metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-DreamBank-Generation-Char
results: []
language:
- en
widget:
- text: >-
I'm in an auditorium. Susie S is concerned at her part in this disability
awareness spoof we are preparing. I ask, 'Why not do it? Lots of AB's
represent us in a patronizing way. Why shouldn't we represent ourselves in
a good, funny way?' I watch the video we all made. It is funny. I try to
sit on a folding chair. Some guy in front talks to me. Merle is in the
audience somewhere. [BL]
t5-base-DreamBank-Generation-Char
This model is a fine-tuned version of t5-base on the DB emotion classification. It achieves the following results on the evaluation set (please note they refer to best uploaded model):
- Loss: 0.3047
- Rouge1: 0.8609
- Rouge2: 0.7956
- Rougel: 0.8476
- Rougelsum: 0.8578
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 | 0.4863 | 0.7670 | 0.6655 | 0.7575 | 0.7634 |
| No log | 2.0 | 48 | 0.4284 | 0.6870 | 0.5207 | 0.6846 | 0.6875 |
| No log | 3.0 | 72 | 0.3541 | 0.7659 | 0.6742 | 0.7600 | 0.7625 |
| No log | 4.0 | 96 | 0.3211 | 0.8147 | 0.7251 | 0.7965 | 0.8078 |
| No log | 5.0 | 120 | 0.3103 | 0.8400 | 0.7747 | 0.8313 | 0.8371 |
| No log | 6.0 | 144 | 0.3220 | 0.8538 | 0.7867 | 0.8285 | 0.8515 |
| No log | 7.0 | 168 | 0.3047 | 0.8609 | 0.7956 | 0.8476 | 0.8578 |
| No log | 8.0 | 192 | 0.3106 | 0.8574 | 0.7836 | 0.8401 | 0.8509 |
| No log | 9.0 | 216 | 0.3054 | 0.8532 | 0.7857 | 0.8378 | 0.8481 |
| No log | 10.0 | 240 | 0.3136 | 0.8455 | 0.7789 | 0.8282 | 0.8432 |
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}
}