Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
Transformers.js
English
nomic_bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use inesaltemir/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use inesaltemir/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("inesaltemir/MNLP_M2_document_encoder", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use inesaltemir/MNLP_M2_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("inesaltemir/MNLP_M2_document_encoder", trust_remote_code=True) model = AutoModel.from_pretrained("inesaltemir/MNLP_M2_document_encoder", trust_remote_code=True) - Transformers.js
How to use inesaltemir/MNLP_M2_document_encoder with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'inesaltemir/MNLP_M2_document_encoder'); - Notebooks
- Google Colab
- Kaggle
Add `search_query: ` prefix to transformers.js sample code (#11)
Browse files- Add `search_query: ` prefix to transformers.js sample code (9ab2f92a0ce330cba7b6a7795f875dfa37f9b69f)
Co-authored-by: Joshua <Xenova@users.noreply.huggingface.co>
README.md
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@@ -2724,7 +2724,7 @@ const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-tex
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});
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// Compute sentence embeddings
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const texts = ['What is TSNE?', 'Who is Laurens van der Maaten?'];
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const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
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console.log(embeddings);
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```
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});
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// Compute sentence embeddings
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const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'];
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const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
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console.log(embeddings);
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```
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