Without this setting TFHub would download the compressed file and extract the checkpoint locally. This is only recommended when running TFHub models on TPU. Next, configure TFHub to read checkpoints directly from TFHub's Cloud Storage buckets.
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Import tensorflow_text as text # A dependency of the preprocessing model
BERT FINETUNE INSTALL
pip install -q -U tf-models-official=2.7.0 pip install -U tfds-nightly import os You will use the AdamW optimizer from tensorflow/models to fine-tune BERT, which you will install as well. pip install -q -U "tensorflow-text=2.8.*" This model depends on tensorflow/text, which you will install below. You will use a separate model to preprocess text before using it to fine-tune BERT. In Colab, choose Runtime -> Change runtime type and verify that a TPU is selected. Note: This notebook should be run using a TPU. The preprocessing logic will be included in the model itself, making it capable of accepting raw strings as input. Key Point: The model you develop will be end-to-end.
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Fine-tune BERT (examples are given for single-sentence and multi-sentence datasets).Choose one of GLUE tasks and download the dataset.You can also run this notebook on a GPU, by changing one line (described below). This tutorial contains complete end-to-end code to train these models on a TPU. WNLI(Winograd Natural Language Inference): The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. RTE(Recognizing Textual Entailment): Determine if a sentence entails a given hypothesis or not. QNLI(Question-answering Natural Language Inference): The task is to determine whether the context sentence contains the answer to the question. MNLI (Multi-Genre Natural Language Inference): Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). QQP (Quora Question Pairs2): Determine whether a pair of questions are semantically equivalent. MRPC (Microsoft Research Paraphrase Corpus): Determine whether a pair of sentences are semantically equivalent. SST-2 (Stanford Sentiment Treebank): The task is to predict the sentiment of a given sentence.
BERT FINETUNE HOW TO
You will learn how to fine-tune BERT for many tasks from the GLUE benchmark:ĬoLA (Corpus of Linguistic Acceptability): Is the sentence grammatically correct?
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BERT can be used to solve many problems in natural language processing.