Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/bert-large-nli-cls-token with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/bert-large-nli-cls-token with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/bert-large-nli-cls-token") 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/bert-large-nli-cls-token with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/bert-large-nli-cls-token") model = AutoModel.from_pretrained("sentence-transformers/bert-large-nli-cls-token", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 654e82227a0e46469cde6052e000a36e35c19320534fd321c4231d4746c88c84
- Size of remote file:
- 1.34 GB
- SHA256:
- 46a8d3dd137d82d5cb78f7d20148ec91f159f61e3e7dce053adc6216e69e542d
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