Cohere Tutorial: A Complete Guide to Text Classification Using Cohere

The Magic of Natural Language Processing

Welcome to the fascinating world of Natural Language Processing (NLP), a unique blend of computer science and linguistics that focuses on the interaction between computers and human languages. At its core, NLP is all about developing advanced algorithms that can understand and produce human language with remarkable accuracy.

The Ultimate Goal of NLP

The long-term objective of NLP is to create computational models of human language that can perform a wide array of tasks. These tasks range from automatic translation and summarization to question answering and information extraction, among others. The research in this field is highly interdisciplinary, involving experts from linguistics, cognitive science, artificial intelligence, and computer science.

The Diverse Methods in NLP

NLP employs a variety of methods, including rule-based methods, statistical methods, and neural computed methods. Rule-based methods rely on hand-crafted rules written by NLP experts. While these methods can be highly effective for specific tasks, they often require a lot of effort to maintain and are limited in their scope. On the other hand, statistical methods use large amounts of data to train computational models, which can then be used to perform various NLP tasks automatically.

The Role of Neural Networks in NLP

Neural networks, a type of machine learning algorithm, are particularly well-suited for NLP tasks. They have been used to create state-of-the-art models for tasks such as machine translation and classification, showcasing the immense potential of this technology.

Cohere: An Overview

Cohere is a powerful neural network tool capable of generating, embedding, and classifying text. In this tutorial, we will use Cohere to classify text descriptions. To get started, you will need to create an account on Cohere and obtain an API key.

Getting Started with Cohere

We will be programming in Python, so first, we need to install the Cohere library using pip:

pip install cohere

Next, we will implement cohere.Client, passing in our generated API key and the version (2021-11-08).

The Dataset

The core component of any neural network is its dataset. In this tutorial, we will use a dataset containing 1000 descriptions divided into 10 classes. You can download the dataset from this link.

The downloaded dataset consists of 10 folders, each containing 100 text files with descriptions. The files are named according to their respective labels, e.g., sport_3.txt.

Loading the Dataset

We need to load all the data using the load_examples function, which utilizes three external libraries:

• os.path: To navigate through the folder containing data (this is part of Python's standard library).
• numpy: Used for working with arrays. Install it via pip install numpy.
• glob: Helps in reading all filenames in the folder. Install it via pip install glob.

Extracting Paths of Examples

The dataset should be extracted in a folder named data. The os.path.join method will help in obtaining universal paths, and we can use the glob method to gather all names of folders, which will represent the labels.

We ensure that the Cohere training dataset contains no more than 50 examples, and each class has at least 5 examples. Looping through the folder names, we will gather paths of the examples into a new list called examples_path.

Loading Descriptions

Next, we create a training set. Using the load_examples() function, we will read descriptions from the text files. Each description will have a maximum length of 100 characters. The result will be a list containing [description, label] pairs.

Cohere Classifier Setup

Returning to the Cohere class, we will implement two methods: one for loading examples and another for classifying input text.

Implementing the Cohere Class

The first method will create a list of examples with the cohere.classify.Example method. The second method utilizes the Cohere library to classify the input. It accepts several parameters, including:

• model: the machine learning model to use.
• inputs: list of data to classify.
• examples: list of training set examples.

In our tutorial, we will implement this functionality as a part of our CoHere class. The output will include the input, the predictions, and a list of confidence ratings for each class.

Creating a Web Application with Streamlit

We will use Streamlit to create a web application featuring a text input box for users to enter their text and display the likelihood of predictions.

Installation Requirements

To set up the application, we need text inputs for both the Cohere API key and the text to predict.

In Streamlit, the following methods will be utilized:

• st.header(): to create a header for the app.
• st.text_input(): for user text input.
• st.button(): to initiate processing.
• st.write(): to display results.
• st.progress(): to show progress.
• st.column(): to format the app structure.

Conclusion: Harnessing Cohere for Text Classification

This tutorial showcased the potential of using Cohere models not only for text generation but also for effective text classification. We achieved high prediction likelihoods even with a limited dataset of just 50 examples across 10 classes.

Next Steps

Identify a problem around you that could be addressed with a Cohere application. Engage in building one to harness the power of NLP! Stay tuned for future tutorials as we delve deeper into the capabilities of Cohere models. The learning journey continues!

For further reference, check out the repository of the code here.