---
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: 6.6964
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 22.6841       | 1.0   | 125  | 10.6306         | 0.0703 | 0.0    | 0.0703 | 0.0703    |
| 15.0485       | 2.0   | 250  | 8.4524          | 0.3398 | 0.0    | 0.3398 | 0.3398    |
| 11.3726       | 3.0   | 375  | 7.4365          | 0.0    | 0.0    | 0.0    | 0.0       |
| 8.9705        | 4.0   | 500  | 6.5795          | 0.0    | 0.0    | 0.0    | 0.0       |
| 7.6846        | 5.0   | 625  | 6.3153          | 0.0    | 0.0    | 0.0    | 0.0       |
| 6.8562        | 6.0   | 750  | 6.5050          | 0.0    | 0.0    | 0.0    | 0.0       |
| 6.4222        | 7.0   | 875  | 6.6475          | 0.0    | 0.0    | 0.0    | 0.0       |
| 6.3944        | 8.0   | 1000 | 6.6964          | 0.0    | 0.0    | 0.0    | 0.0       |


### Framework versions

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