Human supervision & annotation of data will increase in importance, and numerous tools are being developed to accelerate the annotation and enrichment of data.

Introduction

I like to think of data as having four dimensions;

1. Data Discovery
2. Data Design
3. Data Development
4. Data Delivery

Data Discovery can consist of a number of layers of refinement; eventually reaching a very granular level of classification. We have already seen the introduction of nested or sub-intents in a few chatbot development frameworks.

A first step in Data Discovery can include data visualisation; a recent project which is focussed on visualising RAG data attracted quite a bit of attention.

Embeddings

In simple terms, embeddings are used to create a spacial representation or mapping, of sentences or chunks of data. This spacial representations are based on the semantic meaning of text; and hence text which have the same semantic meaning are clustered together.

When these clusters are displayed visually, it creates a visual representation of what topics/intents/classes are more prevalent. It also helps in finding outliers and conversations or topics not discovered previously.

Data visualisation is the presentation of data in a graphical or visual format. The goal of data visualisation is to communicate complex information in a simplified and understandable way, making it easier to detect patterns, trends, and insights from the data.

Bulk

Bulk is open-source code which make it easy to experiment with embeddings and visualisation.

Data Preparation

For this demo I downloaded a list of sentences from Kaggle; in total I made use of 1,035 sentences. In the image below, the top section shows the sentences in a CSV file.

The bottom section shows a CSV where the embeddings are created for the utterances. Notice the x and y fields added to the end.

These coordinates represents the semantic similarity of the sentences. This data set contains sentences which are very ambiguous in meaning; hence the signal is not strong. Data from an enterprise or customer conversations should have a stronger signal and hence better defined classifications.

The code below is working Python code to run the embeddings and CSV creation process in a notebook. I made use of Colab to run the code, and upload and download the CSV files.

You might notice that the code look different to that on the Github page; but I needed to make some changes to get it to work.

import umap.umap_ as umap

import pandas as pd
import umap.umap_ as UMAP
from sklearn.pipeline import make_pipeline 


import pandas as pd
import umap.umap_ as UMAP
from sklearn.pipeline import make_pipeline 

# pip install "embetter[text]"
from embetter.text import SentenceEncoder

# Build a sentence encoder pipeline with UMAP at the end.
text_emb_pipeline = make_pipeline(
  SentenceEncoder('all-MiniLM-L6-v2'),
  umap.UMAP()
)

# Load sentences
sentences = list(pd.read_csv("/original.csv")['sentences'])

# Calculate embeddings 
X_tfm = text_emb_pipeline.fit_transform(sentences)

# Write to disk. Note! Text column must be named "text"
df = pd.DataFrame({"text": sentences})
df['x'] = X_tfm[:, 0]df['y'] = X_tfm[:, 1]df.to_csv("/ready.csv", index=False)

Visualisation

After installing Bulk with this command:

python -m pip install --upgrade pip
python -m pip install bulk

The application can be run with the command:

python -m bulk text ready.csv

A web instance is launched, with the text on the right, and the visual representation on the left. There are a number of tools available to interrogate the visual representation.

Each dot, represents a sentence; and as a selection is made, the text is displayed on the right. These groupings can be used to created initial classes or intents for more granular refinement.