transformers pytorch mt5 text2text-generation summarization generated_from_trainer dataset:xlsum base_model:google/mt5-base base_model:finetune:google/mt5-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/mt5-base
datasets:
- xlsum
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-base-xlsum
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: xlsum
      type: xlsum
      config: ukrainian
      split: train
      args: ukrainian
    metrics:
    - type: rouge
      value: 2.98
      name: Rouge1
---

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# mt5-base-xlsum

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: 2.0396
- Rouge1: 2.98
- Rouge2: 0.1333
- Rougel: 3.0267
- Rougelsum: 2.9933

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 5.3745        | 1.0   | 500  | 2.5041          | 1.0696 | 0.13   | 1.062  | 1.0629    |
| 3.413         | 2.0   | 1000 | 2.2178          | 1.8333 | 0.1333 | 1.84   | 1.8633    |
| 3.1052        | 3.0   | 1500 | 2.0844          | 3.14   | 0.2667 | 3.18   | 3.1733    |
| 2.9673        | 4.0   | 2000 | 2.0396          | 2.98   | 0.1333 | 3.0267 | 2.9933    |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
