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--- |
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language: en |
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widget: |
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- text: ' brown dog fox jumped lazy over quick the the ' |
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datasets: |
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- 'stas/c4-en-10k' |
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--- |
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# T5-deshuffle |
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Bag Of Words (BOW) is a simple and typical encoding for making statistical models discover patterns in language |
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However BOW is a lossy compression that eliminates a very important feature of text: order |
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This model is trained to learn the most probable order of an unordered token sequence, |
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using a subset of the c4 dataset, and can thus be seen as a "bag-of-words decoder". |
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Currently, it does not perform well. I'm planning to re-train on a larger subset of c4 later (after may). |
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How to run: |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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tokenizer = T5Tokenizer.from_pretrained("marksverdhei/t5-deshuffle") |
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model = T5ForConditionalGeneration.from_pretrained("marksverdhei/t5-deshuffle") |
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prompt = ' brown dog fox jumped lazy over quick the the ' |
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ids = tokenizer(prompt, return_tensors="pt").input_ids |
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generated_tokens, = model.generate(ids) |
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print(tokenizer.decode(generated_tokens, skip_special_tokens=True)) |
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``` |
