Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
roberta
feature-extraction
information-retrieval
text-embeddings-inference
Instructions to use sdadas/mmlw-retrieval-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-retrieval-roberta-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-retrieval-roberta-large") sentences = [ "zapytanie: Jak dożyć 100 lat?", "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use sdadas/mmlw-retrieval-roberta-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-retrieval-roberta-large") model = AutoModel.from_pretrained("sdadas/mmlw-retrieval-roberta-large") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03f7dab0359f232df0f3232f2384aa199fc1d20af0d6f9138e1c1c0147402596
- Size of remote file:
- 870 MB
- SHA256:
- 17086c73c86b609877298774dffbc56625f3e982aa4efd1c13e5cc35fba26f8d
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