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---
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
If you use the model, please cite the pre-print.
```bibtex
@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}
}
``` |