Feature Extraction
sentence-transformers
ONNX
Transformers
Russian
English
gigarembed
MTEB
custom_code
Instructions to use Gotoro/Giga-Embeddings-instruct-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Gotoro/Giga-Embeddings-instruct-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Gotoro/Giga-Embeddings-instruct-onnx", 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 Gotoro/Giga-Embeddings-instruct-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Gotoro/Giga-Embeddings-instruct-onnx", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Gotoro/Giga-Embeddings-instruct-onnx", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75d4a9146d04ae60865867c2efaea076f4bf34bee46521f868911e1186f2969d
- Size of remote file:
- 3.59 MB
- SHA256:
- bb4ecec936292a419e4f368d40922f277a1584e67688920cdb6154f01ee2957a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.