Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use NetherlandsForensicInstitute/ARM64BERT-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NetherlandsForensicInstitute/ARM64BERT-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NetherlandsForensicInstitute/ARM64BERT-embedding") 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] - Notebooks
- Google Colab
- Kaggle
Put both models in the evaluation table
Browse files
README.md
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| Model | Pool size | MRR | Recall@1 |
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| ARM64BERT-embedding | 32 | 0.99 | 0.99 |
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| ARM64BERT-embedding | 10.000 | 0.87 | 0.83 |
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## Purpose and use of the model
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| Model | Pool size | MRR | Recall@1 |
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| ARM64BERT | 32 | 0.78 | 0.72 |
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| ARM64BERT-embedding | 32 | 0.99 | 0.99 |
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| ARM64BERT | 10.000 | 0.58 | 0.56 |
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| ARM64BERT-embedding | 10.000 | 0.87 | 0.83 |
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## Purpose and use of the model
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