402154
    - name: F1
      type: f1
      value: 0.8729633394714756
    - name: Rouge1
      type: rouge
      value: 0.6144211309547953
    - name: Bleu
      type: bleu
      value: 0.4223746159966924
---

<!-- 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-b64

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: 1.9257
- Accuracy: 0.8733
- F1: 0.8730
- Bertscore F1: 0.9356
- Rouge1: 0.6144
- Rouge2: 0.4096
- Rougel: 0.5592
- Rougelsum: 0.5611
- Bleu: 0.4224

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:|
| 1.6638        | 0.23  | 2000  | 2.0039          | 0.7883   | 0.7869 | 0.9274       | 0.5705 | 0.3601 | 0.5175 | 0.5192    | 0.3730 |
| 1.2998        | 0.47  | 4000  | 1.9378          | 0.8283   | 0.8293 | 0.9303       | 0.5861 | 0.3748 | 0.5310 | 0.5329    | 0.3854 |
| 1.2351        | 0.7   | 6000  | 1.8752          | 0.8431   | 0.8437 | 0.9321       | 0.5951 | 0.3880 | 0.5411 | 0.5430    | 0.3954 |
| 1.1948        | 0.93  | 8000  | 1.9346          | 0.8536   | 0.8529 | 0.9333       | 0.6018 | 0.3931 | 0.5451 | 0.5472    | 0.4006 |
| 1.1537        | 1.16  | 10000 | 1.8881          | 0.8654   | 0.8647 | 0.9332       | 0.6070 | 0.4023 | 0.5483 | 0.5506    | 0.4096 |
| 1.1298        | 1.4   | 12000 | 1.9265          | 0.8690   | 0.8685 | 0.9337       | 0.6053 | 0.3988 | 0.5507 | 0.5526    | 0.4093 |
| 1.1219        | 1.63  | 14000 | 1.9017          | 0.8713   | 0.8714 | 0.9332       | 0.6029 | 0.3941 | 0.5470 | 0.5489    | 0.4042 |
| 1.1088        | 1.86  | 16000 | 1.9257          | 0.8733   | 0.8730 | 0.9356       | 0.6144 | 0.4096 | 0.5592 | 0.5611    | 0.4224 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2