---
license: apache-2.0
tags:
- summarization
- english
- en
- mt5
- Abstractive Summarization
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: mt5-base-finetuned-english
  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-base-finetuned-english

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3271
- Rouge-1: 31.7
- Rouge-2: 11.83
- Rouge-l: 26.43
- Gen Len: 18.88
- Bertscore: 74.3

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.174         | 1.0   | 3125  | 3.5662          | 27.01   | 7.95    | 22.16   | 18.91   | 72.62     |
| 3.6577        | 2.0   | 6250  | 3.4304          | 28.84   | 9.09    | 23.64   | 18.87   | 73.32     |
| 3.4526        | 3.0   | 9375  | 3.3691          | 29.69   | 9.96    | 24.58   | 18.84   | 73.69     |
| 3.3091        | 4.0   | 12500 | 3.3368          | 30.38   | 10.32   | 25.1    | 18.9    | 73.9      |
| 3.2056        | 5.0   | 15625 | 3.3271          | 30.7    | 10.65   | 25.45   | 18.89   | 73.99     |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1