---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-DreamBank-Generation-Char
  results: []
language:
- en
widget:
- text: "I'm in an auditorium. Susie S is concerned at her part in this disability awareness spoof we are preparing. I ask, 'Why not do it? Lots of AB's represent us in a patronizing way. Why shouldn't we represent ourselves in a good, funny way?' I watch the video we all made. It is funny. I try to sit on a folding chair. Some guy in front talks to me. Merle is in the audience somewhere. [BL]"
---

<!-- 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. -->

# t5-base-DreamBank-Generation-Char

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the DB emotion classification.
It achieves the following results on the evaluation set (please note they refer to best uploaded model):
- Loss:  0.3047 
- Rouge1: 0.8609
- Rouge2: 0.7956
- Rougel: 0.8476
- Rougelsum: 0.8578

## 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: 0.0002
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log        | 1.0   | 24   | 0.4863          | 0.7670 | 0.6655 | 0.7575 | 0.7634    |
| No log        | 2.0   | 48   | 0.4284          | 0.6870 | 0.5207 | 0.6846 | 0.6875    |
| No log        | 3.0   | 72   | 0.3541          | 0.7659 | 0.6742 | 0.7600 | 0.7625    |
| No log        | 4.0   | 96   | 0.3211          | 0.8147 | 0.7251 | 0.7965 | 0.8078    |
| No log        | 5.0   | 120  | 0.3103          | 0.8400 | 0.7747 | 0.8313 | 0.8371    |
| No log        | 6.0   | 144  | 0.3220          | 0.8538 | 0.7867 | 0.8285 | 0.8515    |
| No log        | 7.0   | 168  | 0.3047          | 0.8609 | 0.7956 | 0.8476 | 0.8578    |
| No log        | 8.0   | 192  | 0.3106          | 0.8574 | 0.7836 | 0.8401 | 0.8509    |
| No log        | 9.0   | 216  | 0.3054          | 0.8532 | 0.7857 | 0.8378 | 0.8481    |
| No log        | 10.0  | 240  | 0.3136          | 0.8455 | 0.7789 | 0.8282 | 0.8432    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1

# Cite 
Should you use our models in your work, please consider citing us as:
```bibtex
@article{BERTOLINI2024406,
title = {DReAMy: a library for the automatic analysis and annotation of dream reports with multilingual large language models},
journal = {Sleep Medicine},
volume = {115},
pages = {406-407},
year = {2024},
note = {Abstracts from the 17th World Sleep Congress},
issn = {1389-9457},
doi = {https://doi.org/10.1016/j.sleep.2023.11.1092},
url = {https://www.sciencedirect.com/science/article/pii/S1389945723015186},
author = {L. Bertolini and A. Michalak and J. Weeds}
}
```