Michio JP

Michio JP


Flair | Modern Natural Language Processing (NLP) Model

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends.

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), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.

A text embedding library. Flair has simple interfaces that allow you to use and combine different word and document embeddings, including our proposed Flair embeddings, BERT embeddings and ELMo embeddings.

A PyTorch NLP framework. Our framework builds directly on PyTorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.

Now at version 0.9!

Join Us: Open Positions at HU-Berlin!

If you're interested in doing NLP/ML research to pursue a PhD and love open source, consider applying to open positions for research associates and PhD candidates at Humboldt University Berlin!

State-of-the-Art Models

Flair ships with state-of-the-art models for a range of NLP tasks. For instance, check out our latest NER models:

LanguageDatasetFlairBest publishedModel card & demo
EnglishConll-03 (4-class)94.0994.3 (Yamada et al., 2018)Flair English 4-class NER demo
EnglishOntonotes (18-class)90.9391.3 (Yu et al., 2016)Flair English 18-class NER demo
GermanConll-03 (4-class)92.3190.3 (Yu et al., 2016)Flair German 4-class NER demo
DutchConll-03 (4-class)95.2593.7 (Yu et al., 2016)Flair Dutch 4-class NER demo
SpanishConll-03 (4-class)90.5490.3 (Yu et al., 2016)Flair Spanish 18-class NER demo

New: Most Flair sequence tagging models (named entity recognition, part-of-speech tagging etc.) are now hosted on the 🤗 HuggingFace model hub! You can browse models, check detailed information on how they were trained, and even try each model out online!

Quick Start

Requirements and Installation

The project is based on PyTorch 1.5+ 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 flair

Example Usage

Let's run named entity recognition (NER) over an example sentence. All you need to do is make a Sentence, load a pre-trained model and use it to predict tags for the sentence:

from flair.data import Sentence
from flair.models import SequenceTagger

# make a sentence
sentence = Sentence('I love Berlin .')

# load the NER tagger
tagger = SequenceTagger.load('ner')

# run NER over sentence

Done! The Sentence now has entity annotations. Print the sentence to see what the tagger found.

print('The following NER tags are found:')

# iterate over entities and print
for entity in sentence.get_spans('ner'):

This should print:

Sentence: "I love Berlin ." - 4 Tokens

The following NER tags are found:

Span [3]: "Berlin"   [− Labels: LOC (0.9992)]


We provide a set of quick tutorials to get you started with the library:

The 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 our biomedical NER and datasets with installation instructions and tutorials.

There are also good third-party articles and posts that illustrate how to use Flair:

Citing Flair

Please cite the following paper when using Flair embeddings:

  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 Flair framework for your experiments, please cite this paper:

  title={FLAIR: An easy-to-use framework for state-of-the-art NLP},
  author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland},
  booktitle={{NAACL} 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)},

If you use the pooled version of the Flair embeddings (PooledFlairEmbeddings), please cite this paper:

  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}

If you use our new "FLERT" models or approach, please cite this paper:

    title={FLERT: Document-Level Features for Named Entity Recognition},
    author={Stefan Schweter and Alan Akbik},


Please email your questions or comments to Alan Akbik.


Thanks for your interest in contributing! There are many ways to get involved; start with our contributor guidelines and then check these open issues for 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 locally

You need Pipenv for this:

pipenv install --dev && pipenv shell
pytest tests/

To run integration tests execute:

pytest --runintegration tests/

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 --runslow tests/


The MIT License (MIT)

Flair is licensed under the following MIT license: The MIT License (MIT) Copyright © 2018 Zalando SE, https://tech.zalando.com

Permission 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.


Download Details:

Author: flairNLP
Download Link: Download The Source Code
Official Website: https://github.com/flairNLP/flair 
License:  View license



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Flair | Modern Natural Language Processing (NLP) Model

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.

Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.

#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

Sival Alethea

Sival Alethea


Natural Language Processing (NLP) Tutorial with Python & NLTK

This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=X2vAabgKiuM&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=16
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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#natural language processing #nlp #python #python & nltk #nltk #natural language processing (nlp) tutorial with python & nltk

Paula  Hall

Paula Hall


Structured natural language processing with Pandas and spaCy

Accelerate analysis by bringing structure to unstructured data

Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.

Introduction to Spacy

spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.

After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.

import spacy
nlp = spacy.load("en_core_web_sm")

We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.

#analytics #nlp #machine-learning #data-science #structured natural language processing with pandas and spacy #natural language processing

Kolby  Wyman

Kolby Wyman


Why NLP Suffers From The Issue Of Underrepresented Languages

Natural language processing (NLP) has made several remarkable breakthroughs in recent years by providing implementations for a range of applications including optical character recognition, speech recognition, text simplification, question-answering, machine translation, dialogue systems and much more.

With the help of NLP, systems learn to identify spam emails, suggest medical articles or diagnosis related to a patient’s symptoms, etc. NLP has also been utilised as a critical ingredient in case of crucial decision-making systems such as criminal justice, credit, allocation of public resources, sorting a list of job candidates, to name a few.

However, despite all these critical use cases, NLP is still lagging and faces the problem of underrepresentation. For instance, one of the significant limitations of NLP is the ambiguity of words in languages. The ambiguity and imprecise characteristics of the natural languages make NLP difficult for machines to implement.

#developers corner #issues in nlp #natural language processing #nlp ai #nlp papers #nlp research

Natural Language Processing (NLP) based Chatbots

Natural Language Processing (NLP)

Natural Language Processing, also known as NLP, is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large amounts of natural language data.

NLP enables the computer to acquire meaning from inputs given by users. It is a branch of informatics, mathematical linguistics, machine learning, and artificial intelligence.

An NLP based chatbot is a computer program or artificial intelligence that communicates with a customer via textual or sound methods.

The relation between Linguistics, Artificial Intelligence, Machine Learning, Deep Learning and NLP.

Various NLP engines available in the market are Google’s DialogflowWit.ai (Facebook), Watson Conversation Service (IBM), Lex (Amazon), and more.

#nlp #chatbots #nlg #nlu #natural language processing (nlp) based chatbots