Fluent One – is a natural language platform that allows organizations to train and deploy sophisticated NLP models with ease. The platform offers a range of advanced NLP capabilities, including text classification, entity recognition, PII anonymization, and text summarization.
With Fluent One, organizations can easily and securely integrate natural language processing capabilities into their existing systems and processes. Our platform offers a simple, secure API that can be used across channels, making it easy for teams to access and use the tools they need to improve their NLP capabilities.
Fluent One is designed to be user-friendly and intuitive, with a range of tools and features that make it easy for organizations to train and deploy multiple NLP models. Whether you’re looking to improve customer service, streamline internal processes, or gain insights from unstructured data, Fluent One has the tools you need to achieve your goals.
Some of the key features of Fluent One include:
- Text classification: Easily classify text documents and identify the topics they cover
- Entity recognition: Identify and extract named entities from text, such as people, places, and organizations
- PII anonymization: Protect sensitive personal information by automatically anonymizing text
- Text summarization: Generate concise summaries of long documents or transcripts
In addition to these capabilities, Fluent One offers a range of other features and tools that can help organizations improve their NLP capabilities. For example, our platform allows organizations to train and deploy multiple models simultaneously, giving teams the flexibility to experiment and find the right approach for their specific needs. With Fluent One, organizations can quickly and easily integrate advanced NLP capabilities into their existing systems and processes.
Group APIs
The group feature allows multiple NL APIs to be grouped together and perform inference in parallel on the same input text. For example the text could be simultaneously classified by multiple text classification models and have key entities recognised by one or more entity recognition token classifiers. You might want to group inference calls together…
Metadata
The Fluent One platform allows for the easy association of metadata with individual labels in any classificaiton model. The ability to associate metadata with the labels in a text classification model can provide a number of benefits. First, it can make it easier to understand the meaning and context of the labels. For example, if…
Analytics
There are several reasons why you might want to track analytics from operational predictions made by a text classification model. First, tracking analytics can provide valuable insights into the performance of the model. This can include metrics such as the accuracy of the model’s predictions, the speed at which it processes data, and the number…
Batch Inference
Performing inference in batches can provide a number of benefits. First, it can improve the efficiency and speed of the inference process. When performing inference on a large dataset, it can be computationally intensive to process each piece of data individually. By grouping the data into batches and performing inference on the entire batch at…
NLP Applications
Text Classification
Text classification is a common task in natural language processing (NLP) where the goal is to assign a text document or a sequence of words to one or more predefined categories based on its content. Text classification can be used for a wide range of applications, including sentiment analysis, spam detection, topic classification, and language…
Entity Recognition
Entity recognition, also known as entity extraction or named entity recognition (NER), is a natural language processing (NLP) task that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and so on. Entity recognition is useful for a variety of applications, including information extraction, text summarization,…
PII Anonymisation
Natural language processing (NLP) models can be used to very effectively anonymize personally identifiable information (PII) in order to protect the privacy of individuals and comply with privacy regulations. Anonymization is the process of removing or obscuring PII from a text or other data source in such a way that the individual can no longer…
Text Summarisation
The same pre-trained deep learning model architecture used for text classification and entity recognition is also used to create our powerful text summarization feature, tuned specifically for summarizing call or chat transcripts. Text summarization is the process of automatically generating a shorter version of a piece of text that retains the most important information from…