Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use vectoriseai/gte-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vectoriseai/gte-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vectoriseai/gte-large") 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
Download special_tokens_map.json from vectoriseai/gte-large: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/vectoriseai/gte-large/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://vectoriseai/gte-large/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/vectoriseai/gte-large/resolve/main/special_tokens_map.json
125 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |