Instructions to use inference4j/gte-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use inference4j/gte-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("inference4j/gte-base") 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] - Notebooks
- Google Colab
- Kaggle
GTE Base โ ONNX
ONNX export of thenlper/gte-base, an English embedding model from Alibaba. Maps sentences to 768-dimensional dense vectors using CLS pooling with L2 normalization.
Mirrored for use with inference4j, an inference-only AI library for Java.
Original Source
- Repository: thenlper/Alibaba (ONNX by Xenova)
- License: mit
Usage with inference4j
try (SentenceTransformerEmbedder model = SentenceTransformerEmbedder.builder()
.modelId("inference4j/gte-base")
.poolingStrategy(PoolingStrategy.CLS)
.normalize()
.build()) {
float[] embedding = model.encode("Hello, world!");
System.out.println("Dimension: " + embedding.length); // 768
}
Model Details
| Property | Value |
|---|---|
| Architecture | BERT Base (12 layers, 768 hidden) |
| Task | Sentence embeddings / semantic similarity |
| Output dimension | 768 |
| Pooling | CLS |
| Normalization | L2 |
| Max sequence length | 512 |
| Original framework | PyTorch (HuggingFace Transformers) |
License
This model is licensed under the MIT License. Original model by thenlper/Alibaba, ONNX export by Xenova.