Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
roberta
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use sdadas/mmlw-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-roberta-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-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-roberta-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-roberta-large") model = AutoModel.from_pretrained("sdadas/mmlw-roberta-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d371138eaf85a902443df4ee1be7de288aa046aa16bd64f14e17b657c5d95ca
- Size of remote file:
- 1.74 GB
- SHA256:
- 3f8ac7cb5d844ca98b20619493cca4e31eec3d7203d0252b7b16f611a81b0db8
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