transformers pytorch t5 text2text-generation generated_from_trainer summarization dataset:billsum base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: t5-small
datasets:
- billsum
license: apache-2.0
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: my_awesome_billsum_model
  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: 0.145
      name: Rouge1
---

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# my_awesome_billsum_model

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: 2.4873
- Rouge1: 0.145
- Rouge2: 0.0555
- Rougel: 0.1223
- Rougelsum: 0.1222
- 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 62   | 2.7886          | 0.1278 | 0.0364 | 0.1079 | 0.1081    | 19.0    |
| No log        | 2.0   | 124  | 2.5701          | 0.1355 | 0.0458 | 0.114  | 0.1139    | 19.0    |
| No log        | 3.0   | 186  | 2.5059          | 0.1461 | 0.0565 | 0.1231 | 0.1228    | 19.0    |
| No log        | 4.0   | 248  | 2.4873          | 0.145  | 0.0555 | 0.1223 | 0.1222    | 19.0    |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3