Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
roberta
feature-extraction
information-retrieval
text-embeddings-inference
Instructions to use sdadas/mmlw-retrieval-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-retrieval-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-retrieval-roberta-base") sentences = [ "zapytanie: Jak dożyć 100 lat?", "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "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-roberta-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-retrieval-roberta-base") model = AutoModel.from_pretrained("sdadas/mmlw-retrieval-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Citation
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```bibtex
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@
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title={
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author={
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primaryClass={cs.CL}
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```
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## Citation
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```bibtex
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@inproceedings{dadas2024pirb,
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title={PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods},
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author={Dadas, Slawomir and Pere{\l}kiewicz, Micha{\l} and Po{\'s}wiata, Rafa{\l}},
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booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
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pages={12761--12774},
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year={2024}
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}
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```
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