Sentence Similarity
Transformers
ONNX
Safetensors
sentence-transformers
Transformers.js
English
new
feature-extraction
gte
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Alibaba-NLP/gte-base-en-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alibaba-NLP/gte-base-en-v1.5 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use Alibaba-NLP/gte-base-en-v1.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-base-en-v1.5", trust_remote_code=True) 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.js
How to use Alibaba-NLP/gte-base-en-v1.5 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'Alibaba-NLP/gte-base-en-v1.5'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ae9508e1dca85ef3181a1de1ccdf381e68ac3915dd2a74d95d6db19afe397e9
- Size of remote file:
- 547 MB
- SHA256:
- c30d96b914af1a141db13075863e666dee17daa5f844d506e07c88cd9ab12d2b
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