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Nn model ru sets4/17/2024 ![]() Why concatenate everything together? The reason is that individual examples might get truncated if they’re too long, and that would result in losing information that might be useful for the language modeling task! This is quite different from our usual approach, where we simply tokenize individual examples. Let’s go! Preprocessing the dataįor both auto-regressive and masked language modeling, a common preprocessing step is to concatenate all the examples and then split the whole corpus into chunks of equal size. As we’ll see, there are some additional steps that one needs to take compared to the sequence classification tasks we saw in Chapter 3. Now that we’ve had a quick look at the data, let’s dive into preparing it for masked language modeling. While you’re at it, you could also check that the labels in the train and test splits are indeed 0 or 1 - this is a useful sanity check that every NLP practitioner should perform at the start of a new project! ✏️ Try it out! Create a random sample of the unsupervised split and verify that the labels are neither 0 nor 1. Yep, these are certainly movie reviews, and if you’re old enough you may even understand the comment in the last review about owning a VHS version □! Although we won’t need the labels for language modeling, we can already see that a 0 denotes a negative review, while a 1 corresponds to a positive one. My daughter keeps singing them over and over.Hope this helps.' '> Label: 1' Though I have to admit it is not as good on a little TV.I do not have older children so I do not know what they would think of it. My 4 and 6 year old children love this movie and have been asking again and again to watch it. I loved it then, and have recently come to own a VHS version. Skip this mess.' '> Label: 0' '> Review: I saw this movie at the theaters when I was about 6 or 7 years old. Even a great cast cannot redeem the time the viewer wastes with this minimal effort.The proper response to the movie is the contempt that the director San Peckinpah, James Caan, Robert Duvall, Burt Young, Bo Hopkins, Arthur Hill, and even Gig Young bring to their work. For those who appreciate Peckinpah and his great work, this movie is a disappointment. Kunal Khemu is OK, and Sharman Joshi is the best.' '> Label: 0' '> Review: Okay, the story makes no sense, the characters lack any dimensionally, the best dialogue is ad-libs about the low quality of movie, the cinematography is dismal, and only editing saves a bit of the muddle, but Sam" Peckinpah directed the film. Rajpal Yadav is irritating, and Tusshar is not a whole lot better. Now, the hilarity of his films is fading as he is using the same formula over and over again.Songs are good. However, in most his previous movies there has actually been some good humor, (Hungama and Hera Pheri being noteworthy ones). Don\'t get me wrong as clichéd and preposterous his movies may be, I usually end up enjoying the comedy. Whether it is a winning lottery ticket in Malamaal Weekly, black money in Hera Pheri, "kodokoo" in Phir Hera Pheri, etc., etc., the director is becoming ridiculously predictable. His signature climax has the entire cast of the film coming together and fighting each other in some crazy moshpit over hidden money. An example of domain adaptation with ULMFiT is shown in the image below in this section we’ll do something similar, but with a Transformer instead of an LSTM!Ĭopied '> Review: This is your typical Priyadarshan movie-a bunch of loony characters out on some silly mission. It was popularized in 2018 by ULMFiT, which was one of the first neural architectures (based on LSTMs) to make transfer learning really work for NLP. This process of fine-tuning a pretrained language model on in-domain data is usually called domain adaptation. By fine-tuning the language model on in-domain data you can boost the performance of many downstream tasks, which means you usually only have to do this step once! For example, if your dataset contains legal contracts or scientific articles, a vanilla Transformer model like BERT will typically treat the domain-specific words in your corpus as rare tokens, and the resulting performance may be less than satisfactory. However, there are a few cases where you’ll want to first fine-tune the language models on your data, before training a task-specific head. Provided that the corpus used for pretraining is not too different from the corpus used for fine-tuning, transfer learning will usually produce good results. For many NLP applications involving Transformer models, you can simply take a pretrained model from the Hugging Face Hub and fine-tune it directly on your data for the task at hand.
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