How to Build a Multi-label NLP Classifier from Scratch by@michael-li

How to Build a Multi-label NLP Classifier from Scratch

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Kaggle is a good place to learn and practice your Machine Learning skills. It’s also a great place to find the proper dataset for your learning projects. We’ll use transfer learning for this task, to do that, we'll use a pre-trained model based on Wikipedia. To achieve the best results, we’d need to ‘fine-tune’ the model to make it learn a bit from our ‘comments’ dataset. Once the language model is fine-tuned, we can then use it to further do our classification task.
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Michael Li

| Product Manager | Machine Learning Practitioner | UI/UX Designer/Preacher | Full-Stack Developer |

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Attacking Toxic Comments Kaggle Competition Using Fast.ai

Kaggle is a good place to learn and practice your Machine Learning skills. It’s also a great place to find the proper dataset for your learning projects. I need a good classification NLP dataset to practice my recently learned fast.ai lesson, and I came across the Toxic Comment Classification Challenge. The competition is held two years ago and has long concluded, but it doesn’t hurt to submit my scores and see how well I did. This is one of the things Kaggle is great for since in the real world, it will usually be much harder to know how good or bad your model is, whereas, in Kaggle, you’ll see clearly where your performance is in the Leaderboard.

The Data Set

This competition is held by The Conversation AI team, a research initiative founded by Jigsaw and Google (both a part of Alphabet). Its goal is to find out the best model that can classify multiple toxicity types in comments. The toxicity types are:

toxic
severe_toxic
obscene
threat
insult
indentity_hate

Comments are given in a training file

train.cvs
and a testing file
test.csv
. And you’ll need to predict a probability of each type of toxicity for each comment in
test.csv
. It is a multi-label NLP classification problem.

Look at the Data

Let’s first take a look at the data. We need to import the necessary modules and do some logistics to set up the paths for our files.

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from fastai.text import *
from fastai import *

Notice here we imported everything from fastai.text and fastai modules. Are we against the software engineering best practice here? Actually, not quite. It’s rather a deliberate move in a more iterative and interactive data science kind of way. With all the library available, I can easily test and try different functions/modules without having to go back and import them every time. It will make the explore/experiment flow much more smoothly. But I digressed, let’s load the data and look at it:

# Kaggle store dataset in the /kaggle/input/ folder,
path = Path('/kaggle/input/jigsaw-toxic-comment-classification-challenge/')
path.ls()
# the /kaggle/input/ folder is read-only, copy away so I can also write to the folder. 
!mkdir data
!cp -a {path}/*.* ./data/
!ls data
# make sure everything is correctly copied over
path = Path('/kaggle/working/data/')
path.ls()
# read in the data and have a peak
df = pd.read_csv(path/'train.csv')
df.head()
image

(The toxicity types are one-hot encoded)

The comments are in

comment_text 
column and all toxicity types are ‘one-hot’ encoded, we’ll have to do something about it to make it fit into our model later.

image

(Have a look at one comment)

Transfer Learning: Fine-Tune Our Language Model

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We’ll use transfer learning for this task, to do that, we’ll use a pre-trained model based on Wikipedia called wikitext-103. It is a model that’s already trained from the Wikipedia dataset(or ‘corpus’ in NLP terms) to predict the next words from a giving unfinished sentence. We’ll leverage the ‘language knowledge’ the model already learned from the Wikipedia dataset and build on top of that. To achieve the best results, we’ll need to ‘fine-tune’ the model to make it learn a bit from our ‘comments’ dataset since what people say in the comments are not necessarily the same with the more formal Wiki. Once the language model is fine-tuned, we can then use it to further do our classification task.

Now let’s load the training data into the fast.ai

databunch 
so we can start training the language model first.

bs = 64   # set batch size to 64, works for Kaggle Kernels
data_lm = (TextList.from_df(df, path, cols='comment_text')
                .split_by_rand_pct(0.1)
                .label_for_lm()
                .databunch(bs=bs))

We use fast.ai’s

Data Block API
for this task. It is a very flexible and powerful way to address the challenging task of building a pipeline: loading your data into the model. It isolates the entire process into different parts/steps, each step with multiple methods/functions to adapt to different types of data and the ways data is stored. This concept is a lot like the Linux philosophy, highly modulized and with each module only do one thing but really really well. You are free to explore the wonderful API here, for the above code though, it does the following things:

1. Import data from Pandas DataFrame named

df
, tell the model to use
comment_text 
as input (
TextList.from_df(df, path, cols=’comment_text’)
) Note here I can also include the test.csv into the language model. It’s not considered ‘cheating’ since we are not using the labels, just do language model training.

