Some typical variations of email addresses are not detected:

works: nlp("send a message to bob@host.com today").emails()
empty: nlp("send a message to mr.bob@host.com today").emails()
empty: nlp("send a message to mailto:bob@host.com today").emails()

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

We need to create a separate dependencies list for react usage. Many react users won't need youtube-dl or ffmpeg libaries and we don't want things to be super bloated if they're using it as an npm module.

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/