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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
If you use the model, please cite the pre-print.
@misc{https://doi.org/10.48550/arxiv.2302.14828,
doi = {10.48550/ARXIV.2302.14828},
url = {https://arxiv.org/abs/2302.14828},
author = {Bertolini, Lorenzo and Elce, Valentina and Michalak, Adriana and Bernardi, Giulio and Weeds, Julie},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Automatic Scoring of Dream Reports' Emotional Content with Large Language Models},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}
}
