Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Greek
bert
feature-extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use dimitriz/st-greek-media-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dimitriz/st-greek-media-bert-base-uncased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dimitriz/st-greek-media-bert-base-uncased") 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 dimitriz/st-greek-media-bert-base-uncased with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dimitriz/st-greek-media-bert-base-uncased") model = AutoModel.from_pretrained("dimitriz/st-greek-media-bert-base-uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26029d3871be6c1c6424fc4627f826a1a931564fbb7d49899398d07af013d67b
- Size of remote file:
- 452 MB
- SHA256:
- 6ea4bbca9a18265ab75010ac3747902c628356f065bae71b87ac50ac597566fe
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