| --- |
| license: apache-2.0 |
| tags: |
| - text-classification |
| - depression |
| - reddit |
| - generated_from_trainer |
| datasets: |
| - mrjunos/depression-reddit-cleaned |
| metrics: |
| - accuracy |
| widget: |
| - text: |
| - >- |
| i just found out my boyfriend is depressed i really want to be there for him |
| but i feel like i ve only been saying the wrong thing how can i be there for |
| him help him and see him get better i m worried it will continue to the |
| point it will consume him i can already see his personality changing and i m |
| scared for the future what thing can i say or do to comfort or help |
| example_title: depression |
| - text: |
| - >- |
| i m getting more and more people asking where they can buy the ambients |
| album simple answer is quot not yet quot it ll be on itunes eventually |
| example_title: not_depression |
| model-index: |
| - name: depression-reddit-distilroberta-base |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: mrjunos/depression-reddit-cleaned |
| type: depression-reddit-cleaned |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9715578539107951 |
| language: |
| - en |
| pipeline_tag: text-classification |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| ## Example Pipeline |
|
|
| ```python |
| from transformers import pipeline |
| predict_task = pipeline(model="mrjunos/depression-reddit-distilroberta-base", task="text-classification") |
| predict_task("Stop listing your issues here, use forum instead or open ticket.") |
| ``` |
| ``` |
| [{'label': 'not_depression', 'score': 0.9813856482505798}] |
| ``` |
|
|
| Disclaimer: This machine learning model classifies texts related to depression, but I am not an expert or a mental health professional. |
| I do not intend to diagnose or offer medical advice. The information provided should not replace consultation with a qualified professional. |
| The results may not be accurate. Use this model at your own risk and seek professional advice if needed. |
|
|
| This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the [mrjunos/depression-reddit-cleaned dataset](https://huggingface.co/datasets/mrjunos/depression-reddit-cleaned). |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0821 |
| - Accuracy: 0.9716 |
|
|
| ## Model description |
|
|
| This model is a transformer-based model that has been fine-tuned on a dataset of Reddit posts related to depression. |
| The model can be used to classify posts as either depression or not depression. |
|
|
| ## Intended uses & limitations |
|
|
| This model is intended to be used for research purposes. It is not yet ready for production use. |
| The model has been trained on a dataset of English-language posts, so it may not be accurate for other languages. |
|
|
| ## Training and evaluation data |
|
|
| The model was trained on the mrjunos/depression-reddit-cleaned dataset, which contains approximately 7,000 labeled instances. |
| The data was split into Train and Test using: |
| ```python |
| ds = ds['train'].train_test_split(test_size=0.2, seed=42) |
| ``` |
| The dataset consists of two main features: 'text' and 'label'. The 'text' feature contains the text data from Reddit posts related to depression, while the 'label' feature indicates whether a post is classified as depression or not. |
|
|
| ## Training procedure |
|
|
| You can find here the steps I followed to train this model: |
| https://github.com/mrjunos/machine_learning/blob/main/NLP-fine_tunning-hugging_face_model.ipynb |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.1711 | 0.65 | 500 | 0.0821 | 0.9716 | |
| | 0.1022 | 1.29 | 1000 | 0.1148 | 0.9709 | |
| | 0.0595 | 1.94 | 1500 | 0.1178 | 0.9787 | |
| | 0.0348 | 2.59 | 2000 | 0.0951 | 0.9851 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.30.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.13.0 |
| - Tokenizers 0.13.3 |
