Saravanan666/pypi-org huggingface dataset:
Package: bnemo
bnemoA Python package for translating Emoji and Emoticons into Bengali text.BackgroundNormally text corpus or text data contains different type of emoji, emoticons to express emotions and sentiment, specially text collected from social media. But this emojis have hardly any meaning in terms of text analysis if those are not converted into corresponding text. From that observation, we are trying to build a database of translation for common emoji, emoticons in Bengali so that Bengali NLP practitionars can use this database to translate emoji, emoticons into Bengali pharases for better understanding of text data. As the initial step, we have created this python package to easily use the emoji, emoticons to Bengali text database in NLP task.InstallationInstall using the following command -pipinstallbnemoUninstall using the following command -pipuninstallbnemoGetting StartedTo usebnemoinstall it usingpip. And make sure you have the required python version in your environment, i.e.python >= 3.5.Step#1:Import theTranslatorclass frombnemomodule.frombnemoimportTranslatorStep#2:Create an instance ofTranslatorclass.translator=Translator()Step#3:Use thetranslate()method ofTranslatorclass to translate any emoji, emoticons in your Bengali text data.input_text="তুমি বড় কষ্ট দিলে 😭"result=translator.translate(input_text)Step#4:It will return aResultobject, that has four attributes.AttributeData TypeExplanationtextStringThis is the translated text from bnemoemoListList of all the emoji, emoticons in the given input textposList of listThis is the list of start and ending index of each emoji in the input textmeaningList of StringsThis is the list of meanings for each emoji, emoticons in the input textStep#5:If you follow the above steps you might get the output as bellow -print(f"Translation:{result.text}\nEmoji:{result.emo}\nPositions:{result.pos}\nMeaning:{result.meaning}")Output:Translation:তুমিবড়কষ্টদিলেজোরেজোরেকান্নাকাটি
Emoji:['😭']Positions:[[18,19]]Meaning:['জোরে জোরে কান্নাকাটি']How to ContributeYou can contribute in the following ways -You can suggest better translation for any emoji, emoticons and send a pull request.You can create notebooks for showing use cases of this package or emoji database.Or may be in someother way not listed hereInspirationThe package was inspired by the awesome tool that you can use for translating emoji, emoticons into english phrases. Check out this awesome tool as well.https://github.com/NeelShah18/emotList of unicode emojishttps://unicode.org/emoji/charts/full-emoji-list.html
Package: bnet
Usage:>>>fromapi.connectionimportBattleNetConnection>>>connection=BattleNetConnection(apikey=<BATTLE_NET_APIKEY>)>>>client=conn.client()>>>client.get_auction_data(server)Documentation:https://dev.battle.net/io-docsTests:$exportBATTLE_NET_APIKEY=<BATTLE_NET_APIKEY>$maketest
Package: bnf
A BNF (Backus-Naur Form) parser and a LL input sequence scanner with backtrackingBNF syntax:non-terminals between<>rules end at newlinenassign with::=operators:alternative derivations separated by|group items between()optional items between{}or with postfix?operatorzero or more repetitions with postfix*operatorone or more repetitions with postfix+operatorset of terminal values between[]: in set[aeiou], not in set[^aeiou]or ranges[a-z]The BNF compiler uses a LL parser with backtracking:no left-recursion:<X> :== <X> …noa+ aalike sequenceslongest rule first: rule<X> ::= a | a bmust be replaced by<X> ::= a b | aspecial chars\<>(){}[]|+*?:=each must be quoted with\Grammar example for a python tuple of integer literals (tuple.bnf):`bnf <tuple> ::= \( <body> \) | \( \) <body>  ::= <elem> <num> | <elem> <elem>  ::= <num> , <elem> | <num> , <num>   ::= <dig> <num> | <dig> <dig>   ::=[0-9]`Test if an input sequence matches the above grammar with:` echo-n"(12,34,)"| python3-mbnf tuple.bnf `The printed result should beTrueorFalsewhether
the input sequence is accepted by the grammar, or not, respectively.Note: input sequence must not contain a newline (n) if grammar does not support it (useecho -n)Use the environmentDEBUG=1for a verbose output
