Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
Generated from Trainer
dataset_size:8408
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use kuljeet98/bert-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kuljeet98/bert-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kuljeet98/bert-model") sentences = [ "president", "assistante de banque priv e banco santander rio", "worldwide executive vice president corindus a siemens healthineers company", "soporte t cnico superior" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from kuljeet98/bert-model: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/kuljeet98/bert-model/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://kuljeet98/bert-model@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kuljeet98/bert-model/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- 5b1eebf28d51b425919503bde32f76a54f1055eb4b3e3f6a03946174a9a2060d
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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