Feature Extraction
sentence-transformers
Safetensors
qwen3
text-to-sql
schema-linking
retrieval
embedding
spider2
bird
text-embeddings-inference
Instructions to use thanhdath/embedding-0.6b-spider2.0-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thanhdath/embedding-0.6b-spider2.0-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thanhdath/embedding-0.6b-spider2.0-v2") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f5e41eb45aca4650a1ce9f0c46b746453352fcd196bc025da1cb7363c4f8931
- Size of remote file:
- 1.19 GB
- SHA256:
- f3e3bd65de3f1bf1abe3176f131cdf6ded3f998095d5c7529c72417db937411d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.