---
license: mit
tags:
- generated_from_trainer
datasets:
- it5/datasets
metrics:
- rouge
model-index:
- name: it5-efficient-small-el32-st_g2r-0.0003
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: it5/datasets st_g2r
      type: it5/datasets
      args: st_g2r
    metrics:
    - name: Rouge1
      type: rouge
      value: 29.8455
---

<!-- 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_g2r-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_g2r dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6892
- Rouge1: 29.8455
- Rouge2: 11.735
- Rougel: 26.6048
- Rougelsum: 26.8553
- Gen Len: 14.6131

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.2179        | 0.74  | 5000  | 2.7813          | 25.8006 | 9.3551  | 23.386  | 23.5287   | 13.5337 |
| 2.9248        | 1.49  | 10000 | 2.6914          | 27.0409 | 10.0228 | 24.4581 | 24.6197   | 13.243  |
| 2.6813        | 2.23  | 15000 | 2.6462          | 27.5333 | 10.3641 | 24.8696 | 25.0564   | 14.3052 |
| 2.691         | 2.98  | 20000 | 2.6205          | 28.3681 | 10.8961 | 25.5144 | 25.722    | 14.5279 |
| 2.5127        | 3.72  | 25000 | 2.6043          | 28.5979 | 11.0477 | 25.759  | 25.9605   | 14.0721 |
| 2.3331        | 4.47  | 30000 | 2.6283          | 28.9106 | 11.3727 | 25.9338 | 26.1387   | 14.4519 |
| 2.2034        | 5.21  | 35000 | 2.6400          | 29.099  | 11.2376 | 26.1221 | 26.3568   | 13.8715 |
| 2.2137        | 5.96  | 40000 | 2.6340          | 29.2641 | 11.3565 | 26.2012 | 26.4214   | 14.5981 |
| 2.1104        | 6.7   | 45000 | 2.6362          | 29.6204 | 11.6807 | 26.5976 | 26.8261   | 13.888  |
| 2.003         | 7.45  | 50000 | 2.6541          | 29.5679 | 11.6334 | 26.5095 | 26.7418   | 14.2246 |
| 1.8955        | 8.19  | 55000 | 2.6940          | 29.6748 | 11.5897 | 26.4862 | 26.7581   | 14.3902 |
| 1.912         | 8.94  | 60000 | 2.6883          | 29.7285 | 11.6448 | 26.5368 | 26.7806   | 14.3574 |
| 1.8581        | 9.68  | 65000 | 2.6874          | 29.7373 | 11.6532 | 26.4799 | 26.738    | 14.3821 |


### Framework versions

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