Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
bert
feature-extraction
information-retrieval
text-embeddings-inference
Instructions to use sdadas/mmlw-retrieval-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-retrieval-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-retrieval-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-retrieval-e5-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-retrieval-e5-small") model = AutoModel.from_pretrained("sdadas/mmlw-retrieval-e5-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4340a1b2c7351e5ed9ccbaef34028fc7239e4994238ee60415c311f4e2a30522
- Size of remote file:
- 235 MB
- SHA256:
- 4c3f319e85b02c2794e20065222528eb7c107ac69f073ad4447d2b1e76a1a5e5
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