Anago

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anago is an Open Source AI tool that provides sequence labeling for tasks like Named Entity Recognition and Part-of-Speech Tagging. It is implemented in Keras and supports Bidirectional LSTM-CRF and ELMo models.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is anago?

anago is a Python library designed for sequence labeling tasks, including Named Entity Recognition (NER) and Part-of-Speech (PoS) Tagging. Built with Keras, it leverages advanced models like Bidirectional LSTM-CRF and ELMo to achieve high performance. A key differentiator is its independence from language-dependent features, making it easily adaptable for various languages. The library offers essential methods for model training, evaluation, and text tagging, along with support for custom models, pre-trained model downloads, and GPU acceleration. It's particularly useful for researchers and developers working on natural language processing applications.

Best used for

Ideal for machine learning engineers and professors who need to implement advanced sequence labeling tasks like Named Entity Recognition and Part-of-Speech Tagging. Especially valuable for developing NLP applications that require high accuracy and adaptability across different languages, leveraging state-of-the-art models like ELMo.

Common actions

perform named entity recognition

perform part-of-speech tagging

train sequence labeling models

analyze text data

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Capabilities

Key features

Named Entity Recognition
Part-of-Speech Tagging
Bidirectional LSTM-CRF
ELMo support
Model training/evaluation
Pre-trained model download
GPU support

Target Audience

professorsoftware developermachine learning engineer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of sequence labeling tasks can anago perform?

anago is capable of handling various sequence labeling tasks, including Named Entity Recognition (NER), Part-of-Speech (PoS) Tagging, and Semantic Role Labeling (SRL). Its architecture is designed to be flexible for different types of sequence-based text analysis.

Does anago require language-specific features for its models?

No, anago is designed to be language-independent. Unlike traditional sequence labeling solvers, it does not require defining any language-dependent features, making it easy to apply the library to any language without extensive customization.

What are the key technologies used in anago for performance?

anago utilizes advanced deep learning architectures for high performance. It is built with Keras and incorporates Bidirectional LSTM-CRF (Long Short-Term Memory - Conditional Random Field) models, along with support for ELMo (Embeddings from Language Models) for enhanced contextual understanding.

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