| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - accuracy |
| model-index: |
| - name: xlm-roberta-large-DreamBank |
| results: [] |
| widget: |
| - text: >- |
| I dreamed that Hannah and Sue and I travelled back in time to meet her |
| parents. Weird. |
| pipeline_tag: text-classification |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # xlm-roberta-large-DreamBank |
|
|
| This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| Best result (loaded model) |
| - F1: 0.8621 |
|
|
| ## 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: 1e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
| | No log | 1.0 | 185 | 0.5949 | 0.0 | 0.5 | 0.0 | |
| | No log | 2.0 | 370 | 0.3825 | 0.6052 | 0.7481 | 0.4595 | |
| | 0.476 | 3.0 | 555 | 0.2891 | 0.7403 | 0.8010 | 0.5730 | |
| | 0.476 | 4.0 | 740 | 0.2604 | 0.8425 | 0.8852 | 0.7081 | |
| | 0.476 | 5.0 | 925 | 0.2484 | 0.8504 | 0.8932 | 0.6649 | |
| | 0.1457 | 6.0 | 1110 | 0.3092 | 0.8352 | 0.8909 | 0.6703 | |
| | 0.1457 | 7.0 | 1295 | 0.2882 | 0.8546 | 0.8950 | 0.6919 | |
| | 0.1457 | 8.0 | 1480 | 0.3099 | 0.8549 | 0.9014 | 0.6865 | |
| | 0.0691 | 9.0 | 1665 | 0.3080 | 0.8548 | 0.9019 | 0.6811 | |
| | 0.0691 | 10.0 | 1850 | 0.2942 | 0.8621 | 0.9069 | 0.6973 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.25.1 |
| - Pytorch 1.12.1 |
| - Datasets 2.5.1 |
| - Tokenizers 0.12.1 |
|
|
| ### Cite |
| Should use our models in your work, please consider citing us as: |
| ```bibtex |
| @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} |
| } |
| ``` |
