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