---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-destination-inference
  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-destination-inference

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: 3.0350
- Rouge1: 4.0
- Rouge2: 0.0
- Rougel: 4.0
- Rougelsum: 4.0

## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 20.4559       | 1.0   | 250  | 9.7574          | 0.0    | 0.0    | 0.0    | 0.0       |
| 9.5672        | 2.0   | 500  | 4.8087          | 0.0    | 0.0    | 0.0    | 0.0       |
| 6.3205        | 3.0   | 750  | 3.9127          | 0.2    | 0.0    | 0.2    | 0.2       |
| 5.3432        | 4.0   | 1000 | 3.5081          | 1.4    | 0.0    | 1.4    | 1.4       |
| 4.9042        | 5.0   | 1250 | 3.3167          | 2.2    | 0.0    | 2.2    | 2.2       |
| 4.6882        | 6.0   | 1500 | 3.1341          | 3.2    | 0.0    | 3.2    | 3.2       |
| 4.538         | 7.0   | 1750 | 3.0736          | 4.0    | 0.0    | 4.0    | 4.0       |
| 4.4857        | 8.0   | 2000 | 3.0350          | 4.0    | 0.0    | 4.0    | 4.0       |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3