metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: canine-c-Mental_Health_Classification
    results: []
pipeline_tag: text-classification
language:
  - en

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

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