transformers tensorboard safetensors mt5 text2text-generation generated_from_trainer base_model:google/mt5-small license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-summarize-ja
  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. -->

# mt5-summarize-ja

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0695
- Rouge1: 0.3667
- Rouge2: 0.1678
- Rougel: 0.2998
- Rougelsum: 0.3123

## 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: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 90
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.3241        | 0.7   | 100  | 2.4795          | 0.2943 | 0.1245 | 0.2472 | 0.2471    |
| 2.7583        | 1.4   | 200  | 2.2710          | 0.3054 | 0.1152 | 0.2539 | 0.2576    |
| 2.5469        | 2.1   | 300  | 2.2936          | 0.3446 | 0.1493 | 0.2808 | 0.2887    |
| 2.5335        | 2.8   | 400  | 2.1913          | 0.3228 | 0.1270 | 0.2665 | 0.2725    |
| 2.4383        | 3.5   | 500  | 2.1507          | 0.3630 | 0.1671 | 0.3082 | 0.3144    |
| 2.3671        | 4.2   | 600  | 2.1338          | 0.3388 | 0.1493 | 0.2814 | 0.2880    |
| 2.349         | 4.9   | 700  | 2.1089          | 0.3621 | 0.1576 | 0.2980 | 0.3079    |
| 2.264         | 5.6   | 800  | 2.1353          | 0.3740 | 0.1784 | 0.3083 | 0.3157    |
| 2.1577        | 6.3   | 900  | 2.1101          | 0.3711 | 0.1716 | 0.3107 | 0.3166    |
| 2.1315        | 7.0   | 1000 | 2.0905          | 0.3862 | 0.1826 | 0.3198 | 0.3269    |
| 2.1418        | 7.7   | 1100 | 2.0893          | 0.3433 | 0.1621 | 0.2895 | 0.2963    |
| 2.0744        | 8.4   | 1200 | 2.0881          | 0.3778 | 0.1834 | 0.3130 | 0.3242    |
| 2.0944        | 9.1   | 1300 | 2.0709          | 0.3676 | 0.1688 | 0.3024 | 0.3140    |
| 2.1015        | 9.8   | 1400 | 2.0695          | 0.3667 | 0.1678 | 0.2998 | 0.3123    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
