Feature Extraction
sentence-transformers
PyTorch
English
custom_snp
emotional-ai
reasoning-embedding
substrate-prism
cognitive-modeling
Instructions to use 366degrees/snp-universal-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 366degrees/snp-universal-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("366degrees/snp-universal-embedding") 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
Delete Dockerfile
Browse files- Dockerfile +0 -25
Dockerfile
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FROM python:3.10-slim
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# Make absolutely sure the working directory exists
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RUN mkdir -p /app
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WORKDIR /app
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# Print working directory (for debugging)
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RUN echo "✅ Building from context:" && pwd && ls -R
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# Copy specific files explicitly by name
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COPY api_inference.py /app/api_inference.py
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COPY snp_universal_embedding.py /app/snp_universal_embedding.py
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COPY config.json /app/config.json
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COPY tokenizer.json /app/tokenizer.json
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COPY pytorch_model.bin /app/pytorch_model.bin
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COPY requirements.txt /app/requirements.txt
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# Install dependencies
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RUN pip install --no-cache-dir -r /app/requirements.txt
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# Expose Hugging Face port
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EXPOSE 7860
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# Run the app
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CMD ["python", "/app/api_inference.py"]
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