--- 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](https://huggingface.co/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)](https://github.com/DunnBC22/NLP_Projects/raw/main/Binary%20Classification/Mental%20Health%20Classification/Images/Input%20Word%20Length.png) _Class Distribution:_ ![Class Distribution](https://github.com/DunnBC22/NLP_Projects/raw/main/Binary%20Classification/Mental%20Health%20Classification/Images/Class%20Distribution.png) ## 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