Many models have identical implementations of prune_heads it would be nice to store that implementation as a method on PretrainedModel and reduce the redundancy.

When you look at the variables in the pretrained base uncased BERT the varibles look like list 1. When you do the training from scratch, 2 additional variables per layer are introduced, with suffixes adam_m and adam_v. It would be nice for someone to explain what these variables are? and what is their significance to the process of training?
If one were to manually initialize variables from a pri

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

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

Looping the process of writing images into the .tfrecords-file works fine, but how do I read multiple images from a .tfrecords-file?

Is there any simple solution? would be great if added to the code.

Hello and thanks for the great library! 👋

I've been having some difficulty getting the doc.lists() method to work, it seems to be very sensitive to the way lists are structured and the items in the list. The example material at https://observablehq.com/@spencermountain/compromise-lists contains an example of this sensitivity, where the code sample for `nlp('he eats, shoots, and leaves.').list

This output is unexpected. The In returns the capitalize In from PorterStemmer's output.

>>> from nltk.stem import PorterStemmer
>>> porter = PorterStemmer()
>>> porter.stem('In')
'In'

More details on https://stackoverflow.com/q/60387288/610569

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 "

My feature request is to include an option on a button made from choice skill, to redirect a link to an external url...

Here's a detailed explanation including screenshots

https://help.botpress.io/t/how-to-redirect-to-an-external-url-while-using-a-button-made-in-choice-skill/1791

This option will be really beneficial using choice skill buttons since at the moment, you can only add an ext

It would be nice to separately log model computed loss from regularization loss in tensorboard. Involves minor changes to the Trainer.

I propose this topic as feature request, but it's also a documentation issue, as lack of details in user guide paragraph: https://rasa.com/docs/rasa/core/actions/#custom-actions.

What specified in paragraph Execute Actions in Other Code is obscure to me, and details at the API documentation link [Action Server](]https://rasa.com/docs/rasa/api/acti

Prerequisites

Please fill in by replacing [ ] with [x].

• Are you running the latest bert-as-service?
• Did you follow the installation and the usage instructions in README.md?
• Did you check the [FAQ list in README.md](https://github.com/hanxiao/bert-as-se

As per the StanfordCoreNLP documentation for CoreLabel, The functions after() and before() should return white space strings between the token and the next/previous tokens respectively.
However, they return an empty string always even if there are some white spaces when the tokenizer option **normalizeOth

The documentation specifies:

The words and sentences properties are helpers that use the textblob.tokenizers.WordTokenizer and textblob.tokenizers.SentenceTokenizer classes, respectively.

You can use other tokenizers, such as those provided by NLTK, by passing them into the TextBlob constructor then accessing the t

Excuse me, https://github.com/graykode/nlp-tutorial/blob/master/1-1.NNLM/NNLM-Torch.py#L50 The comment here may be wrong. It should be X = X.view(-1, n_step * m) # [batch_size, n_step * m]

Sorry for disturbing you.

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

Hi, can batchify method only batch a doc in a file, not two docs in the same file? Why the EOD flag not use to distinguish different docs in data_utils.py ?

Description

Add a ReadMe file in the GitHub folder.
Explain usage of the Templates

Other Comments

Principles of NLP Documentation
Each landing page at the folder level should have a ReadMe which explains -
○ Summary of what this folder offers.
○ Why and how it benefits users
○ As applicable - Documentation of using it, brief description etc
Scenarios folder:
○