---
license: mit
tags:
- generated_from_trainer
datasets:
- it5/datasets
metrics:
- rouge
model-index:
- name: it5-efficient-small-el32-st_r2g-0.0003
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: it5/datasets st_r2g
      type: it5/datasets
      args: st_r2g
    metrics:
    - name: Rouge1
      type: rouge
      value: 30.0502
---

<!-- 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. -->

# it5-efficient-small-el32-st_r2g-0.0003

This model is a fine-tuned version of [stefan-it/it5-efficient-small-el32](https://huggingface.co/stefan-it/it5-efficient-small-el32) on the it5/datasets st_r2g dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6135
- Rouge1: 30.0502
- Rouge2: 11.5687
- Rougel: 26.5953
- Rougelsum: 27.0402
- Gen Len: 16.9578

## 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: 0.0003
- 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: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.1265        | 0.74  | 5000  | 2.7247          | 26.8378 | 9.3464  | 23.9521 | 24.2837   | 15.5914 |
| 2.8786        | 1.49  | 10000 | 2.6532          | 27.5869 | 10.0861 | 24.7406 | 25.0245   | 15.3272 |
| 2.6587        | 2.23  | 15000 | 2.6080          | 28.2336 | 10.5229 | 25.3053 | 25.6716   | 15.4338 |
| 2.664         | 2.98  | 20000 | 2.5630          | 28.6673 | 10.8421 | 25.7032 | 26.0245   | 15.6255 |
| 2.4896        | 3.72  | 25000 | 2.5679          | 28.842  | 10.885  | 25.6757 | 26.0633   | 16.1841 |
| 2.34          | 4.47  | 30000 | 2.5564          | 29.3246 | 11.1981 | 26.1637 | 26.5392   | 15.7826 |
| 2.2204        | 5.21  | 35000 | 2.5744          | 29.5545 | 11.3806 | 26.3237 | 26.6993   | 15.8374 |
| 2.2301        | 5.96  | 40000 | 2.5614          | 29.5872 | 11.4227 | 26.3139 | 26.7196   | 15.7213 |
| 2.1219        | 6.7   | 45000 | 2.5617          | 29.8256 | 11.3702 | 26.4156 | 26.8465   | 15.936  |
| 2.007         | 7.45  | 50000 | 2.6014          | 29.743  | 11.4336 | 26.38   | 26.772    | 15.7144 |
| 1.9398        | 8.19  | 55000 | 2.6080          | 29.9478 | 11.4801 | 26.5352 | 26.9746   | 15.9308 |
| 1.9426        | 8.94  | 60000 | 2.6022          | 30.097  | 11.5602 | 26.705  | 27.1092   | 15.8598 |
| 1.8853        | 9.68  | 65000 | 2.6138          | 30.1588 | 11.5823 | 26.6984 | 27.1371   | 15.803  |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3