Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/nli-bert-base-max-pooling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/nli-bert-base-max-pooling with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/nli-bert-base-max-pooling") 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 sentence-transformers/nli-bert-base-max-pooling with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/nli-bert-base-max-pooling") model = AutoModel.from_pretrained("sentence-transformers/nli-bert-base-max-pooling") - Notebooks
- Google Colab
- Kaggle
Add exported ONNX model 'model_qint8_avx512.onnx'
Browse files
onnx/model_qint8_avx512.onnx
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