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Fine-Tuning Models for Better Performance
For example, let’s say you have a bunch of pictures of cats and dogs, but your model only knows how to identify cats or…
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Using Key-Value Cache in Transformers for Efficient Decoding
This can be time-consuming if you have long sequences or are running on slower hardware. But what if we could save some of these…
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How to Download and Use Pretrained Models for Natural Language Processing in Python
It is often used as a weighting factor in text searches and classification learning algorithms, and can be applied at either the document level…
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Transformers Offline Mode
Here’s how it works: first, you gather up a bunch of text data that your transformer will learn from. This could be anything from…
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Roberta Processing for Tokenization
For example, let’s say you have this sentence: “ChatGPT, with its advanced NLP, is transforming digital communication.” When we tokenize it, it might look…
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Streaming Text Generation in Python
This is super useful for things like chatbots or generating content for websites because you don’t have to worry about running out of RAM…
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Transformers for NLP: A Comprehensive Guide
Well, it’s basically like a magic wand for your NLP tasks it takes input text and turns it into something else (like machine-readable output…
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Preparing Dataset for BERT Pretraining
Before anything else, we need to download some data from Hugging Face Hub. This is like going to the library but instead of books,…
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Transformers for Inference
Instead of using traditional machine learning techniques like logistic regression or decision trees, which can take a long time and require a lot of…