(DEBUG=2for a more verbose output):` echo-n"(12,34,)"| DEBUG=1 python3-mbnf tuple.bnf `In interactive mode:` >>> from bnf import grammar, parse >>>grammar("<tuple>::= \( <body> \) | \(\)\n<body>::= <elem> <num> |<elem>\n<elem>::= <num> , <elem> | <num> ,\n<num>   ::= <dig> <num> |<dig>\n<dig>::=[0-9]\n")>>>parse("(12,34,)")`EBNF syntax:terminal symbols must be quoted between“”:“if”rules end with;not a newlineremaining rules are the same as for BNF syntax.The tuple example in eBNF becomes (tuple.ebnf):`bnf tuple ::= '(' body ')' | '(' ')' ; body  ::= elem num | elem ; elem  ::= num ',' elem | num ',' ; num   ::= dig num | dig ; dig   ::=[0-9]; `Test if an input sequence matches the above grammar with:` echo-n"(12,34,)"| python3-mebnf tuple.ebnf `In interactive mode:` >>> from ebnf import grammar, parse >>>grammar("tuple::= '(' body ')' | '(' ')' ;body  ::= elem num | elem ;elem  ::= num ',' elem | num ',' ;num   ::= dig num | dig ;dig   ::=[0-9];") >>>parse("(12,34,)")`The bnf/ package includes:bnf.py: BNF parser and input sequence scannerebnf.py: extended BNF parser and input sequence scannerDocumentation in the docs/ directory:tutorial.html: an introdution guideinternals.html: bnf/ebnf routine descriptionCode examples:exs/: some demonstration examplesprs, IST 2022
Package: bng-dis-probability
BnG Distributions PackageIncludes Binomial and Gaussian DistributionsSummary of the package.This package contains three files.Generaldistribution.pyGaussiandistribution.pyBinomialdistribution.pyFilesExplanation of the files.The Generaldistribution.py contains:Generic distribution class for calculating and
visualizing a probability distribution.And a Function to read in data from a txt file.The Gaussiandistribution.py contains:Gaussian distribution class for calculating and
visualizing a Gaussian distribution.Function to calculate the mean of the data set.Function to calculate the standard deviation of the data set.Function to output a histogram of the instance variable data using
matplotlib pyplot library.Probability density function calculator for the gaussian distribution.Function to plot the normalized histogram of the data and a plot of the
probability density function along the same range.Function to add together two Gaussian distributions.Function to output the characteristics of the Gaussian instance.The Binomialdistribution.py contains:Binomial distribution class for calculating and
visualizing a Binomial distribution.Function to calculate the mean from p and n.Function to calculate the standard deviation from p and n.Function to calculate p and n from the data set.Function to output a histogram of the instance variable data using
matplotlib pyplot library.Probability density function calculator for the binomial distribution.Function to plot the pdf of the binomial distribution.Function to add together two Binomial distributions with equal p.Function to output the characteristics of the Binomial instance.InstallationFor installation, write the following command in your terminal:pip install bng_distributions
Package: bng-latlon
🇬🇧BNG ⇄ LatLon🌐Converts british national grid eastings and northings (OSBG36) to
latitude and longitude (WGS84) and vice versa as used byhttps://blocl.ukOriginally authored byHannah Fry.NEW!importable, installable, PEP8 styled, pure python, doctested,
with optional numba compiler support for 10x speedDocumentationPackage includes:bng_to_latlon.pyoriginally published as "Converting British National Grid to
Latitude and Longitude
II"latlon_to_bng.pyoriginally "Converting Latitude and Longitude to British National
grid"The mathematical theory used here is set out in "A guide to coordinate
systems in Great
Britain"
by Ordnance Survey.InstallationGet the latest stable release from PyPi:pip install bng_latlonoptional but recommend is numba compilerpip install numbaUsage>>> from bng_latlon import OSGB36toWGS84
>>> OSGB36toWGS84(538890, 177320)
(51.477795, -0.001402)
...
>>> from bng_latlon import WGS84toOSGB36
>>> WGS84toOSGB36(51.4778, -0.0014)
(538890.1053, 177320.4965)MIT licensed. Designed in the UK, published from Slovakia.Blocl Schools.
