Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
depression
BERT
mental-health
Eval Results (legacy)
text-embeddings-inference
Instructions to use poudel/Depression_and_Non-Depression_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use poudel/Depression_and_Non-Depression_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="poudel/Depression_and_Non-Depression_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("poudel/Depression_and_Non-Depression_Classifier") model = AutoModelForSequenceClassification.from_pretrained("poudel/Depression_and_Non-Depression_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Bias, Risks, and Limitations
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### Recommendations
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## Bias, Risks, and Limitations
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The model may suffer from biases inherent in the dataset, such as overrepresenting certain language patterns. It is trained on social media posts, which may not capture all the nuances of real-world conversations about mental health
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### Recommendations
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