Feature Extraction
sentence-transformers
Safetensors
Transformers
English
qwen3
sentence-similarity
retrieval
agent-skills
skill-routing
skillcorpus
contrastive-learning
text-embeddings-inference
Instructions to use EverMind-AI/skillcorpus-embedding-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EverMind-AI/skillcorpus-embedding-0.6b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EverMind-AI/skillcorpus-embedding-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 EverMind-AI/skillcorpus-embedding-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="EverMind-AI/skillcorpus-embedding-0.6b")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("EverMind-AI/skillcorpus-embedding-0.6b") model = AutoModel.from_pretrained("EverMind-AI/skillcorpus-embedding-0.6b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47c7ed3b55e1d32df6b69020b0348a9f970f87d0d4d1b182bffe66163b1c23e9
- Size of remote file:
- 1.19 GB
- SHA256:
- a9d8bd183cb50a00a2c9a52d2d26d225dc438a03e5fb42e29d8936d10c53004a
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