Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use TaylorAI/gte-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TaylorAI/gte-tiny with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TaylorAI/gte-tiny") 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
How to use TaylorAI/gte-tiny with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TaylorAI/gte-tiny") model = AutoModel.from_pretrained("TaylorAI/gte-tiny", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from TaylorAI/gte-tiny: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/TaylorAI/gte-tiny/resolve/refs%2Fpr%2F2/special_tokens_map.json
- Command line
-
hf download hf://TaylorAI/gte-tiny@refs/pr/2/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/TaylorAI/gte-tiny/resolve/refs%2Fpr%2F2/special_tokens_map.json
228 Bytes
| { | |
| "additional_special_tokens": [ | |
| "[PAD]", | |
| "[UNK]", | |
| "[CLS]", | |
| "[SEP]", | |
| "[MASK]" | |
| ], | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |