Feature Extraction
Transformers.js
ONNX
Transformers
English
bert
fill-mask
PyTorch
custom_code
text-embeddings-inference
Instructions to use do-me/jina-embeddings-v2-base-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use do-me/jina-embeddings-v2-base-en with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'do-me/jina-embeddings-v2-base-en'); - Transformers
How to use do-me/jina-embeddings-v2-base-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="do-me/jina-embeddings-v2-base-en", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("do-me/jina-embeddings-v2-base-en", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("do-me/jina-embeddings-v2-base-en", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).
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Head over to https://huggingface.co/Xenova/jina-embeddings-v2-base-en for an improved version.
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