transformers safetensors mt5 text2text-generation summarization generated_from_trainer base_model:google/mt5-base base_model:finetune:google/mt5-base license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: google/mt5-base
library_name: transformers
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-finetuned-summarize
  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-finetuned-summarize

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.0           | 0.9875  | 79   | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 1.975   | 158  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 2.9625  | 237  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 3.95    | 316  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 4.9375  | 395  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 5.925   | 474  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 6.9125  | 553  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 7.9     | 632  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 8.8875  | 711  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 9.875   | 790  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 10.8625 | 869  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 11.85   | 948  | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 12.8375 | 1027 | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 13.825  | 1106 | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 14.8125 | 1185 | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 15.8    | 1264 | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 16.7875 | 1343 | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 17.775  | 1422 | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 18.7625 | 1501 | nan             | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.0           | 19.75   | 1580 | nan             | 0.0    | 0.0    | 0.0    | 0.0       |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
