Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use LazarusNLP/all-indo-e5-small-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LazarusNLP/all-indo-e5-small-v4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LazarusNLP/all-indo-e5-small-v4") 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 LazarusNLP/all-indo-e5-small-v4 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/all-indo-e5-small-v4") model = AutoModel.from_pretrained("LazarusNLP/all-indo-e5-small-v4") - Inference
- Notebooks
- Google Colab
- Kaggle
Add exported onnx model 'model.onnx'
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by itsdevice - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:8c88615aefc5b348f1e6a1d30f373104c41d7f0b9bd3b9cb28dd5373b3956137
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size 470301610
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