Sentence Similarity
sentence-transformers
Safetensors
Transformers
Polish
modernbert
feature-extraction
text-embeddings-inference
Instructions to use OPI-PIB/PolDense-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OPI-PIB/PolDense-150M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OPI-PIB/PolDense-150M") 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 OPI-PIB/PolDense-150M with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OPI-PIB/PolDense-150M") model = AutoModel.from_pretrained("OPI-PIB/PolDense-150M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Running with vLLM:
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```sh
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vllm serve OPI-PIB/PolDense-150M --dtype bfloat16 --runner pooling --convert embed
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```
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## Acknowledgements
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Running with vLLM:
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```sh
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vllm serve OPI-PIB/PolDense-150M --dtype bfloat16 --runner pooling --convert embed --max-model-len 7999
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```
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## Acknowledgements
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