Package: lineflow
LineFlow: Framework-Agnostic NLP Data Loader in PythonLineFlow is a simple text dataset loader for NLP deep learning tasks.LineFlow was designed to use in all deep learning frameworks.LineFlow enables you to build pipelines via functional APIs (.map,.filter,.flat_map).LineFlow provides common NLP datasets.LineFlow is heavily inspired bytensorflow.data.Datasetandchainer.dataset.Basic Usagelineflow.TextDataset expects line-oriented text files:importlineflowaslf'''/path/to/text will be expected as follows:i 'm a line 1 .i 'm a line 2 .i 'm a line 3 .'''ds=lf.TextDataset('/path/to/text')ds.first()# "i 'm a line 1 ."ds.all()# ["i 'm a line 1 .", "i 'm a line 2 .", "i 'm a line 3 ."]len(ds)# 3ds.map(lambdax:x.split()).first()# ["i", "'m", "a", "line", "1", "."]ExamplePlease check out theexamplesto see how to use LineFlow, especially for tokenization, building vocabulary, and indexing.Loads Penn Treebank:>>>importlineflow.datasetsaslfds>>>train=lfds.PennTreebank('train')>>>train.first()' aer banknote berlitz calloway centrust cluett fromstein gitano guterman hydro-quebec ipo kia memotec mlx nahb punts rake regatta rubens sim snack-food ssangyong swapo wachter 'Splits the sentence to the words:>>># continuing from above>>>train=train.map(str.split)>>>train.first()['aer','banknote','berlitz','calloway','centrust','cluett','fromstein','gitano','guterman','hydro-quebec','ipo','kia','memotec','mlx','nahb','punts','rake','regatta','rubens','sim','snack-food','ssangyong','swapo','wachter']Obtains words in dataset:>>># continuing from above>>>words=train.flat_map(lambdax:x)>>>words.take(5)# This is useful to build vocabulary.['aer','banknote','berlitz','calloway','centrust']Further more:How to fine-tune BERT with pytorch-lightningby@sobamchanRequirementsPython3.6+InstallationTo install LineFlow:pipinstalllineflowDatasetsIs the dataset you want to use not supported?Suggest a new dataset:tada:Commonsense ReasoningLanguage ModelingMachine TranslationParaphraseQuestion AnsweringSentiment AnalysisSequence TaggingText SummarizationCommonsense ReasoningCommonsenseQALoads the CommonsenseQA dataset:>>>importlineflow.datasetsaslfds>>>train=lfds.CommonsenseQA("train")>>>dev=lfds.CommonsenseQA("dev")>>>test=lfds.CommonsenseQA("test")The items in this datset as follows:>>>importlineflow.datasetsaslfds>>>train=lfds.CommonsenseQA("train")>>>train.first(){"id":"075e483d21c29a511267ef62bedc0461","answer_key":"A","options":{"A":"ignore","B":"enforce","C":"authoritarian","D":"yell at","E":"avoid"},"stem":"The sanctions against the school were a punishing blow, and they seemed to what the efforts the school had made to change?"}}Language ModelingPenn TreebankLoads the Penn Treebank dataset:importlineflow.datasetsaslfdstrain=lfds.PennTreebank('train')dev=lfds.PennTreebank('dev')test=lfds.PennTreebank('test')WikiText-103Loads the WikiText-103 dataset:importlineflow.datasetsaslfdstrain=lfds.WikiText103('train')dev=lfds.WikiText103('dev')test=lfds.WikiText103('test')This dataset is preprossed, so you can tokenize each line withstr.split:>>>importlineflow.datasetsaslfds>>>train=lfds.WikiText103('train').flat_map(lambdax:x.split()+['<eos>'])>>>train.take(5)['<eos>','=','Valkyria','Chronicles','III']WikiText-2(Added by@sobamchan, thanks.)Loads the WikiText-2 dataset:importlineflow.datasetsaslfdstrain=lfds.WikiText2('train')dev=lfds.WikiText2('dev')test=lfds.WikiText2('test')This dataset is preprossed, so you can tokenize each line withstr.split:>>>importlineflow.datasetsaslfds>>>train=lfds.WikiText2('train').flat_map(lambdax:x.split()+['<eos>'])>>>train.take(5)['<eos>','=','Valkyria','Chronicles','III']Machine Translationsmall_parallel_enja:Loads the small_parallel_enja dataset which is small English-Japanese parallel corpus:importlineflow.datasetsaslfdstrain=lfds.SmallParallelEnJa('train')dev=lfds.SmallParallelEnJa('dev')test=lfd.SmallParallelEnJa('test')This dataset is preprossed, so you can tokenize each line withstr.split:>>>importlineflow.datasetsaslfds>>>train=lfds.SmallParallelEnJa('train').map(lambdax:(x[0].split(),x[1].split()))>>>train.first()(['i','can',"'t",'tell','who','will','arrive','first','.'],['誰','が','一番','に','着','く','か','私','に','は','分か','り','ま','せ','ん','。']ParaphraseMicrosoft Research Paraphrase Corpus:Loads the Miscrosoft Research Paraphrase Corpus:importlineflow.datasetsaslfdstrain=lfds.MsrParaphrase('train')test=lfds.MsrParaphrase('test')The item in this dataset as follows:>>>importlineflow.datasetsaslfds>>>train=lfds.MsrParaphrase('train')>>>train.first(){'quality':'1','id1':'702876','id2':'702977','string1':'Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.','string2':'Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.'}Question AnsweringSQuAD:Loads the SQuAD dataset:importlineflow.datasetsaslfdstrain=lfds.Squad('train')dev=lfds.Squad('dev')The item in this dataset as follows:>>>importlineflow.datasetsaslfds>>>train=lfds.Squad('train')>>>train.first(){'answers':[{'answer_start':515,'text':'Saint Bernadette Soubirous'}],'question':'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?','id':'5733be284776f41900661182','title':'University_of_Notre_Dame','context':'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'}Sentiment AnalysisIMDB:Loads the IMDB dataset:importlineflow.datasetsaslfdstrain=lfds.Imdb('train')test=lfds.Imdb('test')The item in this dataset as follows:>>>importlineflow.datasetsaslfds>>>train=lfds.Imdb('train')>>>train.first()('For a movie that gets no respect there sure are a lot of memorable quotes listed for this gem. Imagine a movie where Joe Piscopo is actually funny! Maureen Stapleton is a scene stealer. The Moroni character is an absolute scream. Watch for Alan "The Skipper" Hale jr. as a police Sgt.',0)Sequence TaggingCoNLL2000Loads the CoNLL2000 dataset:importlineflow.datasetsaslfdstrain=lfds.Conll2000('train')test=lfds.Conll2000('test')Text SummarizationCNN / Daily Mail:Loads the CNN / Daily Mail dataset:importlineflow.datasetsaslfdstrain=lfds.CnnDailymail('train')dev=lfds.CnnDailymail('dev')test=lfds.CnnDailymail('test')This dataset is preprossed, so you can tokenize each line withstr.split:>>>importlineflow.datasetsaslfds>>>train=lfds.CnnDailymail('train').map(lambdax:(x[0].split(),x[1].split()))>>>train.first()...# the output is omitted because it's too long to display here.SciTLDRLoads the TLDR dataset:importlineflow.datasetsaslfdstrain=lfds.SciTLDR('train')dev=lfds.SciTLDR('dev')test=lfds.SciTLDR('test')