Sentence Similarity
sentence-transformers
Safetensors
English
bert
ColBERT
multi-vector
feature-extraction
Generated from Trainer
dataset_size:497901
loss:Contrastive
text-embeddings-inference
Instructions to use NeuML/colbert-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/colbert-bert-tiny with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/colbert-bert-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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from NeuML/colbert-bert-tiny: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/NeuML/colbert-bert-tiny/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://NeuML/colbert-bert-tiny@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/NeuML/colbert-bert-tiny/resolve/refs%2Fpr%2F1/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.