TensorLayerX's Natural Language Processing Algorithms Library, with a multi-scene model library, a simple and easy-to-use API, and deployment capabilities compatible with multiple hardware, is designed to improve the modeling efficiency of text processing for developers
Text Classification is one of the most basic tasks in natural language processing. It refers to the use of computer algorithms to automatically classify text sequences according to a certain classification system or standard. For example, a text classification model trained on the IMDB movie review dataset can categorize a movie review text into two types: positive and negative.
TensorLayerX provides a pre-trained text classification model:
The Conditional Text Generation task is to maximize the likelihood of the target text sequence y given the original text sequence X. Tasks such as language models, machine translation, and text summaries can be summarized as conditional text generation.
TensorLayerX provides the latest conditional text generation methods for machine translation, text summary, and other tasks:
Text Token Classification is the assignment of tags to certain tags in the text. Some common tag recognition subtasks are named entity recognition (NER) and part-of-speech (PoS) tags. NER models can be trained to identify specific entities in the text, such as dates, individuals, and places; For example, part-of-speech labeling can identify which words in the text are verbs, nouns, and punctuation symbols.
TensorLayerX provides the latest text tag recognition algorithms:
