transformers safetensors mt5 text2text-generation generated_from_trainer base_model:google/mt5-small base_model:finetune:google/mt5-small license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: google/mt5-small
library_name: transformers
license: apache-2.0
metrics:
- bleu
tags:
- generated_from_trainer
model-index:
- name: testing22_mt5-ha2zh-translation
  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. -->

# testing22_mt5-ha2zh-translation

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 15.2208
- Bleu: 0.0
- Ter: 112.1622
- Chrf: 0.0592
- Rougel: 0.0
- Meteor: 0.0
- Bertscore F1: 0.4861

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu | Ter      | Chrf   | Rougel | Meteor | Bertscore F1 |
|:-------------:|:-----:|:----:|:---------------:|:----:|:--------:|:------:|:------:|:------:|:------------:|
| 24.1484       | 1.0   | 63   | 17.4027         | 0.0  | 108.7838 | 0.0592 | 0.0    | 0.0    | 0.4856       |
| 24.1484       | 2.0   | 126  | 15.8128         | 0.0  | 109.7973 | 0.0592 | 0.0    | 0.0    | 0.4859       |
| 24.1484       | 3.0   | 189  | 15.2208         | 0.0  | 112.1622 | 0.0592 | 0.0    | 0.0    | 0.4861       |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
