Sentence Similarity
sentence-transformers
PyTorch
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use sheldonrobinson/all-MiniLM-L12-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sheldonrobinson/all-MiniLM-L12-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sheldonrobinson/all-MiniLM-L12-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 sheldonrobinson/all-MiniLM-L12-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sheldonrobinson/all-MiniLM-L12-v2") model = AutoModel.from_pretrained("sheldonrobinson/all-MiniLM-L12-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
de7f5af
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Parent(s): 2e5bea9
adding training datasets
Browse files
README.md
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- sentence-similarity
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language: en
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license: apache-2.0
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---
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- sentence-similarity
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language: en
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license: apache-2.0
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datasets:
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- s2orc
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- flax-sentence-embeddings/stackexchange_xml
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- ms_marco
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- gooaq
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- yahoo_answers_topics
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- code_search_net
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- search_qa
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- eli5
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- snli
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- multi_nli
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- wikihow
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- natural_questions
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- trivia_qa
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