Update README.md

README.md

@@ -18,18 +18,17 @@ We follow the standard fine-tuning a masked language model of [Huggingface’s N
 We used the models provided by HuggingFace v4.24.0, and Pytorch v1.13.0. 
 In particular, for training the model we used a batch size of 256, Adam optimizer, with a learning rate of 1e<sup>-5</sup>, and cross-entropy as a loss function. We trained the model for three epochs using a GPU NVIDIA Tesla V100 32GB SXM2.
 
-## Usage
-# Use a pipeline as a high-level helper from transformers import pipeline
+# Usage
+## Use a pipeline as a high-level helper from transformers import pipeline
 pipe = pipeline("fill-mask", model="citiusLTL/DisorBERT")
 
--------
-# Load model directly
+## Load model directly
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 
 tokenizer = AutoTokenizer.from_pretrained("citiusLTL/DisorBERT")
 model = AutoModelForMaskedLM.from_pretrained("citiusLTL/DisorBERT")
 
-## Paper
+# Paper
 
 For more details, refer to the paper [DisorBERT: A Double Domain Adaptation Model for Detecting Signs of Mental Disorders in Social Media](https://aclanthology.org/2023.acl-long.853/).