Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
xlm-roberta
feature-extraction
information-retrieval
text-embeddings-inference
Instructions to use sdadas/mmlw-retrieval-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-retrieval-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-retrieval-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-retrieval-e5-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-retrieval-e5-large") model = AutoModel.from_pretrained("sdadas/mmlw-retrieval-e5-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sdadas/mmlw-retrieval-e5-large: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/sdadas/mmlw-retrieval-e5-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://sdadas/mmlw-retrieval-e5-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sdadas/mmlw-retrieval-e5-large/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- be42ccf98ca5396fe1d8f9d3ba7ef5b34f131800e4ed70e3aa7d27048fc47c01
- Size of remote file:
- 17.1 MB
- SHA256:
- f7bc482bbe4d17395039e8c52dfd60092475c46bdcd8cd16c7238b2c522cd2c8
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