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@@ -18,13 +18,14 @@ of publicly available data) with an automatic process to generate inputs and lab
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  it was trained to guess the next word in sentences.
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  More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
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- shifted one token (word or piece of word) to the right. The model uses a mask mechanism to make sure the
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- predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
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  This way, the model learns an inner representation of the English language that can then be used to extract features
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  useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
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  prompt.
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  ```python
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  >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
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  it was trained to guess the next word in sentences.
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  More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
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+ shifting one token (word or piece of word) to the right. The model uses a mask mechanism to make sure the
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+ predictions for the token `i` only use the inputs from `1` to `i` but not the future tokens.
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  This way, the model learns an inner representation of the English language that can then be used to extract features
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  useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
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  prompt.
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+ ### To use this model
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  ```python
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  >>> from transformers import AutoTokenizer, AutoModelForCausalLM