Marie Haynes recently had a really insightful podcast interview with John Muller. I specifically enjoyed the conversation about BERT and its potential for content quality evaluation. Google has repeatedly said that it helps understand natural language better. Content quality assessment like humans do is still fairly complicated for machines to do.
On the other hand, keywords stuffing is something that is easier for machines to spot.
Question Answering with a Fine-Tuned BERT
One way to check that is to see if the text is written in a nonsensical way. This is the part that got me excited. Trying this out is a great idea and precisely what we will do in this article. Britney Muller from Moz shared a really good idea and Python notebook with the code to test it. Hmm, but isn't that measuring 'quality' in a way? It could be used as one of several proxies for content quality.
We are going to use Ludwiga very powerful and code-free deep learning toolkit from Uber, to do the same. I coded a simple to follow Google Colab notebook with all the steps. You can use the form at the top to test the code on your own articles. You might need to change the CSS selector to extract relevant text per target page. The one included works with SEL articles. When you compare the original notebook to the one that I createdyou will find that we avoided having to write a lot of advanced deep learning code.
In order to create our cutting edge model with Ludwigwe need to complete four simple steps:. You should be able to follow each of these steps in the notebook.
I will explain my choices here and some of the nuances needed to make it work. Google Colab comes with Tensorflow version 2. But, Ludwig requires version 1.We all know BERT is a compelling language model which has already been applied to various kinds of downstream tasks, such as Sentiment Analysis and Question answering QA. In some of them, it over-performed human beings!
Have you ever tried it on text binary classification? Honestly, till recently, my answer was still NO. I want to control the useful parameters, such as the number of epochs and batch size. I am a spoiled machine learning user after I tried all other friendly frameworks. For example, in Scikit-learn, if you try to build a tree classifier, here is almost all your code.
If you want to do image classification in fast. Not only you can get the classification result, but an activation map as well. On Monday, I found this Colab Notebook.
BERT for dummies — Step by Step Tutorial
However, when I opened it, I found there are still too many details for a user who only cares about the application of text classification. So I tried to refactor the code, and I made it. However, originally, there were still a lot of codes in the notebook.
So I asked my readers to help me package them. Please follow this link and you will see the IPynb Notebook file on github.
Google Colab will be opened automatically. You only need to do four things after that. Let us install bert-text package and load the API. My example is a sample dataset of IMDB reviews. It contains positive and negative samples in training set, while the testing set contains positive and negative samples.
You need to run the following line to make sure the training data is shuffled correctly. Your dataset should be stored in Pandas Data Frame. There should be one training set, called train and one testing set, called test. Both of them should at least contain two columns. One column is for the text, and the other one is for the binary label. It is highly recommended to select 0 and 1 as label values. Now that your data is ready, you can set the parameters. The first two parameters are just the name of columns of your data frame.
You can change them accordingly.
The third parameter is the learning rate. You need to read the original paper to figure out how to select it wisely.Because NLP is a diversified field with many distinct tasks, most task-specific datasets contain only a few thousand or a few hundred thousand human-labeled training examples. However, modern deep learning-based NLP models see benefits from much larger amounts of data, improving when trained on millions, or billionsof annotated training examples.
To help close this gap in data, researchers have developed a variety of techniques for training general purpose language representation models using the enormous amount of unannotated text on the web known as pre-training.
The pre-trained model can then be fine-tuned on small-data NLP tasks like question answering and sentiment analysisresulting in substantial accuracy improvements compared to training on these datasets from scratch. With this release, anyone in the world can train their own state-of-the-art question answering system or a variety of other models in about 30 minutes on a single Cloud TPUor in a few hours using a single GPU.
The release includes source code built on top of TensorFlow and a number of pre-trained language representation models. However, unlike these previous models, BERT is the first deeply bidirectionalunsupervised language representation, pre-trained using only a plain text corpus in this case, Wikipedia. Why does this matter? Pre-trained representations can either be context-free or contextualand contextual representations can further be unidirectional or bidirectional.
Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary. The arrows indicate the information flow from one layer to the next.
To understand why, consider that unidirectional models are efficiently trained by predicting each word conditioned on the previous words in the sentence. To solve this problem, we use the straightforward technique of masking out some of the words in the input and then condition each word bidirectionally to predict the masked words. For example:. Follow googleai. Give us feedback in our Product Forums.
Google Privacy Terms.To get the most of this tutorial, we suggest using this Colab Version. This will allow you to experiment with the information presented below. Author : Jianyu Huang. Reviewed by : Raghuraman Krishnamoorthi. Edited by : Jessica Lin. With this step-by-step journey, we would like to demonstrate how to convert a well-known state-of-the-art model like BERT into dynamic quantized model.
In addition, we also install scikit-learn package, as we will reuse its built-in F1 score calculation helper function. Because we will be using the beta parts of the PyTorch, it is recommended to install the latest version of torch and torchvision. You can find the most recent instructions on local installation here. For example, to install on Mac:. We set the number of threads to compare the single thread performance between FP32 and INT8 performance. In the end of the tutorial, the user can set other number of threads by building PyTorch with right parallel backend.
How to evaluate content quality with BERT
The helper functions are built-in in transformers library. We mainly use the following helper functions: one for converting the text examples into the feature vectors; The other one for measuring the F1 score of the predicted result. The relative contribution of precision and recall to the F1 score are equal. The spirit of BERT is to pre-train the language representations and then to fine-tune the deep bi-directional representations on a wide range of tasks with minimal task-dependent parameters, and achieves state-of-the-art results.
