t5-base-DreamBank-Generation-Emot-EmotNn

This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3559
• Rouge1: 0.8119
• Rouge2: 0.2070
• Rougel: 0.8102
• Rougelsum: 0.8115

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	0.5128	0.5154	0.0562	0.5072	0.5086
No log	2.0	48	0.3782	0.7132	0.0145	0.7127	0.7159
No log	3.0	72	0.3387	0.7872	0.1712	0.7745	0.7756
No log	4.0	96	0.3221	0.7804	0.1598	0.7754	0.7777
No log	5.0	120	0.3669	0.7453	0.1330	0.7403	0.7414
No log	6.0	144	0.3559	0.8119	0.2070	0.8102	0.8115
No log	7.0	168	0.3559	0.8047	0.1895	0.8036	0.8047
No log	8.0	192	0.3808	0.7967	0.1925	0.7934	0.7949
No log	9.0	216	0.3899	0.8047	0.2127	0.8030	0.8040
No log	10.0	240	0.3991	0.8096	0.2247	0.8068	0.8074

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}
}