transformers safetensors t5 text2text-generation generated_from_trainer dataset:bills-summarization base_model:t5-small license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- bills-summarization
metrics:
- rouge
model-index:
- name: ft-t5-with-dill-sum
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: billsum
      type: bills-summarization
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.1441
---

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# ft-t5-with-dill-sum

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: 2.5658
- Rouge1: 0.1441
- Rouge2: 0.0526
- Rougel: 0.1184
- Rougelsum: 0.1184
- 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: 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 31   | 3.0881          | 0.1395 | 0.0436 | 0.1155 | 0.1154    | 19.0    |
| No log        | 2.0   | 62   | 2.7574          | 0.1315 | 0.0394 | 0.109  | 0.1093    | 19.0    |
| No log        | 3.0   | 93   | 2.6284          | 0.1377 | 0.0465 | 0.1138 | 0.1139    | 19.0    |
| No log        | 4.0   | 124  | 2.5796          | 0.1428 | 0.0512 | 0.1177 | 0.1175    | 19.0    |
| No log        | 5.0   | 155  | 2.5658          | 0.1441 | 0.0526 | 0.1184 | 0.1184    | 19.0    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
