Named entity recognition (NER)- is a natural language processing task that performs two actions:
CoNNL: Named entities are phrases that contain the names of persons, organizations and locations
Most Named Entity Recognition tasks require large amounts of labeled data for…
In this article, we will build a pre-trained transformer model FashionBERT using the Hugging Face models.
The goal is to train a tokenizer and the transformer model, save the model and test it.
The dataset is a collection of 87K clothing product descriptions in Hebrew. …
Find and extract table from PDF file with pdfplumber library.
Python provides several libraries for PDF table extraction. Libraries like camelot, tabula-py and excalibur-py can easily find and extract the well-defined tables. But sometimes, all of these powerful libraries failed when you try to extract non-formatted tables.
pdfplumber is a…
All NLP (Natural Language Processing) tasks need text data for training. One of the largest text data sources is Wikipedia that offers free copies of all available content in many languages as dump files.
In this article, I will download, extract, and split into sentences text data from a Wikipedia…
How to build a simple search engine dashboard with Docker, Elasticsearch, and Plotly.
In this article, I will build a full-text search functionality that allows finding relevant articles by searching for a specific word or phrase across thousands of news articles.
Docker is a platform that packages…
In this article, we will classify women’s clothing product descriptions into 13 predefined classes. All descriptions are in Hebrew.
We will use two Scikit-Learn classifiers: Naive Bayes and Logistic Regression for multi-class machine learning algorithms.
To predict the class of the product given its description.
