Support for NoisyNER dataset

[teresaloeffelhardt]

Hi,

This PR adds support for the NoisyNER dataset as proposed in this paper and released in this repo:

NoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models. It is from the NLP/Information Extraction domain and was created through a realistic distant supervision technique. Some highlights and interesting aspects of the data are:

• Seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances
• Full parallel clean labels available to compute upper performance bounds or study scenarios where a small amount of gold-standard data can be leveraged
• Skewed label distribution (typical for Named Entity Recognition tasks)
• For some label sets: noise level higher than the true label probability
• Sequential dependencies between the labels

[helpmefindaname]

please use the google docstring format as described in our docs

[teresaloeffelhardt]

Hi. I have made all the requested changes. Is the description in the correct format now?

[alanakbik]

@teresaloeffelhardt thanks a lot for adding this!