Feature Extraction
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
distilbert
sentence-similarity
text-embeddings-inference
Instructions to use sentence-transformers/distilbert-base-nli-mean-tokens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/distilbert-base-nli-mean-tokens with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/distilbert-base-nli-mean-tokens") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use sentence-transformers/distilbert-base-nli-mean-tokens with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sentence-transformers/distilbert-base-nli-mean-tokens")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/distilbert-base-nli-mean-tokens") model = AutoModel.from_pretrained("sentence-transformers/distilbert-base-nli-mean-tokens", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1567ee4d8033b162e7cf4009264a15778795d9be2e5d4587282a0479a26d7d1
- Size of remote file:
- 265 MB
- SHA256:
- 3d58b7cb2697fad5b606046557e98380a868824a4d8a508bd04e97b2fb1559b8
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