Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use RedHatAI/all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RedHatAI/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RedHatAI/all-MiniLM-L6-v2") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use RedHatAI/all-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RedHatAI/all-MiniLM-L6-v2") model = AutoModel.from_pretrained("RedHatAI/all-MiniLM-L6-v2") - Notebooks
- Google Colab
- Kaggle
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license_link: https://www.apache.org/licenses/LICENSE-2.0
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tasks:
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- text-embedding
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tags:
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- sentence-transformers
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- feature-extraction
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license_link: https://www.apache.org/licenses/LICENSE-2.0
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tasks:
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- text-embedding
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hardware_tag:
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- Intel Xeon
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tags:
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- sentence-transformers
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- feature-extraction
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