Feature Extraction
sentence-transformers
Safetensors
Transformers
gemma3_text
mteb
text-embeddings-inference
Instructions to use microsoft/harrier-oss-v1-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use microsoft/harrier-oss-v1-270m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("microsoft/harrier-oss-v1-270m") 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 microsoft/harrier-oss-v1-270m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/harrier-oss-v1-270m")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/harrier-oss-v1-270m") model = AutoModel.from_pretrained("microsoft/harrier-oss-v1-270m") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54a250b3b7c81482286dfd8f776cbfdfd7e2889cf6c1674679395abd967cdd60
- Size of remote file:
- 33.4 MB
- SHA256:
- 6852f8d561078cc0cebe70ca03c5bfdd0d60a45f9d2e0e1e4cc05b68e9ec329e
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