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+ ---
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+ license: cc-by-sa-4.0
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+ ---
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+
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+ # Model generates definitions of the word given context.
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+
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+
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+ ### How to use model:
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+ ```
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+ from transformers import pipeline
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+ generator = pipeline("text2text-generation", model = 'clarin-knext/wnet-def-plt5large', use_auth_token=True)
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+ generator("komik: Poszedłem dzisiaj na kabaret i był tam śmieszny komik.")
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+ ```
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+ ``` [{'generated_text': 'człowiek , który lubi żartować i rozśmieszać innych . '}] ```
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+
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+ ```
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+ model = T5ForConditionalGeneration.from_pretrained("clarin-knext/wnet-def-plt5large", use_auth_token=True)
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+ tokenizer = T5TokenizerFast.from_pretrained("clarin-knext/wnet-def-plt5large", use_auth_token=True)
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+
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+ input_ids = tokenizer("samochód: po drodze jedzie samochód", return_tensors="pt").input_ids
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+ outputs = model.generate(input_ids)
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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+ ```
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+