* Generates an URL string from a map of url parameters * @param {{}} urlParameters - the map of parameters * @returns {string} - an URI stri

Sample `length` words from the model. Code strongly inspired by https://github.com/huggingface/pytorch-pretrained-BERT/blob/m

Filters logits to only the top k choices from https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_gpt2.py

In the subclass, you need to load all necessary components for the other functions. Typically, this will comprise a tokenizer

Function that GLTR interacts with to check the probabilities of words Params: - in_text: str -- The text that you want to ch

Same behavior as GPT-2 Extra param: max_context controls how many words should be fed in left and right Speeds up inf

* Simple constructor. Subclasses should call @superInit(options) as well. * see why here: https://stackoverflow.com/questions/43595943/why-are-de

* Read all URL parameters into a map. * @returns {Map} the url parameters as a key-value store (ES6 map)

clean up the tokens from any special chars and encode leading space by UTF-8 code '\u0120', linebreak with UTF-8 code 266 '\u010A'

serves all files from the data dir to ``/data/<path:path>`` :param path: path from api call

serves all files from ./client/ to ``/client/<path:path>`` :param path: path from api call

* Has to be called as last call in subclass constructor. * @param {{}} options * @param defaultLayers -- create the default <g> layers: bg -

* Every time data has changed, update is called and * triggers wrangling and re-rendering * @param {Object} data data object * @return

* Updates instance options * @param {Object} options only the options that should be updated * @param {Boolean} reRender if option change re