transformers tensorboard safetensors mt5 text2text-generation summarization generated_from_trainer base_model:google/mt5-small base_model:finetune:google/mt5-small license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: google/mt5-small
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-news-summary-kaggle
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mt5-small-finetuned-news-summary-kaggle

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: 2.6907
- Rouge1: 26.6547
- Rouge2: 10.1
- Rougel: 24.0137
- Rougelsum: 23.9999

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

https://www.kaggle.com/datasets/sunnysai12345/news-summary

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| No log        | 1.0   | 220  | 3.9956          | 14.9021 | 3.3744 | 13.4763 | 13.499    |
| 8.3183        | 2.0   | 440  | 3.1550          | 17.9472 | 5.9671 | 16.6974 | 16.6959   |
| 8.3183        | 3.0   | 660  | 2.8950          | 21.2665 | 7.4266 | 19.5041 | 19.4837   |
| 4.0457        | 4.0   | 880  | 2.8087          | 25.063  | 9.4484 | 22.746  | 22.7351   |
| 4.0457        | 5.0   | 1100 | 2.7375          | 25.5269 | 9.4299 | 23.0623 | 23.0075   |
| 3.6505        | 6.0   | 1320 | 2.7091          | 25.8308 | 9.3392 | 23.2001 | 23.1586   |
| 3.6505        | 7.0   | 1540 | 2.6949          | 26.2177 | 9.8536 | 23.5946 | 23.6358   |
| 3.5175        | 8.0   | 1760 | 2.6907          | 26.6547 | 10.1   | 24.0137 | 23.9999   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
