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xlm-roberta-large-DreamBank-emotion-presence

Text Classification
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PyTorch
Safetensors
xlm-roberta
Generated from Trainer
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metadata
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

xlm-roberta-large-DreamBank

This model is a fine-tuned version of 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.

@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}
}