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:
- a8ded1dc7061097661c8123808c71fae1b0afbc61035b23b4ae83b617861612d
- Size of remote file:
- 271 MB
- SHA256:
- 60c4fbeddfae2f00234d2d9e6646cd7a11914454eaf46e278f85bfb088d1417f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.