---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
- bleu
model-index:
- name: T5-small_finetuned_billsum_subset_model_bs32_lr2e-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: 0.1887
    - name: Bleu
      type: bleu
      value: 0.0008
---

<!-- 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_bs32_lr2e-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: 1.9763
- Rouge1: 0.1887
- Rouge2: 0.0967
- Rougel: 0.1659
- Rougelsum: 0.1657
- Gen Len: 19.0
- Bleu: 0.0008

## 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: 32
- eval_batch_size: 32
- 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   | 31   | 1.9837          | 0.1873 | 0.0945 | 0.1635 | 0.1633    | 19.0    | 0.0007 |
| No log        | 2.0   | 62   | 1.9812          | 0.1884 | 0.0955 | 0.1652 | 0.1648    | 19.0    | 0.0007 |
| No log        | 3.0   | 93   | 1.9785          | 0.1866 | 0.0936 | 0.1636 | 0.1634    | 19.0    | 0.0007 |
| No log        | 4.0   | 124  | 1.9763          | 0.1887 | 0.0967 | 0.1659 | 0.1657    | 19.0    | 0.0008 |


### Framework versions

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