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")# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
b10-poc
#3
by michaelfeil - opened
- config.json +0 -1
config.json
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"sliding_window": 512,
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"sliding_window_pattern": 1,
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"transformers_version": "4.57.6",
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"use_bidirectional_attention": false,
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"use_cache": false,
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"vocab_size": 262144
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}
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"sliding_window": 512,
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"sliding_window_pattern": 1,
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"transformers_version": "4.57.6",
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"use_cache": false,
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"vocab_size": 262144
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}
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