Transformer Preprocessing: From Text to Tensors
Transformers don't read text; they read numbers. A tokenizer is the translator, converting sentences into numerical tensors the model understands. This is the mandatory first step for any NLP task. The footgun is using a tokenizer that doesn't match the model.
Transformers don't read text; they read numbers. A tokenizer is the essential translator, converting human language into numerical tensors by splitting text into tokens (words or subwords) and mapping them to integer IDs. This is the mandatory first step for any NLP task, like sentiment analysis or text generation. The most common footgun is a model-tokenizer mismatch; you must use the exact tokenizer the model was pretrained with, or the model will receive nonsensical input and produce garbage results.
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