---
license: apache-2.0
tags:
- generated_from_trainer
- relation-extraction
metrics:
- rouge
model-index:
- name: t5-base-DreamBank-Generation-Act-Char
  results: []
language:
- en
inference:
  parameters:
    max_length: 128
widget:
- text: >-
    I was skating on the outdoor ice pond that used to be across the street from
    my house. I was not alone, but I did not recognize any of the other people
    who were skating around. I went through my whole repertoire of jumps,
    spires, and steps-some of which I can do and some of which I'm not yet sure
    of. They were all executed flawlessly-some I repeated, some I did only once.
    I seemed to know that if I went into competition, I would be sure of coming
    in third because there were only three contestants. Up to that time I hadn't
    considered it because I hadn't thought I was good enough, but now since
    everything was going so well, I decided to enter.
  example_title: Dream 1
- text: >-
    I was talking on the telephone to the father of an old friend of mine (boy,
    21 years old). We were discussing the party the Saturday night before to
    which I had invited his son as a guest. I asked him if his son had a good
    time at the party. He told me not to tell his son that he had told me, but
    that he had had a good time, except he was a little surprised that I had
    acted the way I did.
  example_title: Dream 2
- text: I was walking alone with my dog in a forest.
  example_title: Dream 3
---

<!-- 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-Act-Char

This model is a fine-tuned version of [DReAMy-lib/t5-base-DreamBank-Generation-NER-Char](https://huggingface.co/DReAMy-lib/t5-base-DreamBank-Generation-NER-Char) on the DreamBank dataset.
The uploaded model contains the weights of the best-performing model (see table below), tune to annotate a given
dream report according to [Hall and Van de Castle the Activity feature](https://dreams.ucsc.edu/Coding/activities.html)

## Model description

The model is trained end-to-end using a text2text solution to annotate dream reports following the Activity feature 
from the Hall and Van de Castle scoring framework. Given a report, the model generates texts of the form 
`(initialiser : activity type : receiver)`. For those cases where `initialiser` and `receiver` are the same 
entity, the output will follow the `(initialiser : alone activity type : none)` setting.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log        | 1.0   | 49   | 0.3674          | 0.4008 | 0.3122 | 0.3821 | 0.3812    |
| No log        | 2.0   | 98   | 0.3200          | 0.4240 | 0.3433 | 0.4130 | 0.4121    |
| No log        | 3.0   | 147  | 0.2845          | 0.4591 | 0.3883 | 0.4459 | 0.4455    |
| No log        | 4.0   | 196  | 0.2508          | 0.4614 | 0.3930 | 0.4504 | 0.4497    |
| No log        | 5.0   | 245  | 0.2632          | 0.4614 | 0.3929 | 0.4467 | 0.4459    |
| No log        | 6.0   | 294  | 0.2688          | 0.4706 | 0.4036 | 0.4537 | 0.4534    |
| No log        | 7.0   | 343  | 0.2790          | 0.4682 | 0.4043 | 0.4559 | 0.4556    |
| No log        | 8.0   | 392  | 0.2895          | 0.4670 | 0.3972 | 0.4529 | 0.4534    |
| No log        | 9.0   | 441  | 0.3058          | 0.4708 | 0.4040 | 0.4576 | 0.4572    |
| No log        | 10.0  | 490  | 0.3169          | 0.4690 | 0.4001 | 0.4547 | 0.4544    |


### Framework versions

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

# Cite
Should 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}
}
```