![]() ![]() In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. 11:18:12.667538: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. ![]() Sentence2: b'The rovers act as robotic geologists, moving on six wheels. Sentence1: b'The identical rovers will act as robotic geologists, searching for evidence of past water. Here is one example from the training set: example_batch = next(iter(glue)) 'sentence2': Text(shape=(), dtype=string), 'sentence1': Text(shape=(), dtype=string), 'label': ClassLabel(shape=(), dtype=int64, num_classes=2), The info object describes the dataset and its features: info.features Maximum sequence length of training and evaluation dataset: 128īegin by loading the MRPC dataset from TFDS: batch_size=32.The GLUE MRPC (Dolan and Brockett, 2005) dataset is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent. The following section handles the necessary preprocessing. This dataset is not set up such that it can be directly fed into the BERT model. This example uses the GLUE (General Language Understanding Evaluation) MRPC (Microsoft Research Paraphrase Corpus) dataset from TensorFlow Datasets (TFDS). The following directory contains the BERT model's configuration, vocabulary, and a pre-trained checkpoint used in this tutorial: gs_folder_bert = "gs://cloud-tpu-checkpoints/bert/v3/uncased_L-12_H-768_A-12" Pip install -q opencv-python pip install -q -U "tensorflow-text=2.11.*" pip install -q tf-models-official Import libraries import os pip will install all models and dependencies automatically.To include the latest changes, you may install tf-models-nightly, which is the nightly Model Garden package created daily automatically. Note that it may not include the latest changes in the tensorflow_models GitHub repo. tf-models-official is the TensorFlow Model Garden package.Start by installing the TensorFlow Text and Model Garden pip packages. It shows how to do a lot of things manually, so you can learn how you can customize the workflow from data preprocessing to training, exporting and saving the model. On the other hand, if you're interested in deeper customization, follow this tutorial. If you're just trying to fine-tune a model, the TF Hub tutorial is a good starting point. For concrete examples of how to use the models from TF Hub, refer to the Solve Glue tasks using BERT tutorial. You can also find the pre-trained BERT model used in this tutorial on TensorFlow Hub (TF Hub). This tutorial demonstrates how to fine-tune a Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) model using TensorFlow Model Garden. ![]()
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