Merge pull request #1997 from flairNLP/GH-1983-0.7

GH-1983: Prepare 0.7 release

README.md

@@ -4,7 +4,6 @@
 [![GitHub Issues](https://img.shields.io/github/issues/flairNLP/flair.svg)](https://github.com/flairNLP/flair/issues)
 [![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CONTRIBUTING.md)
 [![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)
-[![Travis](https://img.shields.io/travis/flairNLP/flair.svg)](https://travis-ci.org/flairNLP/flair)
 
 A very simple framework for **state-of-the-art NLP**. Developed by [Humboldt University of Berlin](https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/) and friends.
 
@@ -27,7 +26,7 @@ document embeddings, including our proposed **[Flair embeddings](https://www.acl
 * **A PyTorch NLP framework.** Our framework builds directly on [PyTorch](https://pytorch.org/), making it easy to
 train your own models and experiment with new approaches using Flair embeddings and classes.
 
-Now at [version 0.6.1](https://github.com/flairNLP/flair/releases)!
+Now at [version 0.7](https://github.com/flairNLP/flair/releases)!
 
 ## Comparison with State-of-the-Art
 

flair/__init__.py

@@ -25,7 +25,7 @@
 
 import logging.config
 
-__version__ = "0.6.1.post1"
+__version__ = "0.7"
 
 logging.config.dictConfig(
     {

flair/datasets/__init__.py

@@ -7,26 +7,39 @@
 # Expose all sequence labeling datasets
 from .sequence_labeling import ColumnCorpus
 from .sequence_labeling import ColumnDataset
+from .sequence_labeling import ANER_CORP
 from .sequence_labeling import BIOFID
 from .sequence_labeling import BIOSCOPE
 from .sequence_labeling import CONLL_03
 from .sequence_labeling import CONLL_03_GERMAN
 from .sequence_labeling import CONLL_03_DUTCH
 from .sequence_labeling import CONLL_03_SPANISH
 from .sequence_labeling import CONLL_2000
-from .sequence_labeling import TWITTER_NER
 from .sequence_labeling import DANE
 from .sequence_labeling import EUROPARL_NER_GERMAN
 from .sequence_labeling import GERMEVAL_14
 from .sequence_labeling import INSPEC
 from .sequence_labeling import LER_GERMAN
+from .sequence_labeling import MIT_MOVIE_NER_SIMPLE
+from .sequence_labeling import MIT_MOVIE_NER_COMPLEX
+from .sequence_labeling import MIT_RESTAURANT_NER
 from .sequence_labeling import NER_BASQUE
 from .sequence_labeling import NER_FINNISH
 from .sequence_labeling import NER_SWEDISH
 from .sequence_labeling import SEMEVAL2010
 from .sequence_labeling import SEMEVAL2017
+from .sequence_labeling import TURKU_NER
+from .sequence_labeling import TWITTER_NER
+from .sequence_labeling import UP_CHINESE
+from .sequence_labeling import UP_ENGLISH
+from .sequence_labeling import UP_FINNISH
+from .sequence_labeling import UP_FRENCH
+from .sequence_labeling import UP_GERMAN
+from .sequence_labeling import UP_ITALIAN
+from .sequence_labeling import UP_SPANISH
+from .sequence_labeling import UP_SPANISH_ANCORA
+from .sequence_labeling import WEIBO_NER
 from .sequence_labeling import WIKIANN
-from .sequence_labeling import XTREME
 from .sequence_labeling import WIKIGOLD_NER
 from .sequence_labeling import WIKINER_ENGLISH
 from .sequence_labeling import WIKINER_GERMAN
@@ -39,20 +52,7 @@
 from .sequence_labeling import WIKINER_RUSSIAN
 from .sequence_labeling import WNUT_17
 from .sequence_labeling import WNUT_2020_NER
-from .sequence_labeling import WEIBO_NER
-from .sequence_labeling import MIT_RESTAURANTS
-from .sequence_labeling import UP_CHINESE
-from .sequence_labeling import UP_ENGLISH
-from .sequence_labeling import UP_FINNISH
-from .sequence_labeling import UP_FRENCH
-from .sequence_labeling import UP_GERMAN
-from .sequence_labeling import UP_ITALIAN
-from .sequence_labeling import UP_SPANISH
-from .sequence_labeling import UP_SPANISH_ANCORA
-from .sequence_labeling import ANER_CORP
-from .sequence_labeling import MITMovieNERSimple
-from .sequence_labeling import MITMovieNERComplex
-from .sequence_labeling import TURKU_NER
+from .sequence_labeling import XTREME
 
 # Expose all document classification datasets
 from .document_classification import ClassificationCorpus