What is MLM ?
MLM stands for "masked language modeling". It is a technique used in pre-training transformer-based models such as BERT, RoBERTa, and GPT-2. The basic idea is to randomly mask some of the tokens in the input and then train the model to predict the original value of the masked tokens. This pre-training step helps the model understand the context and relationships between words, which can be useful for downstream tasks such as language understanding, question answering, and text generation.
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Why You Need MLM?
Masked token prediction: The model is trained to predict the original value of masked tokens in the input text. This helps the model learn the relationships between words and the context in which they are used.
Next sentence prediction: The model is trained to predict whether a given sentence is the next sentence in a given text. This helps the model understand the relationship between sentences and the context in which they are used.
Token-level classification: The model is trained to classify each token in the input text into one or more predefined categories such as part of speech, named entity, sentiment, etc.
Language model fine-tuning: Once the pre-training is done, the model can be fine-tuned on a specific task with a relatively small amount of task-specific training data.
Generative Pre-training: This is a variation of MLM, where the model is trained to predict the probability of the next word, given the previous words. This helps the model to generate text in a similar fashion as human.
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What Features We Offer?
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MLM is used in pre-training transformer-based models because it helps the model understand the context and relationships between words. This is important because many natural language processing tasks, such as language understanding, question answering, and text generation, require the model to understand the context in which words are used.
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By training the model to predict the original value of masked tokens, the model learns to understand the relationships between words and the context in which they are used. This enables the model to perform better on downstream tasks that require a deep understanding of the text.
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Additionally, pre-training with MLM allows the model to be fine-tuned on a specific task with a relatively small amount of task-specific training data, which can be beneficial when the amount of task-specific data is limited.
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Overall, MLM is an important technique for pre-training transformer-based models that helps the model achieve a better understanding of the text and perform better on a wide range of natural language processing tasks.
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MLM is a pre-training technique that helps a model understand the context and relationships between words. The specific features offered by MLM can vary depending on the model architecture and the implementation, but some common features include.
