---
library_name: transformers
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit5-base_nli
  results: []
---

<!-- 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. -->

# vit5-base_nli

This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9361
- Accuracy: 0.7508
- Precision Macro: 0.7512
- Recall Macro: 0.7507
- F1 Macro: 0.7508
- F1 Weighted: 0.7508

## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
| 1.2042        | 1.0   | 72   | 1.0168          | 0.4772   | 0.5084          | 0.4784       | 0.4539   | 0.4536      |
| 1.0195        | 2.0   | 144  | 0.7645          | 0.6723   | 0.6767          | 0.6724       | 0.6711   | 0.6709      |
| 0.6388        | 3.0   | 216  | 0.6869          | 0.7126   | 0.7199          | 0.7121       | 0.7128   | 0.7127      |
| 0.5149        | 4.0   | 288  | 0.6967          | 0.7428   | 0.7453          | 0.7432       | 0.7427   | 0.7425      |
| 0.2882        | 5.0   | 360  | 0.7899          | 0.7375   | 0.7440          | 0.7375       | 0.7376   | 0.7374      |
| 0.2238        | 6.0   | 432  | 0.9740          | 0.7313   | 0.7398          | 0.7319       | 0.7300   | 0.7298      |
| 0.1326        | 7.0   | 504  | 1.0921          | 0.7344   | 0.7372          | 0.7350       | 0.7337   | 0.7335      |
| 0.096         | 8.0   | 576  | 1.2234          | 0.7366   | 0.7420          | 0.7361       | 0.7366   | 0.7366      |
| 0.0755        | 9.0   | 648  | 1.3014          | 0.7326   | 0.7355          | 0.7324       | 0.7332   | 0.7330      |
| 0.0505        | 10.0  | 720  | 1.3717          | 0.7397   | 0.7414          | 0.7395       | 0.7400   | 0.7399      |
| 0.0419        | 11.0  | 792  | 1.4521          | 0.7392   | 0.7429          | 0.7389       | 0.7394   | 0.7393      |
| 0.0301        | 12.0  | 864  | 1.5602          | 0.7428   | 0.7433          | 0.7428       | 0.7430   | 0.7429      |
| 0.0213        | 13.0  | 936  | 1.7194          | 0.7450   | 0.7457          | 0.7448       | 0.7450   | 0.7450      |
| 0.0171        | 14.0  | 1008 | 1.7975          | 0.7450   | 0.7475          | 0.7448       | 0.7449   | 0.7449      |
| 0.018         | 15.0  | 1080 | 1.7963          | 0.7525   | 0.7528          | 0.7525       | 0.7526   | 0.7526      |
| 0.0084        | 16.0  | 1152 | 1.8312          | 0.7512   | 0.7517          | 0.7512       | 0.7513   | 0.7513      |
| 0.0083        | 17.0  | 1224 | 1.8834          | 0.7525   | 0.7531          | 0.7526       | 0.7526   | 0.7525      |
| 0.0089        | 18.0  | 1296 | 1.9212          | 0.7561   | 0.7568          | 0.7561       | 0.7562   | 0.7561      |
| 0.0064        | 19.0  | 1368 | 1.9379          | 0.7508   | 0.7512          | 0.7507       | 0.7508   | 0.7508      |
| 0.0082        | 20.0  | 1440 | 1.9361          | 0.7508   | 0.7512          | 0.7507       | 0.7508   | 0.7508      |


### Framework versions

- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4