Feature Extraction
Transformers
Safetensors
Russian
gigarembed
embeddings
retrieval
semantic-search
product-classification
catalog-search
lora-merged
nkt
custom_code
Instructions to use NAUMOV/Adata-Embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NAUMOV/Adata-Embeddings with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="NAUMOV/Adata-Embeddings", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NAUMOV/Adata-Embeddings", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from NAUMOV/Adata-Embeddings: direct link, hf CLI and curl.
- Browser
- Download file 10.7 MB
-
https://huggingface.co/NAUMOV/Adata-Embeddings/resolve/main/tokenizer.json
- Command line
-
hf download hf://NAUMOV/Adata-Embeddings/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/NAUMOV/Adata-Embeddings/resolve/main/tokenizer.json
10.7 MB
- Xet hash:
- e002f0a5045832b5ebe60cc366aca637d55d09e6f6d1004e205b57c4db4c3f08
- Size of remote file:
- 10.7 MB
- SHA256:
- 0ec0a1cffcc9192f5ee3d7b273673a062918055238bda3d23cfb6d2512e947ff
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