Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
roberta
feature-extraction
text-embeddings-inference
Instructions to use radlab/polish-bi-encoder-mean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use radlab/polish-bi-encoder-mean with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("radlab/polish-bi-encoder-mean") 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 radlab/polish-bi-encoder-mean with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("radlab/polish-bi-encoder-mean") model = AutoModel.from_pretrained("radlab/polish-bi-encoder-mean", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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## Usage (Sentence-Transformers)
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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Tis model is the newest version of [radlab/polish-sts-v2](https://huggingface.co/radlab/polish-sts-v2) model.
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## Usage (Sentence-Transformers)
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