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	---
	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