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
How to change the batch size? or any tips to make the model faster?
#7
by sdvfsfwfgfwf - opened
How to change the batch size? or any tips to make the model faster?
If you are referring to accelerating the inference phase of the embedding model, you can refer to the inference acceleration solution provided by Hugging Face at https://github.com/huggingface/text-embeddings-inference