Package: bnglonlat
British National Grid to Longitude/LattitudePure python implementation ofconvert_lonlatfromlonlat_bng.Note: This uses the version without OSTN15 corrections, which can be several meters off.LicenseThis code is available under an MIT license, Seelicense.txt.It is based onlonlat_bng, which is Copyright (c) 2015 Stephan Hügel, and also published with an MIT license.
Package: bnlm
Bengal Language ModelBengali language model is build with fastai'sULMFitand ready forpredictionandclassficationtask.NB:This tool mostly followedinltkWe separatedBengalipart with better evaluation resultsInstallationpip install bnlmEvaluation ResultLanguage ModelAccuracy 48.26% on validation datasetPerplexity: ~22.79TrainingTo train with your own corpus followthisrepositoryFeatures and APIDownload pretrained ModelTo start, first download pretrained Language Model and Sentencepiece modelfrombnlm.bnlmimportdownload_modelsdownload_models()Predict N Wordsfrombnlm.bnlmimportBengaliTokenizerfrombnlm.bnlmimportpredict_n_wordsmodel_path='model'input_sen="আমি বাজারে"output=predict_n_words(input_sen,3,model_path)print("Word Prediction: ",output)Get Sentence Encodingfrombnlm.bnlmimportBengaliTokenizerfrombnlm.bnlmimportget_sentence_encodingmodel_path='model'sp_model="model/bn_spm.model"input_sentence="আমি ভাত খাই।"encoding=get_sentence_encoding(input_sentence,model_path,sp_model)print("sentence encoding is: ",encoding)Get Embedding Vectorsfrombnlm.bnlmimportBengaliTokenizerfrombnlm.bnlmimportget_embedding_vectorsmodel_path='model'sp_model="model/bn_spm.model"input_sentence="আমি ভাত খাই।"embed=get_embedding_vectors(input_sentence,model_path,sp_model)print("sentence embedding is : ",embed)Sentence Similarityfrombnlm.bnlmimportBengaliTokenizerfrombnlm.bnlmimportget_sentence_encodingfrombnlm.bnlmimportget_similar_sentencesmodel_path='model'sp_model="model/bn_spm.model"sentence_1="আমি ভাত খাই।"sentence_2="আমি ভাত খাই।"sim=get_sentence_similarity(sentence_1,sentence_2,model_path,sp_model)print("similarity is: ",sim)Get Simillar Sentencesfrombnlm.bnlmimportBengaliTokenizerfrombnlm.bnlmimportget_embedding_vectorsfrombnlm.bnlmimportget_similar_sentencesmodel_path='model'sp_model="model/bn_spm.model"input_sentence="আমি ভাত খাই।"sen_pred=get_similar_sentences(input_sentence,3,model_path,sp_model)print(sen_pred)Classificationupcomming
Package: bnlp-toolkit
Bengali Natural Language Processing(BNLP)BNLP is a natural language processing toolkit for Bengali Language. This tool will help you totokenize Bengali text,Embedding Bengali words,Embedding Bengali Document,Bengali POS Tagging,Bengali Name Entity Recognition,Bangla Text Cleaningfor Bengali NLP purposes.FeaturesTokenizationBasic TokenizerNLTK TokenizerSentencepiece TokenizerEmbeddingsWord2vec embeddingFasttext embeddingGlove EmbeddingDoc2vec Document embeddingPart of speech taggingCRF-based POS taggingNamed Entity RecognitionCRF-based NERText CleaningCorpusLetters, vowels, punctuations, stopwordsInstallationPIP installerpip install bnlp_toolkitor Upgradepip install -U bnlp_toolkitPython: 3.6, 3.7, 3.8, 3.9, 3.10OS: Linux, Windows, MacBuild from sourcegit clone https://github.com/sagorbrur/bnlp.git
cd bnlp
python setup.py installSample UsagefrombnlpimportBasicTokenizertokenizer=BasicTokenizer()raw_text="আমি বাংলায় গান গাই।"tokens=tokenizer(raw_text)print(tokens)# output: ["আমি", "বাংলায়", "গান", "গাই", "।"]DocumentationFull documentation are availablehereIf you are using previous version ofbnlpcheck the documentationarchiveContributor GuideCheckCONTRIBUTING.mdpage for details.Thanks ToSemantics LabAll the developers who are contributing to enrich Bengali NLP.
