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prose vs Transformers: What are the differences?
Developers describe prose as "A Golang library for text processing". prose is a natural language processing library (English only, at the moment) in pure Go. It supports tokenization, segmentation, part-of-speech tagging, and named-entity extraction. On the other hand, Transformers is detailed as "State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0". It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
prose and Transformers can be primarily classified as "NLP / Sentiment Analysis" tools.
Some of the features offered by prose are:
- Tokenizing
- Segmenting
- Tagging, NER
On the other hand, Transformers provides the following key features:
- High performance on NLU and NLG tasks
- Low barrier to entry for educators and practitioners
- Deep learning researchers
prose is an open source tool with 2.5K GitHub stars and 116 GitHub forks. Here's a link to prose's open source repository on GitHub.