Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
roberta
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use sdadas/mmlw-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-roberta-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-roberta-large") 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-roberta-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-roberta-large") model = AutoModel.from_pretrained("sdadas/mmlw-roberta-large") - Inference
- Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +2 -2
tokenizer_config.json
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"eos_token": "</s>",
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"errors": "replace",
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"mask_token": "<mask>",
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"model_max_length":
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"tokenizer_class": "
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"trim_offsets": true,
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"unk_token": "<unk>"
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}
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"eos_token": "</s>",
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"errors": "replace",
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"mask_token": "<mask>",
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"model_max_length": 512,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"tokenizer_class": "XLMRobertaTokenizer",
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"trim_offsets": true,
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"unk_token": "<unk>"
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}
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