Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use sdadas/mmlw-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-e5-small") sentences = [ "query: Jak dożyć 100 lat?", "passage: Trzeba zdrowo się odżywiać i uprawiać sport.", "passage: Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "passage: 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-e5-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-e5-small") model = AutoModel.from_pretrained("sdadas/mmlw-e5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e1bd6cfc694372cae4f3db2a7fc012739db95055c754644cc25670d4fcbe8ab
- Size of remote file:
- 17.1 MB
- SHA256:
- 3ca5f734b9407eb910ec87ecf2a0325a7c5c3436836ba12c600b0ce787b8c3a6
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