Package: bnltk
BNLTKBNLTK(Bangla Natural Language Processing Toolkit) is open-source python package for Bengali Natural Language Processing. It includes modules for Tokenization, Stemming, Parts of speech tagging. I'm looking forward to helping form contributors to make this look far better than this.installationpip install bnltkUsageTokenizerfrom bnltk.tokenize import Tokenizers
t = Tokenizers()
print(t.bn_word_tokenizer(' আমার সোনার বাংলা । '))Stemmerfrom bnltk.stemmer import BanglaStemmer
bn_stemmer = BanglaStemmer()
print(bn_stemmer.stem('খেয়েছিলো'))Parts of TaggerFor using the Parts of Tagger you need to download some data files as follows:from bnltk.bnltk_downloads import DataFiles
DataFiles().download()After successfully downloading the files, then you can use this module.pos_tagger = PosTagger()    
pos_tagger.loader()
sentences = 'দুশ্চিন্তার কোন কারণই নাই'
print(pos_tagger.tagger(sentences))
Package: bnnsurv
Uncertainty Estimation in Deep Bayesian Survival Models*UPDATE 11/16/23: pip package now available. Use "pip install bnnsurv". Tested with TensorFlow 2.13 and TensorFlow Probability 0.21. Seetest filefor how to use.This repository is the official TensorFlow implementation ofUncertainty Estimation in Deep Bayesian Survival Models, BHI 2023.The proposed method is implemented based onTensorFlow Probability.Full paper is available on IEEE Xplore:https://ieeexplore.ieee.org/document/10313466In this work, we introduce the use of Bayesian inference techniques for survival analysis in neural networks that rely on the Coxâ€™s proportional hazard assumption, for which we discuss a new flexible and effective architecture. We implement three architectures: a fully-deterministic neural network that acts as a baseline, a Bayesian model using variational inference and one using Monte-Carlo Dropout.Experiments show that the Bayesian models improve predictive performance over SOTA neural networks in a test dataset with few samples (WHAS500, 500 samples) and provide comparable performance in two larger ones (SEER and SUPPORT, 4024 and 8873 samples, respectively)LicenseTo view the license for this work, visithttps://github.com/thecml/UE-BNNSurv/blob/main/LICENSERequirementsTo run the models, please refer toRequirements.txt.Install auton-survival manually from Git:pip install git+https://github.com/autonlab/auton-survival.gitCode was tested in virtual environment withPython 3.8,TensorFlow 2.11andTensorFlow Probability 0.19TrainingMake directoriesmkdir resultsandmkdir models.Please refer topaths.pyto set appropriate paths. By default, results are inresultsand models inmodelsNetwork configuration using best hyperparameters are found inconfigs/*Run the training code:# SOTA models
python train_sota_models.py

# BNN Models
python train_bnn_models.pyEvaluationAfter model training, view the results in theresultsfolder.VisualizationRun the visualization notebooks:jupyter notebook plot_survival_curves.ipynb
jupyter notebook plot_survival_time.ipynbCitation@inproceedings{lillelund_uncertainty_2023,
  author={Lillelund, Christian Marius and Magris, Martin and Pedersen, Christian Fischer},
  booktitle={2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)}, 
  title={Uncertainty Estimation in Deep Bayesian Survival Models}, 
  year={2023},
  pages={1-4},
  doi={10.1109/BHI58575.2023.10313466}
}
Package: bnnumerizer
bnNumerizerBangla number to Bangla string converter based on the workbanglakit/number-to-bengali-wordoriginal authors: aniruddha-adhikary,Mahir Labib Chowdhury
pypi: mnansaryinstallpipinstallbnnumerizeruseagefrombnnumerizerimportnumerizetext="বিল গেটসের ব্যাংক ব্যালেন্স ২২২১২৩৪৫৬.১২৩৪ টাকা। এর মধ্যে আমার অর্থের পরিমাণ ৩৪৫ টাকা মাত্র "numerize(text)'বিল গেটসের ব্যাংক ব্যালেন্স বাইশ কোটি একুশ লক্ষ তেইশ হাজার চার শত ছাপ্পান্ন দশমিক এক দুই তিন চার টাকা। এর মধ্যে আমার অর্থের পরিমাণ তিন শত পঁয়তাল্লিশ টাকা মাত্র 'Limitationscan not convert numbers > "৯৯৯৯৯৯৯৯৯"Citingif you find the work useful please star/ cite theOrininal Repository:https://github.com/banglakit/number-to-bengali-wordChange Log
Package: b-node-cdk-layer
B.NodeCdkLayerDescriptionThis is a simple Lambda layer AWS CDK resource that containsnodeandaws cdk cliwithin.
