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

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 "

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

Hi! Great package!

Both NLTK and Spacy offer the option to install models from local files, as with:

pip install /Users/you/en_core_web_sm-2.2.0.tar.gz

Do you have any thoughts on adding this to stanza? This makes it easier to deploy in an environment where the resources for the cod

I have some values in slots that are surrounded by curly braces and are meant to be returned as is. Instead, the trailing brace is being stripped. "${website}" becomes "${website". I have training examples where the whole "${website}" is included. Is there a way to change this behavior?

近期在看模型的时候，因为README.md文件里涉及到了代码，但是markdown文件里代码的变量为Python的关键字str，如下所示，

import time
from bert_base.client import BertClient

with BertClient(show_server_config=False, check_version=False, check_length=False, mode='NER') as bc:
   start_t = time.perf_counter()
   str = '1月24日，新华社对外发布了中央对雄安新区的指导意见，洋洋洒洒1.2万多字，17次提到北京，4次提到天津，信息量很大，其实也回答了人们关心的很多问题。'
   rst = bc.encode([str, str])
   pri

I have prepared my test file containing this 3 lines :

Survey B-DOC
Report I-DOC
Geographic I-DOC

when I make run, I get this error :

default = tags[NONE]
KeyError: 'O'
makefile:7: recipe for target 'run' failed
make: *** [run] Error 1

Could you please help me.

Testing started at 上午 11:10 ...
F:\python\Anaconda3\envs\tensorflow\python.exe "F:\Program Files\PyCharm 2018.3.3\helpers\pycharm_jb_pytest_runner.py" --path F:/WorkSpace/python/Information-Extraction-Chinese-master/RE_BGRU_2ATT/test_GRU.py
Launching pytest with arguments F:/WorkSpace/python/Information-Extraction-Chinese-master/RE_BGRU_2ATT/test_GRU.py in F:\WorkSpace\python\Information-Extrac

Running python main.py with default settings raises the following warnings:

WARNING:tensorflow:From /usr/local/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py:417: calling reverse_sequence (from tensorflow.python.ops.array_ops) with seq_dim is deprecated and will be removed in a future version.
Instructions for updating:
seq_dim is deprecated, use seq_axis instead

WAR

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): 10.14.5
• TensorFlow/Keras version: 1.14.0
• Python version: 3.6.7

Describe the problem

While running the elmo_example.py file, In the 'Loading word embeddings' section of the code, the execution stops with the following error.

`
Traceback (most recent call last): File "elmo_example.py",

On home page of website: https://nlp.johnsnowlabs.com/ I read "Full Python, Scala, and Java support"

Unfortunately it's 3 days now I'm trying to use Spark NLP in Java without any success.

• I cannot find Java API (JavaDoc) of the framework.
• not event a single example in Java is available
• I do not know Scala, I do not know how to convert things like:
  val testData = spark.createDataFrame(

As Simple Transformers grows, the single page README documentation has gotten quite bloated and difficult to use. Because of this, I've decided that it's time (if not a little late already) to move the documentation to a more user-friendly Github Pages hosted website at the link below.

https://thilinarajapakse.github.io/simpletransformers/

As of now, only the text classification section is

flake8 testing of https://github.com/juand-r/entity-recognition-datasets on Python 3.7.0

$ flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics

./data/NIST_IEER/CONLL-format/utils/quick_comma_fix.py:41:37: E999 SyntaxError: invalid syntax
                    print annotations
                                    ^
./data/NIST_IEER/