---
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-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: 0.8622231253810201
    - name: F1
      type: f1
      value: 0.8623314280769628
    - name: Rouge1
      type: rouge
      value: 0.605873896307076
    - name: Bleu
      type: bleu
      value: 0.40472213589689604
---

<!-- 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-b48

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: 1.8720
- Accuracy: 0.8622
- F1: 0.8623
- Bertscore F1: 0.9329
- Rouge1: 0.6059
- Rouge2: 0.3988
- Rougel: 0.5475
- Rougelsum: 0.5496
- Bleu: 0.4047

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:|
| 1.5084        | 0.17  | 2000  | 1.7484          | 0.8001   | 0.7997 | 0.9271       | 0.5768 | 0.3695 | 0.5209 | 0.5229    | 0.3703 |
| 1.2745        | 0.35  | 4000  | 1.8137          | 0.8113   | 0.8110 | 0.9304       | 0.5881 | 0.3804 | 0.5305 | 0.5325    | 0.3853 |
| 1.2287        | 0.52  | 6000  | 1.8358          | 0.8392   | 0.8403 | 0.9298       | 0.5828 | 0.3747 | 0.5282 | 0.5301    | 0.3778 |
| 1.1964        | 0.7   | 8000  | 1.8432          | 0.8430   | 0.8437 | 0.9326       | 0.5974 | 0.3905 | 0.5447 | 0.5462    | 0.3998 |
| 1.1674        | 0.87  | 10000 | 1.8567          | 0.8507   | 0.8485 | 0.9310       | 0.5947 | 0.3888 | 0.5383 | 0.5402    | 0.3892 |
| 1.1371        | 1.05  | 12000 | 1.8720          | 0.8622   | 0.8623 | 0.9329       | 0.6059 | 0.3988 | 0.5475 | 0.5496    | 0.4047 |


### Framework versions

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