Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use arinze/address-match-abp-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use arinze/address-match-abp-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("arinze/address-match-abp-v1") 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 arinze/address-match-abp-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("arinze/address-match-abp-v1") model = AutoModel.from_pretrained("arinze/address-match-abp-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8545ecba4f36f05d5e1dfd17cfffb0c975539c1a78de84d4e24989c1f615f7d6
- Size of remote file:
- 90.9 MB
- SHA256:
- 0a44f52e1ba07ec951edc71d98ce038b868744ec777e6de6b6f6f39fdcd21dd8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.