---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-billsum-ca_test
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: billsum
      type: billsum
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 52.2582
---

<!-- 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-billsum-ca_test

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5234
- Rouge1: 52.2582
- Rouge2: 34.8162
- Rougel: 50.5491
- Rougelsum: 50.6121
- Gen Len: 18.996

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 495  | 1.8113          | 58.4024 | 41.7432 | 56.9521 | 57.0516   | 18.9597 |
| 2.709         | 2.0   | 990  | 1.6230          | 47.7769 | 32.1777 | 46.0344 | 46.046    | 18.996  |
| 1.9323        | 3.0   | 1485 | 1.5459          | 51.2371 | 33.8242 | 49.4532 | 49.5038   | 18.996  |
| 1.7842        | 4.0   | 1980 | 1.5234          | 52.2582 | 34.8162 | 50.5491 | 50.6121   | 18.996  |


### Framework versions

- Transformers 4.12.2
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3