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---
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:_
_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 |