Sentence Similarity
sentence-transformers
Safetensors
Transformers
new
feature-extraction
custom_code
text-embeddings-inference
Instructions to use OTHMAN7/gte_hack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OTHMAN7/gte_hack with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OTHMAN7/gte_hack", 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 OTHMAN7/gte_hack with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OTHMAN7/gte_hack", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d321d9c6744aac4121d7a9a9c9fcd916cbf6a7c9d6d01f4f8ebdbcbdd0b4a736
- Size of remote file:
- 1.74 GB
- SHA256:
- 0b6a0493462208e27ff5a8ae7cc25b9f829b406efd74b3139d2f4ed9d6aba506
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