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bert-base-cased-DreamBank-emotion-presence

Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
Inference Endpoints
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
model-index:
  - name: bert-base-cased-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

bert-base-cased-DreamBank

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2697
  • F1: 0.8335
  • Roc Auc: 0.8761
  • Accuracy: 0.6703

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 0.5983 0.0330 0.5064 0.0162
No log 2.0 370 0.3939 0.6104 0.7317 0.4649
0.4638 3.0 555 0.3227 0.7572 0.8154 0.5568
0.4638 4.0 740 0.2852 0.7902 0.8412 0.5784
0.4638 5.0 925 0.2720 0.7982 0.8382 0.6270
0.1877 6.0 1110 0.2795 0.8144 0.8619 0.6541
0.1877 7.0 1295 0.2575 0.8147 0.8568 0.6541
0.1877 8.0 1480 0.2556 0.8204 0.8630 0.6595
0.0952 9.0 1665 0.2668 0.8321 0.8764 0.6703
0.0952 10.0 1850 0.2697 0.8335 0.8761 0.6703

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}
}