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How much training data does BERT need?

How much training data does BERT need?

We find that only 25 training examples per intent are required for our BERT model to achieve 94\% intent accuracy compared to 98\% with the entire datasets, challenging the belief that large amounts of labeled data are required for high performance in intent recognition.

How do you fine tune the BERT model?

Fine-tuning a BERT model

  1. On this page.
  2. Setup. Install the TensorFlow Model Garden pip package. Imports. Resources.
  3. The data. Get the dataset from TensorFlow Datasets. The BERT tokenizer. Preprocess the data.
  4. The model. Build the model. Restore the encoder weights.
  5. Appendix. Re-encoding a large dataset. TFModels BERT on TFHub.
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How many epochs do you need to train a BERT?

BERT based original model is trained with 3 epoch, and BERT with additional layer is trained on 4 epoch.

How long does it take to Pretrain BERT?

about 54 hours
What would it take? Pre-training a BERT-Base model on a TPUv2 will take about 54 hours. Google Colab is not designed for executing such long-running jobs and will interrupt the training process every 8 hours or so. For uninterrupted training, consider using a paid pre-emptible TPUv2 instance.

Does BERT need a lot of data?

The most important thing about BERT training is that it only requires unlabelled data — any text corpus can be used, you do not need any special labelled dataset. The BERT paper used Wikipedia and a book corpus for training the model. As with “normal” language models, data comes cheap, and this is a huge advantage.

How many words do you need to train a BERT?

Second, BERT is pre-trained on a large corpus of unlabelled text including the entire Wikipedia(that’s 2,500 million words!) and Book Corpus (800 million words)….

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BERT-Base, Uncased 12-layer, 768-hidden, 12-heads, 110M parameters
BERT-Large, Uncased 24-layer, 1024-hidden, 16-heads, 340M parameters

How long does fine-tuning BERT take?

As you can see, I only have 22.000 parameters to learn I don’t understand why it takes so long per epoch (almost 10 min). Before using BERT, I used a classic Bidirectional LSTM model with more than 1M parameters and it only took 15 seconds per epoch.

WHAT IS FINE TUNE IN BERT?

Fine-Tuning the Core. The core of BERT is trained using two methods, next sentence prediction (NSP) and masked-language modeling (MLM). Two consecutive sentences result in a ‘true pair’, anything else is not a true pair. BERTs task here is to accurately identify which pairs genuinely are pairs, and which are not.

What does fine-tuning BERT mean?

It is a bidirectional transformer pre-trained model developed using a combination of two tasks namely: masked language modeling objective and next sentence prediction on a large corpus. …

How can I speed up BERT training?

The first (or even zeroth) thing to speed up BERT training is to distribute it on a larger cluster. While the original BERT was already trained using several machines, there are some optimized solutions for distributed training of BERT (e.g. from Alibaba or NVIDIA).

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What is fine tune BERT?

What is Model Fine-Tuning? BERT (Bidirectional Encoder Representations from Transformers) is a big neural network architecture, with a huge number of parameters, that can range from 100 million to over 300 million. So, training a BERT model from scratch on a small dataset would result in overfitting.