---
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