Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
mteb
text-embeddings-inference
Instructions to use microsoft/harrier-oss-v1-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use microsoft/harrier-oss-v1-0.6b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("microsoft/harrier-oss-v1-0.6b") 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-v1-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/harrier-oss-v1-0.6b")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/harrier-oss-v1-0.6b") model = AutoModel.from_pretrained("microsoft/harrier-oss-v1-0.6b") - Inference
- Notebooks
- Google Colab
- Kaggle
Add exported onnx model 'model.onnx'
#9 opened 10 days ago
by
acul21
Request: DOI
#8 opened 29 days ago
by
ldragon
Reranker model
#7 opened about 1 month ago
by
Duonglv
Why distal?
#6 opened about 1 month ago
by
breadlicker45
ONNX version
1
#5 opened about 2 months ago
by
uasan
Will release a technical report & paper?
➕ 2
#4 opened about 2 months ago
by
undefined-x
Training/finetunning
1
#2 opened about 2 months ago
by
AliKhajegiliM
Add a default "query" prompt for `model.encode_query`
#1 opened about 2 months ago
by
tomaarsen