Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/bert-base-nli-max-tokens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/bert-base-nli-max-tokens with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/bert-base-nli-max-tokens") 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-base-nli-max-tokens with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/bert-base-nli-max-tokens") model = AutoModel.from_pretrained("sentence-transformers/bert-base-nli-max-tokens", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb038a604f0d1c92eadbf2f7150fb6895d8b701f565429a52aa2e378b08b3a56
- Size of remote file:
- 438 MB
- SHA256:
- e18af41f25f8e171fb76ccb58e6b1930e06b19e0f7c381261d58439c70753595
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