update readme

README.md

@@ -2633,6 +2633,8 @@ new_version: nomic-ai/nomic-embed-text-v1.5
 
 For example, if you are implementing a RAG application, you embed your documents as `search_document: <text here>` and embed your user queries as `search_query: <text here>`.
 
+**Notice**: From transformers v5.5.0 and sentence transformers v5.3.0, `trust_remote_code=True` will no longer be necessary. This will only be possible with the text-only series as of now.
+
 ## Task instruction prefixes
 
 ### `search_document`
@@ -2644,7 +2646,7 @@ This prefix is used for embedding texts as documents, for example as documents f
 ```python
 from sentence_transformers import SentenceTransformer
 
-model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
 sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
 embeddings = model.encode(sentences)
 print(embeddings)
@@ -2659,7 +2661,7 @@ This prefix is used for embedding texts as questions that documents from a datas
 ```python
 from sentence_transformers import SentenceTransformer
 
-model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
 sentences = ['search_query: Who is Laurens van Der Maaten?']
 embeddings = model.encode(sentences)
 print(embeddings)
@@ -2674,7 +2676,7 @@ This prefix is used for embedding texts in order to group them into clusters, di
 ```python
 from sentence_transformers import SentenceTransformer
 
-model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
 sentences = ['clustering: the quick brown fox']
 embeddings = model.encode(sentences)
 print(embeddings)
@@ -2689,7 +2691,7 @@ This prefix is used for embedding texts into vectors that will be used as featur
 ```python
 from sentence_transformers import SentenceTransformer
 
-model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
 sentences = ['classification: the quick brown fox']
 embeddings = model.encode(sentences)
 print(embeddings)
@@ -2699,7 +2701,7 @@ print(embeddings)
 ```python
 from sentence_transformers import SentenceTransformer
 
-model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+model = SentenceTransformer("nomic-ai/nomic-embed-text-v1")
 sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
 embeddings = model.encode(sentences)
 print(embeddings)
@@ -2720,7 +2722,7 @@ def mean_pooling(model_output, attention_mask):
 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', trust_remote_code=True)
+model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1')
 model.eval()
 
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -2740,8 +2742,9 @@ 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', trust_remote_code=True)
-+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)
+- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1')
++ rope_parameters = {"rope_theta": 1000.0, "rope_type": "dynamic", "factor": 2.0}
++ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', rope_parameters=rope_parameters)
 ```
 
 ### Transformers.js