Text Retrieval
Transformers
Safetensors
sentence-transformers
English
kpr-bert
feature-extraction
custom_code
Instructions to use knowledgeable-ai/kpr-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knowledgeable-ai/kpr-bert-base-uncased with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("knowledgeable-ai/kpr-bert-base-uncased", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use knowledgeable-ai/kpr-bert-base-uncased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("knowledgeable-ai/kpr-bert-base-uncased", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 229 Bytes
fe723cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.models.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.models.Pooling"
}
] |