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

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
No log	1.0	93	0.6486	0.5936	0.4495	0.5705	0.5701
No log	2.0	186	0.5363	0.7196	0.6020	0.6990	0.6983
No log	3.0	279	0.4391	0.7568	0.6459	0.7235	0.7244
No log	4.0	372	0.4223	0.7751	0.6748	0.7473	0.7477
No log	5.0	465	0.4266	0.7789	0.6746	0.7512	0.7522
0.6336	6.0	558	0.4296	0.7810	0.6790	0.7537	0.7539
0.6336	7.0	651	0.4400	0.7798	0.6808	0.7537	0.7543
0.6336	8.0	744	0.4497	0.7749	0.6821	0.7471	0.7481
0.6336	9.0	837	0.4661	0.7828	0.6910	0.7554	0.7563
0.6336	10.0	930	0.4674	0.7853	0.6927	0.7564	0.7565

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}
}