---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: yingchuanong_582_team_summarization
  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: 0.2039
---

<!-- 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. -->

# yingchuanong_582_team_summarization

This model is a fine-tuned version of [t5-base](https://huggingface.co./t5-base) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8978
- Rouge1: 0.2039
- Rouge2: 0.1189
- Rougel: 0.1798
- Rougelsum: 0.1798
- Gen Len: 19.0

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 124  | 2.0176          | 0.2024 | 0.1102 | 0.175  | 0.1747    | 19.0    |
| No log        | 2.0   | 248  | 1.9361          | 0.2033 | 0.1146 | 0.1773 | 0.1771    | 19.0    |
| No log        | 3.0   | 372  | 1.9046          | 0.2038 | 0.1184 | 0.1792 | 0.1791    | 19.0    |
| No log        | 4.0   | 496  | 1.8978          | 0.2039 | 0.1189 | 0.1798 | 0.1798    | 19.0    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0