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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: xlm-roberta-large-DreamBank |
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results: [] |
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widget: |
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- text: >- |
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I dreamed that Hannah and Sue and I travelled back in time to meet her |
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parents. Weird. |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlm-roberta-large-DreamBank |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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Best result (loaded model) |
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- F1: 0.8621 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| No log | 1.0 | 185 | 0.5949 | 0.0 | 0.5 | 0.0 | |
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| No log | 2.0 | 370 | 0.3825 | 0.6052 | 0.7481 | 0.4595 | |
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| 0.476 | 3.0 | 555 | 0.2891 | 0.7403 | 0.8010 | 0.5730 | |
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| 0.476 | 4.0 | 740 | 0.2604 | 0.8425 | 0.8852 | 0.7081 | |
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| 0.476 | 5.0 | 925 | 0.2484 | 0.8504 | 0.8932 | 0.6649 | |
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| 0.1457 | 6.0 | 1110 | 0.3092 | 0.8352 | 0.8909 | 0.6703 | |
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| 0.1457 | 7.0 | 1295 | 0.2882 | 0.8546 | 0.8950 | 0.6919 | |
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| 0.1457 | 8.0 | 1480 | 0.3099 | 0.8549 | 0.9014 | 0.6865 | |
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| 0.0691 | 9.0 | 1665 | 0.3080 | 0.8548 | 0.9019 | 0.6811 | |
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| 0.0691 | 10.0 | 1850 | 0.2942 | 0.8621 | 0.9069 | 0.6973 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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### Cite |
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If you use the model, please cite the pre-print. |
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```bibtex |
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@misc{https://doi.org/10.48550/arxiv.2302.14828, |
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doi = {10.48550/ARXIV.2302.14828}, |
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url = {https://arxiv.org/abs/2302.14828}, |
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author = {Bertolini, Lorenzo and Elce, Valentina and Michalak, Adriana and Bernardi, Giulio and Weeds, Julie}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Automatic Scoring of Dream Reports' Emotional Content with Large Language Models}, |
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publisher = {arXiv}, |
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year = {2023}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
