Update README.md

README.md

@@ -7,5 +7,36 @@ This model has been first pretrained on the BEIR corpus and fine-tuned on MS MAR
 This model is trained with BERT-base as the backbone with 110M hyperparameters.
 
 
+## Usage
+
+Pre-trained models can be loaded through the HuggingFace transformers library:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained("OpenMatch/cocodr-base-msmarco") 
+tokenizer = AutoTokenizer.from_pretrained("OpenMatch/cocodr-base-msmarco") 
+```
+
+Then embeddings for different sentences can be obtained by doing the following:
+
+```python
+
+sentences = [
+    "Where was Marie Curie born?",
+    "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
+    "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
+]
+
+inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
+embeddings = model(**inputs, output_hidden_states=True, return_dict=True).hidden_states[-1][:, :1].squeeze(1) # the embedding of the [CLS] token after the final layer
+```
+
+Then similarity scores between the different sentences are obtained with a dot product between the embeddings:
+```python
+
+score01 = embeddings[0] @ embeddings[1] # 216.9792
+score02 = embeddings[0] @ embeddings[2] # 216.6684
+```