1 line to BERT Word Embeddings with NLU in Python

Christian Kasim Loan

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spark-nlp

5 min readSep 21, 2020

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

With the freshly released NLU library which gives you 350+ NLP models and 100+ Word Embeddings, you have infinite possibilities to explore your data and gain insights.

In this tutorial, we will cover how to get the powerful BERT embeddings with 1 line of NLU code and then how to visualize them with t-SNE.

T-SNE [1] 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.

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

nlu.load('Bert').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 wich 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 bert')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 dataframe

# 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.bert_embeddings])

6. Fit T-SNE

Finally, we fit the T-SNE algorithm and get our 2-Dimensional representation of our Bert 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 Bert 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 BERT Embeddings, colored by Part of Speech Tag')

8. Plot Bert 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 BERT Embeddings, colored by Emotion')

9. Plot Bert 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 BERT Embeddings, colored by Sarcasm label')

10. There are many many more word Embeddings!

To view all word embeddings, type the following command

nlu.print_all_model_kinds_for_action('embed')

11. More NLU Medium articles