Update README.md

README.md

@@ -2657,12 +2657,15 @@ Training data to train the models is released in its entirety. For more details,
 
 ## Usage
 
+Note `nomic-embed-text` requires prefixes! We support the prefixes `[search_query, search_document, classification, clustering]`.
+For retrieval applications, you should prepend `search_document` for all your documents and `search_query` for your queries.
+
 ### Sentence Transformers
 ```python
 from sentence_transformers import SentenceTransformer
 
 model = SentenceTransformer("nomic-ai/nomic-embed-text-v1-ablated", trust_remote_code=True)
-sentences = ['What is TSNE?', 'Who is Laurens van der Maaten?']
+sentences = ['search_query: What is TSNE?', 'search_query Who is Laurens van der Maaten?']
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -2679,7 +2682,7 @@ def mean_pooling(model_output, attention_mask):
     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 
-sentences = ['What is TSNE?', 'Who is Laurens van der Maaten?']
+sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
 
 tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
 model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-ablated', trust_remote_code=True)
@@ -2702,8 +2705,8 @@ The model natively supports scaling of the sequence length past 2048 tokens. To
 + tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
 
 
-- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True)
-+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-unsupervised', trust_remote_code=True, rotary_scaling_factor=2)
+- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-ablated', trust_remote_code=True)
++ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1-ablated', trust_remote_code=True, rotary_scaling_factor=2)
 ```
 
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