---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-samsum-en
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 42.3215
---

<!-- 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. -->

# t5-small-finetuned-samsum-en

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7863
- Rouge1: 42.3215
- Rouge2: 19.4644
- Rougel: 35.3715
- Rougelsum: 39.1274

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.2448        | 1.0   | 300  | 1.8993          | 39.5059 | 17.0654 | 32.9974 | 36.6153   |
| 2.0428        | 2.0   | 600  | 1.8499          | 40.0529 | 17.4367 | 33.4804 | 37.057    |
| 1.9626        | 3.0   | 900  | 1.8278          | 40.7994 | 17.918  | 34.0773 | 37.6219   |
| 1.8992        | 4.0   | 1200 | 1.8118          | 41.3782 | 18.5579 | 34.7794 | 38.4994   |
| 1.8429        | 5.0   | 1500 | 1.8006          | 41.8624 | 18.7592 | 34.9262 | 38.7019   |
| 1.8057        | 6.0   | 1800 | 1.7988          | 41.1316 | 18.5242 | 34.7271 | 38.2821   |
| 1.775         | 7.0   | 2100 | 1.7856          | 42.2036 | 19.3343 | 35.4442 | 39.2114   |
| 1.7376        | 8.0   | 2400 | 1.7797          | 41.9569 | 18.9482 | 35.1953 | 38.7609   |
| 1.7096        | 9.0   | 2700 | 1.7780          | 42.6065 | 19.2152 | 35.4563 | 39.2736   |
| 1.6885        | 10.0  | 3000 | 1.7826          | 42.1595 | 18.8477 | 34.8679 | 38.9388   |
| 1.6581        | 11.0  | 3300 | 1.7809          | 42.291  | 19.0846 | 35.1938 | 38.894    |
| 1.6392        | 12.0  | 3600 | 1.7824          | 42.3588 | 19.4507 | 35.4588 | 39.2067   |
| 1.6258        | 13.0  | 3900 | 1.7806          | 42.0932 | 19.002  | 35.0112 | 38.8053   |
| 1.6042        | 14.0  | 4200 | 1.7828          | 42.0564 | 19.3141 | 35.2479 | 38.8301   |
| 1.5993        | 15.0  | 4500 | 1.7824          | 42.6056 | 19.5164 | 35.4112 | 39.2322   |
| 1.5869        | 16.0  | 4800 | 1.7839          | 42.1505 | 19.1529 | 35.0853 | 38.8788   |
| 1.5778        | 17.0  | 5100 | 1.7827          | 42.5416 | 19.5103 | 35.5507 | 39.293    |
| 1.5716        | 18.0  | 5400 | 1.7865          | 42.3028 | 19.3783 | 35.3466 | 39.0594   |
| 1.5615        | 19.0  | 5700 | 1.7857          | 42.4001 | 19.5111 | 35.4686 | 39.1614   |
| 1.5606        | 20.0  | 6000 | 1.7863          | 42.3215 | 19.4644 | 35.3715 | 39.1274   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1