docs: usage

README.md

@@ -2902,8 +2902,7 @@ base_model:
 
 # ModernBERT Embed
 
-ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), brining the new advances of ModernBERT to embeddings!
-
+ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), brining the new advances of ModernBERT to embeddings! 
 
 ## Performance
 
@@ -2912,5 +2911,49 @@ ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://hu
 | nomic-embed-text-v1 | 768 | 62.4 | 74.1 | 43.9 | 85.2 | 55.7 | 52.8 | 82.1 | 30.1 |
 | nomic-embed-text-v1.5 | 768 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | 53.01 | 81.94 | 30.4 |
 | ModernBERT | 768 | 62.62 | 74.31 | 44.98 | 83.96 | 56.42 | 52.89 | 81.78 | 31.39 |
-| nomic-embed-text-v1.5 | 256 | 61.04 | 72.1 | 43.16 | 84.09 | 55.18 | 50.81 | 81.34 | 30.05 |
-| ModernBERT | 256 | 61.17 | 72.40 | 43.82 | 83.45 | 55.69 | 50.62 | 81.12 | 31.27 |
+| nomic-embed-text-v1.5 | 256 | 61.04 | 72.1 | 43.16 | 84.09 | 55.18 | 50.81 | 81.34|
+| ModernBERT | 256 | 61.17 | 72.40 | 43.82 | 83.45 | 55.69 | 50.62 | 81.12 | 31.27 |
+
+## Usage
+
+You can use these models directly with the transformers library. Until the next transformers release, doing so requires installing transformers from main:
+
+```bash
+pip install git+https://github.com/huggingface/transformers.git
+```
+
+Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes).
+
+Most use cases, adding `search_query` to the query and `search_document` to the documents will be sufficient.
+
+### Transformers
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    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 = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
+
+tokenizer = AutoTokenizer.from_pretrained('nomic-ai/modernbert-embed')
+model = AutoModel.from_pretrained('nomic-ai/modernbert-embed')
+model.eval()
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+matryoshka_dim = 768
+
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+
+embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+embeddings = embeddings[:, :matryoshka_dim]
+embeddings = F.normalize(embeddings, p=2, dim=1)
+print(embeddings)
+```