Update README.md

README.md

@@ -8,7 +8,22 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+<div align="center">
+  <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1>
+  <em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em>
+</div>
+
+<div style="margin-bottom: 2em"></div>
+
+<div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;">
+  <div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div>
+  <div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div>
+  <div><a href="https://github.com/McGill-NLP/WebLINX">💾Code</a></div>
+</div>
+
+<div style="margin-bottom: 2em"></div>
+
+# Sentence Transformers Details
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -28,7 +43,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('McGill-NLP/gte-base-dmr')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -54,8 +69,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained('McGill-NLP/gte-base-dmr')
+model = AutoModel.from_pretrained('McGill-NLP/gte-base-dmr')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -77,7 +92,7 @@ print(sentence_embeddings)
 
 <!--- Describe how your model was evaluated -->
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=McGill-NLP/gte-base-dmr)
 
 
 ## Training