2. Split the training dataset into train/validation set by random 10/90 percent. (

.split_by_rand_pct(0.1)
)

3. Ignore the given labels( since we are only fine-tuning the language model, not training the classifier yet) and use the language model’s ‘predict next word’ as labels. (

.label_for_lm()
)

4. Build the data into a

databunch
, with batch size
bs
. (
.databunch(bs=bs)
)

Now let’s look at the

databunch 
we just built:

image

(Notice we lost all the toxicity types)

Notice that the

databunch 
doesn’t have all the toxicity type labels since we are only fine-tuning the language model.

OK, time for some typical fast.ai learning rate adjustments and training:

image

We put our

databunch 
into a
language_model_learner
, tell it the language model base we want to use (
AWD_LSTM
) and assign a default dropout rate of 0.3. From the
LR Finder
graph, find the biggest downward slope and pick the middle point as our learning rate. (For a more detailed explanation of how this ‘fit_one_cycle’ magic is done, please refer to this article. It is a SOTA technique of fast.ai that combines learning rate and momentum annealing). Now we can ‘unfreeze’ the model and train the entire model couple of epochs:

image

We can look at one example of how well the model did:

image

The result is hardly optimal. But we at least get a sentence that actually makes sense and 0.38 accuracy for predicting the next word is not bad. Ideally, we need to train a bit more epochs but for this Kaggle Kernel, I was running out of GPU quota so I stopped at 4. The result definitely has room to improve and you can try it yourself. Anyway, what we want from the language model is the encoder part, so we save it.

Training the language model does take quite some time, but the good news is, for your own domain corpus, you only have to train once and later you can use it as a base for any other classification tasks.
# save the encoder for next step use
learn.save_encoder('fine_tuned_enc')

Transfer Learning: Training the Classifier

Let’s read in the test dataset:

test = pd.read_csv(path/"test.csv")
test_datalist = TextList.from_df(test, cols='comment_text')

Again, build our

databunch
:

data_cls = (TextList.from_csv(path, 'train.csv', cols='comment_text', vocab=data_lm.vocab)
                .split_by_rand_pct(valid_pct=0.1)
                .label_from_df(cols=['toxic', 'severe_toxic','obscene', 'threat', 'insult', 'identity_hate'], label_cls=MultiCategoryList, one_hot=True)
                .add_test(test_datalist)
                .databunch())
data_cls.save('data_clas.pkl')

Please note the difference this time:

1. When building the

TextList 
, we specified
vocab=data_lm.vocab
 , this way we make sure we are using the same vocabulary and our training on the language model can be properly applied to the classifier model.

2. We now use all our toxicity styles labels (

.label_from_df(cols=[‘toxic’, ‘severe_toxic’,’obscene’, ‘threat’, ‘insult’, ‘identity_hate’],label_cls=MultiCategoryList, one_hot=True),
)

3. We added our test set here. (

.add_test(test_datalist)
)

Now look at our classifier

databunch 
:

image

(Note that now we have all the toxicity styles labels)

Finally, time to put everything together! We’ll put the

databunch 
into the
text_classifier_learner 
model and load the encoder we learned from the language model.

learn = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.5)
learn.load_encoder('fine_tuned_enc')

Again, find the best learning rate and train one cycle:

image

Train a bit more cycles and unfreeze:

image
image
image

See the results:

image

Off by one, but overall the prediction is OK. For the purpose of reference, I submitted the prediction to Kaggle and get a 0.98098 Public Score (land in the middle of the Public Leader Board). The result is not optimal but like I said I didn’t train all the way due to limited GPU. The purpose of this article is to show you the whole process of using fast.ai to tackle multi-labels text classification problem. The real challenge here is to load the data into the model using Data Block API.

Conclusion

I hope you learned a thing or two from this article. Fast.ai is really a lean, flexible and powerful library. For the things it can do (like image/text classification, tabular data, collaborative filtering, etc.), it does it very well. It is not as extensive as Keras, but it’s very sharp and focused. Kind of like Vim and Emacs if you are familiar with the command line text editor war. 😜

You can find the Kaggle Kernel here.

Any feedback or constructive criticism is welcomed. You can either find me on Twitter @lymenlee or my blog site wayofnumbers.com.



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by Michael Li @michael-li.| Product Manager | Machine Learning Practitioner | UI/UX Designer/Preacher | Full-Stack Developer |
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