---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
  results: []
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/shresthasingh1506-vellore-institute-of-technology/huggingface/runs/lo1964uv)
# my_awesome_billsum_model

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5520
- Rouge1: 0.1374
- Rouge2: 0.0485
- Rougel: 0.1133
- Rougelsum: 0.1134
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 62   | 2.8420          | 0.1227 | 0.0341 | 0.1045 | 0.1047    | 19.0    |
| No log        | 2.0   | 124  | 2.6293          | 0.1335 | 0.0448 | 0.1106 | 0.1106    | 19.0    |
| No log        | 3.0   | 186  | 2.5683          | 0.1356 | 0.0482 | 0.1129 | 0.113     | 19.0    |
| No log        | 4.0   | 248  | 2.5520          | 0.1374 | 0.0485 | 0.1133 | 0.1134    | 19.0    |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1