Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
xlm-roberta
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use sdadas/mmlw-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-e5-large") 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-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-e5-large") model = AutoModel.from_pretrained("sdadas/mmlw-e5-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 810fbe0bc8c533658ccb933be7d17b202756146b8f5057e42353bd6b4d90a4ab
- Size of remote file:
- 2.24 GB
- SHA256:
- 87c8fd683bbaca29953721b2e3229095a023f47876d5f1cf9b1ef044982c18c8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.