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
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Download README.md from nesoai/EnergyBert: direct link, hf CLI and curl.
- Browser
- Download file 602 Bytes
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https://huggingface.co/nesoai/EnergyBert/resolve/main/README.md
- Command line
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hf download hf://nesoai/EnergyBert/README.md
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curl -L -o README.md https://huggingface.co/nesoai/EnergyBert/resolve/main/README.md
602 Bytes
| pipeline_tag: sentence-similarity | |
| tags: | |
| - sentence-transformers | |
| - feature-extraction | |
| - sentence-similarity | |
| - transformers | |
| Schema matching plays a crucial role in this process by giving each representation a unique identity by matching raw energy data to a generic data model. We developed an energy domain language model, Energy BERT, which was trained them using an open-source scientific corpus. Energy BERT can be used to automate schema matching, reducing manual effort in integrating heterogeneous data. | |
| ## Citing & Authors | |
| <!--- Describe where people can find more information --> |