Kashgari

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Kashgari is an NLP transfer learning framework built on TensorFlow Keras. It helps users build state-of-the-art models for text-labeling and text-classification tasks.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is Kashgari?

Kashgari is an open-source Natural Language Processing (NLP) transfer learning framework. It is built on top of TensorFlow Keras, providing a robust foundation for various text-related AI applications. The framework is specifically designed to facilitate text-labeling and text-classification tasks, making it suitable for a range of data annotation and categorization needs. Kashgari incorporates popular language embedding models such as Word2Vec, BERT, and GPT2, enabling users to leverage pre-trained knowledge for improved model performance. A key capability of Kashgari is its ability to assist users in building advanced models for named entity recognition (NER).

Best used for

Building and deploying advanced NLP models for text-labeling, text-classification, and named entity recognition using transfer learning.

Common actions

Build NLP models

Perform text classification

Conduct named entity recognition

Leverage transfer learning

Develop AI applications

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Capabilities

Key features

NLP transfer learning
TensorFlow Keras based
Text-labeling
Text-classification
Named entity recognition

Target Audience

Machine Learning EngineersData ScientistsNLP ResearchersSoftware Developers

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Does Kashgari support the latest transformer models beyond BERT and GPT2?

Kashgari is built on TensorFlow Keras and aims to integrate popular embedding models. While BERT and GPT2 are explicitly mentioned, its architecture allows for potential integration of other transformer models, depending on community contributions and updates to the framework. Users might need to check the latest documentation or GitHub for specific model support.

Can Kashgari be used for real-time NLP applications, or is it primarily for offline model training?

Kashgari focuses on simplifying the training and fine-tuning of NLP models using transfer learning. While the trained models can certainly be deployed for real-time inference, Kashgari itself is a framework for model development. Its performance in real-time scenarios would depend on the complexity of the trained model and the deployment infrastructure.

What are the typical hardware requirements for using Kashgari, especially with large language models?

As Kashgari leverages TensorFlow Keras and large language models like BERT, it benefits significantly from GPU acceleration. For efficient training, especially with larger datasets and complex models, a dedicated GPU with sufficient VRAM is highly recommended. CPU-only training is possible but will be considerably slower.

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