Package: pd3f-flair
NB: This is a ForkFlair's Language Models without unnecessary dependencies.This package removed all the unnecessary dependencies if you only want to use Flair's character-based language models.
Flair listed all kind of optional requirements as hard requirments.
This made the installation tedious and bloated.pipinstallpd3f-flairA very simple framework forstate-of-the-art NLP. Developed byHumboldt University of Berlinand friends.IMPORTANT: (30.08.2020)We moved our models to a new server. Please update your Flair to the newest version!Flair is:A powerful NLP library.Flair allows you to apply our state-of-the-art natural language processing (NLP)
models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS),
sense disambiguation and classification, with support for a rapidly growing number of languages.A biomedical NER library.Flair has special support forbiomedical datawith
state-of-the-art models for biomedical NER and support for over 32 biomedical datasets.A text embedding library.Flair has simple interfaces that allow you to use and combine different word and
document embeddings, including our proposedFlair embeddings, BERT embeddings and ELMo embeddings.A PyTorch NLP framework.Our framework builds directly onPyTorch, making it easy to
train your own models and experiment with new approaches using Flair embeddings and classes.Now atversion 0.6!Comparison with State-of-the-ArtFlair outperforms the previous best methods on a range of NLP tasks:TaskLanguageDatasetFlairPrevious bestNamed Entity RecognitionEnglishConll-0393.18(F1)92.22(Peters et al., 2018)Named Entity RecognitionEnglishOntonotes89.3(F1)86.28(Chiu et al., 2016)Emerging Entity DetectionEnglishWNUT-1749.49(F1)45.55(Aguilar et al., 2018)Part-of-Speech taggingEnglishWSJ97.8597.64(Choi, 2016)ChunkingEnglishConll-200096.72(F1)96.36(Peters et al., 2017)Named Entity RecognitionGermanConll-0388.27(F1)78.76(Lample et al., 2016)Named Entity RecognitionGermanGermeval84.65(F1)79.08(Hänig et al, 2014)Named Entity RecognitionDutchConll-0292.38(F1)81.74(Lample et al., 2016)Named Entity RecognitionPolishPolEval-201886.6(F1)(Borchmann et al., 2018)85.1(PolDeepNer)Here's how toreproduce these numbersusing Flair. You can also find detailed evaluations and discussions in our papers:Contextual String Embeddings for Sequence Labeling.
Alan Akbik, Duncan Blythe and Roland Vollgraf.
27th International Conference on Computational Linguistics,COLING 2018.Pooled Contextualized Embeddings for Named Entity Recognition.
Alan Akbik, Tanja Bergmann and Roland Vollgraf.
2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics,NAACL 2019.FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP.
Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter and Roland Vollgraf.
2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations),NAACL 2019.Quick StartRequirements and InstallationThe project is based on PyTorch 1.1+ and Python 3.6+, because method signatures and type hints are beautiful.
If you do not have Python 3.6, install it first.Here is how for Ubuntu 16.04.
Then, in your favorite virtual environment, simply do:pip install flairExample UsageLet's run named entity recognition (NER) over an example sentence. All you need to do is make aSentence, load
a pre-trained model and use it to predict tags for the sentence:fromflair.dataimportSentencefromflair.modelsimportSequenceTagger# make a sentencesentence=Sentence('I love Berlin .')# load the NER taggertagger=SequenceTagger.load('ner')# run NER over sentencetagger.predict(sentence)Done! TheSentencenow has entity annotations. Print the sentence to see what the tagger found.print(sentence)print('The following NER tags are found:')# iterate over entities and printforentityinsentence.get_spans('ner'):print(entity)This should print:Sentence: "I love Berlin ." - 4 TokensThe following NER tags are found:Span [3]: "Berlin"   [− Labels: LOC (0.9992)]TutorialsWe provide a set of quick tutorials to get you started with the library:Tutorial 1: BasicsTutorial 2: Tagging your TextTutorial 3: Embedding WordsTutorial 4: List of All Word EmbeddingsTutorial 5: Embedding DocumentsTutorial 6: Loading a DatasetTutorial 7: Training a ModelTutorial 8: Training your own Flair EmbeddingsThe tutorials explain how the base NLP classes work, how you can load pre-trained models to tag your
text, how you can embed your text with different word or document embeddings, and how you can train your own
language models, sequence labeling models, and text classification models. Let us know if anything is unclear.There is also a dedicated landing page for ourbiomedical NER and datasetswith
installation instructions and tutorials.There are also good third-party articles and posts that illustrate how to use Flair:How to build a text classifier with FlairHow to build a microservice with Flair and FlaskA docker image for FlairGreat overview of Flair functionality and how to use in ColabVisualisation tool for highlighting the extracted entitiesPractical approach of State-of-the-Art Flair in Named Entity RecognitionBenchmarking NER algorithmsTraining a Flair text classifier on Google Cloud Platform (GCP) and serving predictions on GCPCiting FlairPlease cite the following paper when using Flair:@inproceedings{akbik2018coling,
  title={Contextual String Embeddings for Sequence Labeling},
  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
  pages     = {1638--1649},
  year      = {2018}
}If you use the pooled version of the Flair embeddings (PooledFlairEmbeddings), please cite:@inproceedings{akbik2019naacl,
  title={Pooled Contextualized Embeddings for Named Entity Recognition},
  author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland},
  booktitle = {{NAACL} 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
  pages     = {724–728},
  year      = {2019}
}ContactPlease email your questions or comments toAlan Akbik.ContributingThanks for your interest in contributing! There are many ways to get involved;
start with ourcontributor guidelinesand then
check theseopen issuesfor specific tasks.For contributors looking to get deeper into the API we suggest cloning the repository and checking out the unit
tests for examples of how to call methods. Nearly all classes and methods are documented, so finding your way around
the code should hopefully be easy.Running unit tests locallyYou needPipenvfor this:pipenvinstall--dev&&pipenvshell
pytesttests/To run integration tests execute:pytest--runintegrationtests/The integration tests will train small models.
Afterwards, the trained model will be loaded for prediction.To also run slow tests, such as loading and using the embeddings provided by flair, you should execute:pytest--runslowtests/LicenseThe MIT License (MIT)Flair is licensed under the following MIT license: The MIT License (MIT) Copyright © 2018 Zalando SE,https://tech.zalando.comPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.