Text Classification
Transformers
PyTorch
Safetensors
English
roberta
depression
text-embeddings-inference
Instructions to use rafalposwiata/roberta-large-depression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rafalposwiata/roberta-large-depression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rafalposwiata/roberta-large-depression")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rafalposwiata/roberta-large-depression") model = AutoModelForSequenceClassification.from_pretrained("rafalposwiata/roberta-large-depression") - Notebooks
- Google Colab
- Kaggle
Fine-tuned RoBERTa model for detecting the level of depression as not depression, moderate or severe, based on social media posts in English.
Model was part of the winning solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI-ACL2022.
More information can be found in the following paper: OPI@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text using RoBERTa Pre-trained Language Models.
If you use this model, please cite:
@inproceedings{poswiata-perelkiewicz-2022-opi,
title = "{OPI}@{LT}-{EDI}-{ACL}2022: Detecting Signs of Depression from Social Media Text using {R}o{BERT}a Pre-trained Language Models",
author = "Po{\'s}wiata, Rafa{\l} and Pere{\l}kiewicz, Micha{\l}",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.40",
doi = "10.18653/v1/2022.ltedi-1.40",
pages = "276--282",
}
- Downloads last month
- 125
Model tree for rafalposwiata/roberta-large-depression
Base model
FacebookAI/roberta-large