Feature Extraction
sentence-transformers
Safetensors
Transformers
Russian
English
gigarembed
MTEB
custom_code
Instructions to use ai-sage/Giga-Embeddings-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ai-sage/Giga-Embeddings-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ai-sage/Giga-Embeddings-instruct", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use ai-sage/Giga-Embeddings-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ai-sage/Giga-Embeddings-instruct", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ai-sage/Giga-Embeddings-instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc8cf25eb9ec3494de69dfe215065fdb795d6d441d3635f7f8ea6d13b6598d82
- Size of remote file:
- 4.91 GB
- SHA256:
- 9f5bdcf6ab584d8e3fa1e53ad3061a203125be81e043b11e7c867e04670e8aa7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.