DReAMy-lib/t5-base-DreamBank-Generation-Act-Char

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t5-base-DreamBank-Generation-Act-Char

This model is a fine-tuned version of 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

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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 10
• —mixedprecisiontraining: Native AMP

Training results

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:

@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}
}