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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-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
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