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canine-c-Mental_Health_Classification

This model is a fine-tuned version of google/canine-c on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2419
  • Accuracy: 0.9226
  • F1: 0.9096
  • Recall: 0.9079
  • Precision: 0.9113

Model description

This is a binary text classification model to distinguish between text that indicate potential mental health issue or not.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Binary%20Classification/Mental%20Health%20Classification/CANINE%20-%20Mental%20Health%20Classification.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/reihanenamdari/mental-health-corpus

Input Word Length:

Length of Input Text (in Words)

Class Distribution:

Class Distribution

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Recall Precision
0.3429 1.0 1101 0.2640 0.9037 0.8804 0.8258 0.9426
0.1923 2.0 2202 0.2419 0.9226 0.9096 0.9079 0.9113

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.12.1
  • Datasets 2.8.0
  • Tokenizers 0.12.1
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