Here we set the global configurations for evaluating the fine-tuned BERT model before and after the dynamic quantization. We reuse the tokenize and evaluation function from Huggingface. We call torch. Running this locally on a MacBook Pro, without quantization, inference for all examples in MRPC dataset takes about seconds, and with quantization it takes just about 90 seconds. We have 0. As a comparison, in a recent paper Table 1it achieved 0.
The main difference is that we support the asymmetric quantization in PyTorch while that paper supports the symmetric quantization only. Note that we set the number of threads to 1 for the single-thread comparison in this tutorial. We also support the intra-op parallelization for these quantized INT8 operators. The users can now set multi-thread by torch.This is the 23rd article in my series of articles on Python for NLP. In the previous article of this series, I explained how to perform neural machine translation using seq2seq architecture with Python's Keras library for deep learning.
In this article we will study BERTwhich stands for Bidirectional Encoder Representations from Transformers and its application to text classification. If you have no idea of how word embeddings work, take a look at my article on word embeddings. Like word embeddings, BERT is also a text representation technique which is a fusion of variety of state-of-the-art deep learning algorithms, such as bidirectional encoder LSTM and Transformers.
BERT was developed by researchers at Google in and has been proven to be state-of-the-art for a variety of natural language processing tasks such text classification, text summarization, text generation, etc. Just recently, Google announced that BERT is being used as a core part of their search algorithm to better understand queries.
In this article we will not go into the mathematical details of how BERT is implemented, as there are plenty of resources already available online.Language Learning with BERT - TensorFlow and Deep Learning Singapore
The dataset used in this article can be downloaded from this Kaggle link. If you download the dataset and extract the compressed file, you will see a CSV file. The file contains 50, records and two columns: review and sentiment.
The review column contains text for the review and the sentiment column contains sentiment for the review. The sentiment column can have two values i.
On the test set the maximum accuracy achieved was Let's see if we can get better accuracy using BERT representation. Next, you need to make sure that you are running TensorFlow 2. Google Colab, by default, doesn't run your script on TensorFlow 2.
Therefore, to make sure that you are running your script via TensorFlow 2. In the above script, in addition to TensorFlow 2. Finally, if in the output you see the following output, you are good to go:.
The script also prints the shape of the dataset. Next, we will preprocess our data to remove any punctuations and special characters. To do so, we will define a function that takes as input a raw text review and returns the corresponding cleaned text review.
The review column contains text while the sentiment column contains sentiments.Is BERT the greatest search engine ever, able to find the answer to any question we pose it? For something like text classification, you definitely want to fine-tune BERT on your own dataset. The task posed by the SQuAD benchmark is a little different than you might think.
The SQuAD homepage has a fantastic tool for exploring the questions and reference text for this dataset, and even shows the predictions made by top-performing models. For example, here are some interesting examples on the topic of Super Bowl The two pieces of text are separated by the special [SEP] token. For every token in the text, we feed its final embedding into the start token classifier. Whichever word has the highest probability of being the start token is the one that we pick.
If you do want to fine-tune on your own dataset, it is possible to fine-tune BERT for question answering yourself. Note: The example code in this Notebook is a commented and expanded version of the short example provided in the transformers documentation here. This example uses the transformers library by huggingface. This class supports fine-tuning, but for this example we will keep things simpler and load a BERT model that has already been fine-tuned for the SQuAD benchmark.
The transformers library has a large collection of pre-trained models which you can reference by name and load easily. The full list is in their documentation here.
BERT-large is really big… it has layers and an embedding size of 1, for a total of M parameters! Altogether it is 1. Side note: Apparently the vocabulary of this model is identicaly to the one in bert-base-uncased. You can load the tokenizer from bert-base-uncased and that works just as well. A QA example consists of a question and a passage of text containing the answer to that question.
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The original example code does not perform any padding. I suspect that this is because we are only feeding in a single example. If we instead fed in a batch of examples, then we would need to pad or truncate all of the samples in the batch to a single length, and supply an attention mask to tell BERT to ignore the padding tokens.
I was curious to see what the scores were for all of the words. The following cells generate bar plots showing the start and end scores for every word in the input. I also tried visualizing both the start and end scores on a single bar plot, but I think it may actually be more confusing then seeing them separately.
Links My video walkthrough on this topic. The blog post version. The Colab Notebook. Install huggingface transformers library 2.To start, we load the WikiText-2 dataset as minibatches of pretraining examples for masked language modeling and next sentence prediction. The batch size is and the maximum length of a BERT input sequence is Note that in the original BERT model, the maximum length is Notably, the former has million parameters while the latter has million parameters.
For demonstration with ease, we define a small BERT, using 2 layers, hidden units, and 2 self-attention heads. Given the shard of training examples, this function computes the loss for both the masked language modeling and next sentence prediction tasks.
Note that the final loss of BERT pretraining is just the sum of both the masked language modeling loss and the next sentence prediction loss. Training BERT can take very long. We can plot both the masked language modeling loss and the next sentence prediction loss during BERT pretraining. After pretraing BERT, we can use it to represent single text, text pairs, or any token in them.
This supports that BERT representations are context-sensitive. In Section 15we will fine-tune a pretrained BERT model for downstream natural language processing applications. The original BERT has two versions, where the base model has million parameters and the large model has million parameters. In the experiment, the same token has different BERT representation when their contexts are different. In the experiment, we can see that the masked language modeling loss is significantly higher than the next sentence prediction loss.
Do you encounter any error when running this section? Colab [pytorch] Open the notebook in Colab. Colab [tensorflow] Open the notebook in Colab. Trainer net. MLM loss 7. Table Of Contents Pretraining BERT Summary Previous Next Natural Language Processing: Applications.