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---
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
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