---
license: apache-2.0
base_model: t5-base
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-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. -->

# t5-base-destination-inference

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1325
- Rouge1: 21.8
- Rouge2: 0.0
- Rougel: 21.8
- Rougelsum: 21.8

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.4532        | 1.0   | 250  | 1.8246          | 1.4    | 0.0    | 1.4    | 1.4       |
| 2.2781        | 2.0   | 500  | 1.5979          | 4.2    | 0.0    | 4.2    | 4.2       |
| 2.0678        | 3.0   | 750  | 1.4511          | 11.6   | 0.0    | 11.5   | 11.6      |
| 1.9165        | 4.0   | 1000 | 1.3417          | 14.8   | 0.0    | 14.8   | 14.8      |
| 1.7977        | 5.0   | 1250 | 1.2545          | 16.4   | 0.0    | 16.4   | 16.4      |
| 1.7429        | 6.0   | 1500 | 1.1880          | 20.8   | 0.0    | 20.8   | 20.8      |
| 1.6793        | 7.0   | 1750 | 1.1493          | 21.8   | 0.0    | 21.8   | 21.8      |
| 1.624         | 8.0   | 2000 | 1.1325          | 21.8   | 0.0    | 21.8   | 21.8      |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3