t5-base-DreamBank-Generation-NER-Char

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t5-base-DreamBank-Generation-NER-Char

This model is a fine-tuned version of t5-base on the DremBan dataset to detect which characters are present in a given report, following the Hall & Van de Castle (HVDC) framework. Please note that, during training: i) it was not specified to which features the characters were associated with; ii) in accordance with the HVDC system, the presence of the dreamer is not assessed.

It achieves the following results on the evaluation set:

• Loss: 0.4674
• Rouge1: 0.7853
• Rouge2: 0.6927
• Rougel: 0.7564
• Rougelsum: 0.7565

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: 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

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:

@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}
}