Feature Extraction
sentence-transformers
Safetensors
PyTorch
Transformers
Russian
English
bert
sentence-similarity
text-embeddings-inference
Instructions to use evilfreelancer/enbeddrus-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use evilfreelancer/enbeddrus-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("evilfreelancer/enbeddrus-v0.1") 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] - Transformers
How to use evilfreelancer/enbeddrus-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="evilfreelancer/enbeddrus-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("evilfreelancer/enbeddrus-v0.1") model = AutoModel.from_pretrained("evilfreelancer/enbeddrus-v0.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f99258957b70b8b236fa44b9b295f75ab255d4e0e41a6c9895a98e6ac47f0b81
- Size of remote file:
- 669 MB
- SHA256:
- 83c3698a48afbd81bd2adc14c418ca3531094d1da72313b9f9aefb74619b4a5b
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