Update README.md

README.md

@@ -5,4 +5,44 @@ language:
 - en
 tags:
 - depression
----
+- medical
+base_model:
+- rafalposwiata/deproberta-large-depression
+pipeline_tag: text-classification
+---
+
+# MentalBERTa
+
+This model, `MentalBERTa`, was developed by the DeepLearningBrasil team and secured the first position in the [DepSign-LT-EDI@RANLP-2023 shared task](https://arxiv.org/abs/2311.05047).
+The objective of the task was to classify social media texts into three distinct levels of depression: "not depressed," "moderately depressed," and "severely depressed".
+The accompanying code is available on [GitHub](https://github.com/eduagarcia/depsign-2023-ranlp).
+
+## Model Description
+
+`MentalBERTa` is a `RoBERTa` large model [from rafalposwiata/deproberta-large-depression](https://huggingface.co/rafalposwiata/deproberta-large-depression), pre-trained on a curated Reddit dataset from mental health-related communities. 
+This pre-training allows for an enhanced understanding of nuanced mental health discourse
+
+The best performing version of the model was trained with Loss Sample Weights and a 50% head + 50% tail truncation method.
+
+## Training Data
+
+The model was pre-trained on a custom dataset collected from mental health-related Subreddits, which is available on Hugging Face at [dlb/mentalreddit](https://huggingface.co/datasets/dlb/mentalreddit).
+The full pre-training dataset comprises 3.4 million comments from mental health-related subreddits and 3.2 million comments from other subreddites, occupying approximately 1.4 GB of text on disk.
+
+### Citation
+```bibtex
+@inproceedings{garcia-etal-2023-deeplearningbrasil,
+    title = "{D}eep{L}earning{B}rasil@{LT}-{EDI}-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text",
+    author = "Garcia, Eduardo  and
+      Gomes, Juliana  and
+      Barbosa Junior, Adalberto  and
+      Borges, Cardeque  and
+      da Silva, N{\'a}dia",
+    booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
+    month = sep,
+    year = "2023",
+    address = "Varna, Bulgaria",
+    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
+    url = "https://aclanthology.org/2023.ltedi-1.42",
+    pages = "272--278",
+}