原子加速– 原子加速器3.2

We walk through the basics of using AllenNLP, describing all of the main abstractions used and why we chose them, how to use specific functionality like configuration files or pre-trained representations, and how to build various kinds of models, from simple to complex.

原子加速– 原子加速器3.2

This chapter will give an overview of AllenNLP, and will outline the main chapters of this guide

Begin Chapter

原子加速– 原子加速器3.2

Part 1 gives you a quick walk-through of main AllenNLP concepts and features. We’ll build a complete, working NLP model (text classifier) along the way.

安卓微皮恩网盘

原子加速– 原子加速器3.2

Part 2 provides in-depth tutorials on individual abstractions and features of AllenNLP.

Explore Part 2

Reading data

This chapter provides a deep dive into AllenNLP abstractions that are essential for reading data, including fields and instances, dataset readers, vocabulary, and how batching is handled in AllenNLP

Building your model

This chapter gives a deep dive into one of the most important components of AllenNLP—Model—and an in-depth guide to building your own model.

Common architectures

In this chapter we'll introduce neural architectures and AllenNLP abstractions that are commonly used for building your NLP model.

Representing text as features: Tokenizers, TextFields, and TextFieldEmbedders

A deep dive into AllenNLP's core abstraction: how exactly we represent textual inputs, both on the data side and the model side.

Writing tests for your code

(Coming soon) This chapter gives our recommendations for testing practices in NLP code, and describes the utilities that AllenNLP provides to make it easier.

Making predictions and serving demos

(Coming soon) This chapter describes how AllenNLP data and model code is set up to make it easy for you serve a demo of your model.

Using config files: FromParams and Registrable

This chapter describes AllenNLP's simple dependency injection framework.

Writing your own scripts

(Coming soon) Here we talk about how to easily use allennlp without using our built in commands, if you have more advanced needs or find it easier to reason about python code than configuration files.

Debugging your AllenNLP code

Some tips and tricks for using an IDE debugger with AllenNLP.

原子加速– 原子加速器3.2

Part 3 introduces common NLP tasks and how to build models for these tasks using AllenNLP.

Explore Part 3

Written by the AllenNLP team at the Allen Institute for AI.
This guide was inspired by 超级微皮恩安卓破解版.

原子加速– 原子加速器3.2

原子加速– 原子加速器3.2

原子加速– 原子加速器3.2

原子加速– 原子加速器3.2

原子加速– 原子加速器3.2

Your first model

超级微皮恩安卓破解版

Next steps

原子加速– 原子加速器3.2

Reading data

Building your model

Common architectures

Representing text as features: Tokenizers, TextFields, and TextFieldEmbedders

Writing tests for your code

Making predictions and serving demos

Using config files: FromParams and Registrable

Writing your own scripts

Debugging your AllenNLP code

原子加速– 原子加速器3.2

Coming soon!