Update model card slightly (#2)

- Update model card slightly (10c3f19872f1e5b090ea7ca44b33592bf34f08b3)


Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>

README.md

@@ -114,7 +114,7 @@ language:
 # nomic-embed-text-v2-moe: Multilingual Mixture of Experts Text Embeddings
 
 ## Model Overview
-nomic-embed-text-v2-moe is SoTA multilingual MoE text embedding model:
+`nomic-embed-text-v2-moe` is SoTA multilingual MoE text embedding model:
 
 - **High Performance**: SoTA Multilingual performance compared to ~300M parameter models, competitive with models 2x in size
 - **Multilinguality**: Supports ~100 languages and trained over 1.6B pairs
@@ -157,14 +157,15 @@ For best performance on GPU, please install
 pip install torch transformers einops git+https://github.com/nomic-ai/megablocks.git
 ```
 
-**Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. 
+> [!IMPORTANT]
+> **Important!**
+> The text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. 
 
-For queries/questions, please use `search_query: ` and `search_document: ` for the corresponding document
+Please use `search_query: ` before your queries/questions, and `search_document: ` before your documents.
 
-**Transformers**
+### Transformers
 
-
-If using Transformers, **make sure to prepend the task instruction prefix**
+If using Transformers, **make sure to prepend the task instruction prefix**.
 
 ```python
 import torch
@@ -187,11 +188,17 @@ with torch.no_grad():
     model_output = model(**encoded_input)
 embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
 embeddings = F.normalize(embeddings, p=2, dim=1)
+print(embeddings.shape)
+# torch.Size([2, 768])
+
+similarity = F.cosine_similarity(embeddings[0], embeddings[1])
+print(similarity)
+# tensor(0.9118)
 ```
 
-**SentenceTransformers**
+### SentenceTransformers
 
-With SentenceTransformers, you can specify the prompt_name (query or passage)
+With SentenceTransformers, you can specify the `prompt_name` as either `"query"` or `"passage"`, and the task instruction will be included automatically.
 
 ```python
 from sentence_transformers import SentenceTransformer
@@ -199,6 +206,12 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("nomic-ai/nomic-embed-text-v2-moe", trust_remote_code=True)
 sentences = ["Hello!", "¡Hola!"]
 embeddings = model.encode(sentences, prompt_name="passage")
+print(embeddings.shape)
+# (2, 768)
+
+similarity = model.similarity(embeddings[0], embeddings[1])
+print(similarity)
+# tensor([[0.9118]])
 ```
 
 ## Performance
@@ -221,7 +234,7 @@ nomic-embed-text-v2-moe performance on BEIR at 768 dimension and truncated to 25
 ## Limitations
 - Performance may vary across different languages
 - Resource requirements may be higher than traditional dense models due to MoE architecture
-- Must have trust_remote_code=True when loading the model
+- Must use `trust_remote_code=True` when loading the model to use our custom architecture implementation
 
 ## Training Details