Text Classification
Transformers
Safetensors
bert
mentalbert
mental-health
stat3799
text-embeddings-inference
Instructions to use lkh125/stat3799-finetuned-mentalbert-denser-sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lkh125/stat3799-finetuned-mentalbert-denser-sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lkh125/stat3799-finetuned-mentalbert-denser-sample")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lkh125/stat3799-finetuned-mentalbert-denser-sample") model = AutoModelForSequenceClassification.from_pretrained("lkh125/stat3799-finetuned-mentalbert-denser-sample") - Notebooks
- Google Colab
- Kaggle
STAT3799 Finetuned MentalBERT With Denser Sample
This repository contains the trained model files for the STAT3799 project: An Ontology-Grounded Framework for Mental Health Disorder Screening.
These files correspond to the GitHub repository path:
Final_model/finetuned_mentalbert_with_denser_sample/finetuned_mentalbert/
To restore the project folder structure, download this repository's files and place them in that directory.
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