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:

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