Feature Extraction
Transformers
Safetensors
sentence-transformers
Russian
English
bert
sentence-similarity
SbertDistil
text-embeddings-inference
Instructions to use FractalGPT/SbertDistil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FractalGPT/SbertDistil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FractalGPT/SbertDistil")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FractalGPT/SbertDistil") model = AutoModel.from_pretrained("FractalGPT/SbertDistil", device_map="auto") - sentence-transformers
How to use FractalGPT/SbertDistil with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FractalGPT/SbertDistil") 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: 190 Bytes
cd9efa3 | 1 2 3 4 5 6 7 | {
"word_embedding_dimension": 312,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false
} |