This package is useful for lambda functions that want to runaws cdkcommands within themselves.
Imagine a scenario where you runcdk deploy --allcommand not within you local pc or a build environment
but through a lambda function. This library would greatly help you.RemarksBiomapasaims to modernise life-science industry by sharing its IT knowledge with other companies and the community.
This is an open source library intended to be used by anyone.
Improvements and pull requests are welcome.Related technologyPython3DockerAWS CDKAssumptionsThis project assumes you have:Good knowledge in AWS.Good knowledge in AWS CDK.Good Python skills and basis of OOP.Useful sourcesRead more about how Docker is ued when building custom lambda layers:https://docs.aws.amazon.com/cdk/api/v1/python/aws_cdk.aws_lambda/Code.html#aws_cdk.aws_lambda.Code.from_docker_buildInstallBefore installing this library, ensure you have these tools setup:Python / PipTo install this project from source run:pip install .Or you can install it from a PyPi repository:pip install b-node-cdk-layerUsage & ExamplesSimply create a lambda function with this layer:fromaws_cdk.aws_lambdaimportFunctionfromb_node_cdk_layer.node_cdk_layerimportNodeCdkLayerFunction(scope=Stack(...),id='Function',...,layers=[NodeCdkLayer(Stack(...))],# Running node process requires more RAM and longer runtime.timeout=Duration.minutes(1),memory_size=512)To run some node or cdk command, follow this example:importsubprocess# This is the path to AWS CDK CLI. It comes from a NodeCDK layer, that is a custom-built layer using docker.CDK_CLI_PATH='/opt/bin/node_modules/aws-cdk/bin/cdk'defhandler(*args,**kwargs):output=subprocess.check_output([CDK_CLI_PATH,'--version',])print(output)TestingThis package has unit tests based onpytest.
To run tests simply run:pytest b_node_cdk_layer_test/integrationContributionFound a bug? Want to add or suggest a new feature?
Contributions of any kind are gladly welcome.
You may contact us directly, create a pull-request or an issue in github platform.
Lets modernize the world together.Release history0.1.0Release build. Package works as intended.0.0.1Initial build.
Package: bnpm
basic_neural_processing_modulesPersonal library of functions used in analyzing neural data.
If you find a bug or just want to reach out:RichHakim@gmail.comInstallationNormal installation ofbnpmdoes not install all possible dependencies; there are some specific functions that wrap libraries that may need to be installed separately on a case-by-case basis.Install stable version:pip install bnpm[core]If installing on a server or any computer without graphics/display, after installingbnpm, please uninstallopencv-contrib-pythonand installopencv-contrib-python-headlessinstead.pip uninstall opencv-contrib-python
pip install opencv-contrib-python-headlessInstall development version:pip install git+https://github.com/RichieHakim/basic_neural_processing_modules.gitimport with:import bnpmUsageMy favorites:automatic_regressionmoduleAllows for easy and fast hyperparameter optimization of regression modelsAny model with afitandpredictmethod can be used (e.g.sklearnand similar)Usesoptunafor hyperparameter optimizationOther useful functions:Signal Processing:timeSeries.rolling_percentile_rq_multicoreFast rolling percentile calculationtimeSeries.event_triggered_tracesFast creation of a matrix of aligned traces relative to specified event timesMachine Learning:neural_networksmoduleHas nice RNN regression and classification classesdecomposition.torch_PCAFast standard PCA using PyTorchsimilarity.orthogonalizeOrthogonalize a matrix relative to a set of vectors using OLS or Gram-Schmidt processMiscellaneouspath_helpers.find_pathsFind paths to files and/or folders in a directory. Searches recursively using regex.image_processing.play_video_cv2Plays and/or saves a 3D array as a video using OpenCVh5_handling.simple_saveandh5_handling.simple_loadSimple lazy loading and saving of dictionaries as nested h5 filesparallel_helpers.multiprocessing_pool_along_axisEasy parallelization of a function along an axisplotting_helpers.get_subplot_indicesReturns the subscript indices of the subplots in a figure
