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
Present AUR url in the same way as Arch' official package repository
Browse files
README.md
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@@ -51,7 +51,7 @@ model](https://huggingface.co/NetherlandsForensicInstitute/ARM64bert) we have pu
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## Data
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### What data was used for training and evaluation?
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The dataset is created in the same way as Wang et al. created Binary Corp.
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A large set of binary code comes from the [ArchLinux official repositories](https://archlinux.org/packages/) and the [ArchLinux user repositories](https://aur.archlinux.org/).
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All this code is split into functions that are compiled with different optimalizations
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(`O0`, `O1`, `O2`, `O3` and `Os`) and security settings (fortify or no-fortify).
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This results in a maximum of 10 (5×2) different functions which are semantically similar, i.e. they represent the same functionality, but have different machine code.
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## Data
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### What data was used for training and evaluation?
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The dataset is created in the same way as Wang et al. created Binary Corp.
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+
A large set of binary code comes from the [ArchLinux official repositories](https://archlinux.org/packages/) and the [ArchLinux user repositories](https://aur.archlinux.org/packages/).
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| 55 |
All this code is split into functions that are compiled with different optimalizations
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(`O0`, `O1`, `O2`, `O3` and `Os`) and security settings (fortify or no-fortify).
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| 57 |
This results in a maximum of 10 (5×2) different functions which are semantically similar, i.e. they represent the same functionality, but have different machine code.
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