Use v1.5 model in examples (#49)
Browse files- Use v1.5 model in examples (0760fbacbf3d000f3ddf79d9899336380aa00778)
Co-authored-by: Cebtenzzre <Cebtenzzre@users.noreply.huggingface.co>
README.md
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@@ -2630,7 +2630,7 @@ This prefix is used for embedding texts as documents, for example as documents f
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
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sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
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embeddings = model.encode(sentences)
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print(embeddings)
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@@ -2645,7 +2645,7 @@ This prefix is used for embedding texts as questions that documents from a datas
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
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sentences = ['search_query: Who is Laurens van Der Maaten?']
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embeddings = model.encode(sentences)
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print(embeddings)
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@@ -2660,7 +2660,7 @@ This prefix is used for embedding texts in order to group them into clusters, di
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
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sentences = ['clustering: the quick brown fox']
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embeddings = model.encode(sentences)
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print(embeddings)
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@@ -2675,7 +2675,7 @@ This prefix is used for embedding texts into vectors that will be used as featur
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
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sentences = ['classification: the quick brown fox']
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embeddings = model.encode(sentences)
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print(embeddings)
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@@ -2737,8 +2737,8 @@ The model natively supports scaling of the sequence length past 2048 tokens. To
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+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
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- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
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+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)
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```
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### Transformers.js
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
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sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
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embeddings = model.encode(sentences)
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print(embeddings)
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
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sentences = ['search_query: Who is Laurens van Der Maaten?']
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embeddings = model.encode(sentences)
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print(embeddings)
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
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sentences = ['clustering: the quick brown fox']
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embeddings = model.encode(sentences)
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print(embeddings)
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```python
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from sentence_transformers import SentenceTransformer
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+
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
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sentences = ['classification: the quick brown fox']
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embeddings = model.encode(sentences)
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print(embeddings)
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+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
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- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
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+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True, rotary_scaling_factor=2)
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```
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### Transformers.js
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