---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
- bleu
model-index:
- name: T5-small_finetuned_billsum_subset_model_bs8_lr5e-05
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: billsum
      type: billsum
      config: default
      split: ca_test
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.1976
    - name: Bleu
      type: bleu
      value: 0.0009
---

<!-- 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_subset_model_bs8_lr5e-05

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: 2.1527
- Rouge1: 0.1976
- Rouge2: 0.0998
- Rougel: 0.1691
- Rougelsum: 0.1689
- Gen Len: 19.0
- Bleu: 0.0009

## 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: 5e-05
- 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu   |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|
| No log        | 1.0   | 124  | 2.2466          | 0.1991 | 0.0966 | 0.1693 | 0.169     | 19.0    | 0.001  |
| No log        | 2.0   | 248  | 2.1993          | 0.1944 | 0.0946 | 0.1654 | 0.1652    | 19.0    | 0.0009 |
| No log        | 3.0   | 372  | 2.1687          | 0.1944 | 0.0961 | 0.1675 | 0.1674    | 19.0    | 0.0009 |
| No log        | 4.0   | 496  | 2.1527          | 0.1976 | 0.0998 | 0.1691 | 0.1689    | 19.0    | 0.0009 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3