1 line to ALBERT Word Embeddings with NLU in Python

Christian Kasim Loan

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Published in

spark-nlp

6 min readJan 17, 2021

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Including Part of Speech, Named Entity Recognition, Emotion Classification in the same line! With Bonus t-SNE plots!

0. Introduction

0.1 What is NLU?

John Snow Labs NLU library gives you 1000+ NLP models and 100+ Word Embeddings in 300+ languages and infinite possibilities to explore your data and gain insights.

In this tutorial, we will cover how to get the powerful Albert Embeddings with 1 line of NLU code and then how to visualize them with t-SNE. We will compare Comparing Sentiment with Sarcasm and Emotions!

0.2 What is t-SNE?

T-SNE is a tool to visualize high-dimensional data. It converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data. t-SNE has a cost function that is not convex, i.e. with different initializations we can get different results.

0.3 What is ALBERT?

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point scaling to larger machines becomes infeasible. To address this problem, two parameter reduction techniques are introduced which lower memory consumption and increase the training speed of BERT. This scales much better compared to the original BERT. A new self-supervised loss that focuses on modeling inter-sentence coherence is used. As a result, the best ALBERT model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.

1. Import NLU, load Albert, and embed a sample string in 1 line

nlu.load('albert').predict('He was suprised by the diversity of NLU')

2. Load a larger dataset

The following snippet will download a Reddit sarcasm dataset and load it to a Pandas Dataframe

import pandas as pd# Download the dataset! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp# Load dataset to Pandasdf = pd.read_csv('/tmp/train-balanced-sarcasm.csv')df

3. Predict on the dataset and also add Part of Speech, Emotion and Sentiment Classifiers

Since adding additional classifiers and getting their predictions is so easy in NLU, we will extend our NLU pipeline with a POS, Emotion, and Sentiment classifier which all achieve results close to the state of the art.

Those extra predictions will also come in handy when plotting our results.
We are limiting ourselves to a subsection of the dataset because our RAM is sadly limited and we are not running on a cluster. With Spark NLP you can take exactly the same models and run them in a scalable fashion inside of a Spark cluster

pipe = nlu.load('pos sentiment emotion albert')df['text'] = df['comment']# NLU to gives us one row per embedded word by specifying the output level
predictions = pipe.predict(df[['text','label']], output_level='token')predictions

4. Emotion Plots

We can quickly plot the distribution of predicted emotions using pandas functions on the data frame

# Some Tokens are None which we must drop firstpredictions.dropna(how='any', inplace=True)# Some sentiment are 'na' which we must drop firstpredictions = predictions[predictions.emotion!= 'na']predictions.emotion.value_counts().plot.bar(title='Dataset emotion distribution')

5. Prepare data for T-SNE

We prepare the data for the T-SNE algorithm by collecting them in a matrix for TSNE

import numpy as npmat = np.matrix([x for x in predictions.albert_embeddings])

6. Fit T-SNE

Finally, we fit the T-SNE algorithm and get our 2-Dimensional representation of our Albert Word Embeddings

from sklearn.manifold import TSNEmodel = TSNE(n_components=2)low_dim_data = model.fit_transform(mat)print('Lower dim data has shape',low_dim_data.shape)

7. Plot ALBERT Word Embeddings, colored by Part of Speech Tag

The following plots show scatter plots for the 2-D representation of the Word Embeddings. Each point represents a word in a sentence and the color represents the POS class that word belongs to.tsne_df = pd.DataFrame(low_dim_data, predictions.pos)ax = sns.scatterplot(data=tsne_df, x=0, y=1, hue=tsne_df.index)ax.set_title(‘T-SNE ALBERT Embeddings, colored by Part of Speech Tag’)

tsne_df =  pd.DataFrame(low_dim_data, predictions.pos)ax = sns.scatterplot(data=tsne_df, x=0, y=1, hue=tsne_df.index)ax.set_title('T-SNE ALBERT Embeddings, colored by Part of Speech Tag')

8. Plot Plot Albert Word Embeddings, colored by Emotion

The following plots show scatter plots for the 2-D representation of the Word Embeddings. Each point represents a word from a sentence that was classified with a particular emotion, which reflects in the colors

tsne_df =  pd.DataFrame(low_dim_data, predictions.emotion)ax =  sns.scatterplot(data=tsne_df, x=0, y=1, hue=tsne_df.index)ax.set_title('T-SNE ALBERT Embeddings, colored by Emotion')

9. Plot Plot Albert Word Embeddings, colored by Sarcasm

The following plots show scatter plots for the 2-D representation of the Word Embeddings. Each point represents a word from a sentence that was classified as sarcastic or not, which reflects in the colors

tsne_df =  pd.DataFrame(low_dim_data, predictions.label.replace({1:'sarcasm',0:'normal'}))
tsne_df.columns=['x','y']ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)ax.set_title('T-SNE ALBERT Embeddings, colored by Sarcasm label')

9. Plot Plot Albert Word Embeddings, colored by Sentiment