---
base_model: X-Wang/pruned-mt5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: pruned-mt5-small
  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. -->

# pruned-mt5-small

This model is a fine-tuned version of [X-Wang/pruned-mt5-small](https://huggingface.co/X-Wang/pruned-mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4431
- Bleu: 11.4084
- Gen Len: 16.1053

## 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: 0.0005
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 3.3446        | 0.07  | 2000  | 2.9103          | 10.3957 | 16.0567 |
| 2.8425        | 0.14  | 4000  | 2.8570          | 10.5695 | 16.1895 |
| 3.186         | 0.21  | 6000  | 2.8137          | 10.5958 | 16.1523 |
| 2.788         | 0.28  | 8000  | 2.7593          | 10.7553 | 16.0138 |
| 2.9075        | 0.35  | 10000 | 2.7266          | 10.9199 | 16.2016 |
| 3.0579        | 0.42  | 12000 | 2.7030          | 10.6    | 16.0496 |
| 2.3618        | 0.49  | 14000 | 2.6547          | 10.8026 | 16.0412 |
| 3.079         | 0.56  | 16000 | 2.6441          | 10.7945 | 16.1148 |
| 2.7597        | 0.63  | 18000 | 2.6244          | 10.5877 | 16.0507 |
| 2.8533        | 0.7   | 20000 | 2.6049          | 10.9986 | 16.1145 |
| 2.843         | 0.77  | 22000 | 2.5836          | 10.9173 | 16.0826 |
| 2.8268        | 0.84  | 24000 | 2.5685          | 10.8136 | 16.0516 |
| 2.7021        | 0.91  | 26000 | 2.5509          | 11.326  | 16.0554 |
| 3.338         | 0.98  | 28000 | 2.5289          | 11.1485 | 16.0333 |
| 2.7374        | 1.05  | 30000 | 2.5220          | 11.0166 | 16.0998 |
| 2.7996        | 1.12  | 32000 | 2.5077          | 11.1316 | 16.131  |
| 2.6897        | 1.19  | 34000 | 2.4994          | 11.0811 | 16.1139 |
| 2.4107        | 1.26  | 36000 | 2.4877          | 11.2641 | 16.142  |
| 2.7695        | 1.33  | 38000 | 2.4756          | 11.2135 | 16.0977 |
| 3.3271        | 1.41  | 40000 | 2.4658          | 11.3328 | 16.0953 |
| 2.2641        | 1.48  | 42000 | 2.4612          | 11.3065 | 16.0549 |
| 2.6594        | 1.55  | 44000 | 2.4556          | 11.2684 | 16.1371 |
| 2.7322        | 1.62  | 46000 | 2.4520          | 11.3739 | 16.1058 |
| 2.6824        | 1.69  | 48000 | 2.4462          | 11.3335 | 16.1043 |
| 2.3369        | 1.76  | 50000 | 2.4455          | 11.3851 | 16.1239 |
| 2.9537        | 1.83  | 52000 | 2.4430          | 11.4026 | 16.0858 |
| 2.3928        | 1.9   | 54000 | 2.4433          | 11.301  | 16.1129 |
| 2.4714        | 1.97  | 56000 | 2.4431          | 11.4084 | 16.1053 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.13.1
- Tokenizers 0.13.3