Example (from TfidfTransformer)

if isinstance(docs[0], tuple):
    docs = [docs]
return [self.gensim_model[doc] for doc in docs]

This method expects a list of tuples, instead of an iterable. This means that the entire corpus has to be stored as a lis

I tried selecting hyper parameters of my model following "Tutorial 8: Model Tuning" below:
https://github.com/flairNLP/flair/blob/master/resources/docs/TUTORIAL_8_MODEL_OPTIMIZATION.md

Although I got the "param_selection.txt" file in the result directory, I am not sure how to interpret the file, i.e. which parameter combination to use. At the bottom of the "param_selection.txt" file, I found "

Hi there,

I think there might be a mistake in the documentation. The Understanding Scaled F-Score section says

The F-Score of these two values is defined as:

$$ \mathcal{F}_\beta(\mbox{prec}, \mbox{freq}) = (1 + \beta^2) \frac{\mbox{prec} \cdot \mbox{freq}}{\beta^2 \cdot \mbox{prec} + \mbox{freq}}. $$

$\beta \in \mathcal{R}^+$ is a scaling factor where frequency is favored if $\beta

I would like to know what all the abbreviations mean? Some I can guess, like "PUNCT", but no idea what "X" might be. I want to retain contractions, but hard to choose options without documentation.

Thanks. Great performance code!

OS: MacOS 10.13.3
I installed the MeTA as instructed on setup guide. When I do the unit test

describe [ranker regression]
libc++abi.dylib: terminating with uncaught exception of type meta::corpus::corpus_exception: corpus configuration file (../data//cranfield/line.toml) not present

ERROR: tensorflow 1.15.0 has requirement numpy<2.0,>=1.16.0, but you'll have numpy 1.14.6 which is incompatible.
ERROR: spacy 2.1.8 has requirement numpy>=1.15.0, but you'll have numpy 1.14.6 which is incompatible.
ERROR: mxnet-cu92 1.5.1.post0 has requirement numpy<2.0.0,>1.16.0, but you'll have numpy 1.14.6 which is incompatible.
ERROR: imgaug 0.2.9 has requirement numpy>=1.15.0, but you'll h

fastText supervised model does not take into account of the document and words representation, it just learns bag of words and labels.
embeddings are computed only on the relation word->label. it would be interesting to learn jointly the semantic relation label<->document<->word<->context.
for now it is only possible to pre-train word embeddings and then use them as initial vectors for the clas