Sentence Similarity
Transformers
Safetensors
Vietnamese
sai_embedding
feature-extraction
embeddings
retrieval
llm2vec
matryoshka
custom-code
custom_code
Instructions to use thongbuind/SAI-Embedding_100M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thongbuind/SAI-Embedding_100M with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thongbuind/SAI-Embedding_100M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download __init__.py from thongbuind/SAI-Embedding_100M: direct link, hf CLI and curl.
- Browser
- Download file 257 Bytes
-
https://huggingface.co/thongbuind/SAI-Embedding_100M/resolve/main/__init__.py
- Command line
-
hf download hf://thongbuind/SAI-Embedding_100M/__init__.py
-
curl -L -o __init__.py https://huggingface.co/thongbuind/SAI-Embedding_100M/resolve/main/__init__.py
257 Bytes
| from .configuration_sai_embedding import SAIEmbeddingConfig | |
| from .modeling_sai_embedding import SAIEmbeddingModel | |
| from .tokenization_sai_embedding import SAIEmbeddingTokenizer | |
| __all__ = ["SAIEmbeddingConfig", "SAIEmbeddingModel", "SAIEmbeddingTokenizer"] | |