transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:billsum license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- billsum
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: TextSummarization
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: billsum
      type: billsum
      config: default
      split: ca_test
      args: default
    metrics:
    - type: rouge
      value: 0.203
      name: Rouge1
---

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# TextSummarization

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: 2.3754
- Rouge1: 0.203
- Rouge2: 0.1015
- Rougel: 0.1714
- Rougelsum: 0.1715
- 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: 4
- eval_batch_size: 4
- 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   | 248  | 2.5358          | 0.1376 | 0.0472 | 0.1131 | 0.113     | 19.0    |
| No log        | 2.0   | 496  | 2.4252          | 0.1994 | 0.0979 | 0.1688 | 0.1689    | 19.0    |
| 2.8754        | 3.0   | 744  | 2.3856          | 0.2027 | 0.1004 | 0.1709 | 0.171     | 19.0    |
| 2.8754        | 4.0   | 992  | 2.3754          | 0.203  | 0.1015 | 0.1714 | 0.1715    | 19.0    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
