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-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-e5-base") 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-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-e5-base") model = AutoModel.from_pretrained("sdadas/mmlw-e5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from sdadas/mmlw-e5-base: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/sdadas/mmlw-e5-base/resolve/main/model.safetensors
- Command line
-
hf download hf://sdadas/mmlw-e5-base/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/sdadas/mmlw-e5-base/resolve/main/model.safetensors
1.11 GB
- Xet hash:
- ac9ba01f012a81cd64b37d28e9d7449c3df46d99ade68d5b245028b33bacb402
- Size of remote file:
- 1.11 GB
- SHA256:
- f50cea47c8eb01dc5e84a45e72e4fef90fb9513e8e56979b6b9a6ed1ae023ecd
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