Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use nesoai/EnergyBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nesoai/EnergyBert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nesoai/EnergyBert") 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 nesoai/EnergyBert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nesoai/EnergyBert") model = AutoModel.from_pretrained("nesoai/EnergyBert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nesoai/EnergyBert: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/nesoai/EnergyBert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nesoai/EnergyBert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nesoai/EnergyBert/resolve/main/pytorch_model.bin
439 MB
- Xet hash:
- 8fa0baaf570e7f1746b601b78c71557b9e127b07a01b3b446b7eb0f6f8585200
- Size of remote file:
- 439 MB
- SHA256:
- 3632d6d62c826c7eaf0a0e024631ce1590e0a7e8856b5c2c07a57c6b1935c5cc
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