I definitely agree that the NLP library shouldn&#x27;t have an opinion about your machine learning, shouldn&#x27;t give you every accuracy measure under the sun, etc. My original vision for spaCy was smaller. But there are two problems.<p>1) If I ship you a statistical model, and it&#x27;s late in a pipeline, like a parser, the earlier components in the pipeline are not swappable. If you change the tokenization, POS tagging, lemmatization etc, the parser model will give you worse output.<p>This isn&#x27;t obvious to people, and the problem can be subtle. For instance, some NER models use POS tag features, others don&#x27;t.<p>2) The output format isn&#x27;t actually that convenient. It sucks that everyone has to write this tree processing code, and then aligning the tokenized output back to the original string is a pain, if you want to calculate mark-up.