Package: bnpostagger
Bengali Pos TaggerInstallationpip install bnpostagger==0.0.1Quick StartAfter installation, using bengali postager should be fairly straight forward:Text Preprocessfrom bnpostagger import bn_postagger

text = "তবে ওষুধের নমুনা পরীক্ষা করা, নমুনা পরীক্ষায় তা উত্তীর্ণ হলে আমদানির জন্য ঋণপত্র (এলসি) খোলা, দেশে আনার পর আরেক দফা ওষুধ পরীক্ষার পর তা ছিটানোর উপযোগী করার মতো কাজগুলো সম্পন্ন করতে এ মাসের পুরোটা লেগে যেতে পারে।"
output =  bn_postagger(text)
print(output)
Package: bnpreprocessor
Bengali Text PreprocessorInstallationpip install bnpreprocessor==1.1.0Quick StartAfter installation, using bengali preprocessor should be fairly straight forward:Text Preprocessfrom bnpreprocessor import preprocessor

text = preprocessor("আগে যখন আমি ফুটবল বুঝতাম না ও দেখতাম না তখন ভাবতাম মেসিই সেরা। তারপর যখন আমি ফুটবল বোঝা ও দেখা শুরু করলাম তখন উপলব্ধি করলাম যে,আগে আমি ভুল ছিলাম না।")
print(text)
Package: bnptool
Simple Commandline interface for creating BNPs via BCMLSetupInstall a compatible version of python forBCMLthen runpip install bnptoolCreate a BNPbnptool create Path/To/ModFull Usage:bnptool create [-h] [--output OUTPUT] [--name NAME] [--version VERSION] [--description DESCRIPTION]
        [--image IMAGE] [--url URL] [--lowestpriority] [--disablepacks] [--disableaamp]
        [--disabledrops] [--disabletext] [--disableactorinfo] [--disableshrineent] [--disablemaps]
        [--disablegamedata] [--disablesavedata] [--disableeventinfo] [--disablestatuseff]
        [--disableresactors] [--disablequests] [--disablerstb] [--norstbest] [--mergetextalllang]
        Path/To/ModExample Usage:bnptool create --name "Linkle Mod - Dialogue Fix" -i "https://images.gamebanana.com/img/ss/mods/530-90_605f95e938639.jpg" -u "https://gamebanana.com/mods/33334" -d "Linkle Mod - Dialogue Fix git-28224c4b" --version "1.5.1" --mergetextalllang C:\Users\linktlh\Desktop\Ballad\wiiu\GPKnown ProblemsCurrently BNPTool does not support mod options, nor does it support requirements. This is being worked on, but is not complete.
Package: bnrml
Be normal! (It’s a string normalization framework.)Project Featuresbnrmla starterClickcommand-line
applicationautomated unit tests you can run withpytestaSphinxdocumentation
projectGetting StartedThe project’s documentation contains a section to help youget
startedas a developer or user of the library.Development PrerequisitesIf you’re going to be working in the code (rather than just using the
library), you’ll want a few utilities.GNU MakePandocResourcesBelow are some handy resource links.Project DocumentationClickis a Python package for
creating beautiful command line interfaces in a composable way with
as little code as necessary.Sphinxis a tool that
makes it easy to create intelligent and beautiful documentation,
written by Geog Brandl and licnsed under the BSD license.pytesthelps you write
better programs.[GNU Make] is a tool which controls the generation of executables and
other non-source files of a program from the program’s source files.AuthorsPat Daburu-Initial work-githubSee also the list ofcontributorswho
participated in this project.LicenseMIT LicenseCopyright (c) patdaburuPermission 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.