Saravanan666/pypi-org huggingface dataset:
Package: text2hologram
text2hologramtext2hologramis a Python package designed to generate holograms from text. It's built on top of the odak, timm, transformers, diffusers, accelerate, and torch libraries.text2hologram supports Python 3.9 and above.The project's source code is availablehere.InstallationTo install the package, use the following pip command:pipinstalltext2hologramUsageYou can use the package from the command line with thetext2hologramcommand.Here is a simple usage example:text2hologram--device'cuda'--outputdir'./output'The command above checks the device first,and then ask for a text input. generates a hologram from the input text using GPU and saves the output in the './output' directory.Here's a brief description of each option:--device: Computation device to use. Can be 'cpu' or 'cuda'.--outputdir: Directory to save output files.--inference_steps: Inference steps to generate an image. A higher number of inference steps will result in a more accurate and detailed image, but increase the computational cost and time required.See more settings inconfig.json. All settings are named in a self-explanatory manner.Licensetext2hologram is licensed under the MIT license. The terms of the license can be found in the LICENSE file.ContactFor any questions or feedback, you are welcome to reach out to Pengze Li atlinsonng@163.com.
Package: text2id
Text converter for text to speech tasks.This script is designed to convert text representation to ordinal numbers in
the sequence of symbols:'_', '-', '!', "'", '(', ')', ',', '.', ':', ';', '?', ' ', 'A', 'B', 'C', 'D', 'E',
'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
'W', 'X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm',
'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '@AA', '@AA0', '@AA1',
'@AA2', '@AE', '@AE0', '@AE1', '@AE2', '@AH', '@AH0', '@AH1', '@AH2', '@AO', '@AO0',
'@AO1', '@AO2', '@AW', '@AW0', '@AW1', '@AW2', '@AY', '@AY0', '@AY1', '@AY2', '@B',
'@CH', '@D', '@DH', '@EH', '@EH0', '@EH1', '@EH2', '@ER', '@ER0', '@ER1', '@ER2',
'@EY', '@EY0', '@EY1', '@EY2', '@F', '@G', '@HH', '@IH', '@IH0', '@IH1', '@IH2',
'@IY', '@IY0', '@IY1', '@IY2', '@JH', '@K', '@L', '@M', '@N', '@NG', '@OW', '@OW0',
'@OW1', '@OW2', '@OY', '@OY0', '@OY1', '@OY2', '@P', '@R', '@S', '@SH', '@T', '@TH',
'@UH', '@UH0', '@UH1', '@UH2', '@UW', '@UW0', '@UW1', '@UW2', '@V', '@W', '@Y', '@Z',
'@ZH'The sequence of symbols is union of English alphabet, arphabet and punctuation symbols.
This script based onhttps://github.com/keithito/tacotronand supports following
preprocessing steps:Convert input text to ascii symbols. We use internal converter which support
restricted set of symbols from German, French, Spanish and Russian alphabets.MÃ¼ller -> MuellerLower case conversion.Mueller -> muellerExpand numbers.1234 -> twelve thirty-fourExpand restricted set of abbreviations.ltd -> limitedCollate whitespaces.My   name -> My nameExamplefromtext2idimportText2Iddefrun_sample(t2i,text):print(t2i.text_to_sequence(text))print(t2i.sequence_to_text(t2i.text_to_sequence(text)))print(t2i.text_to_sequence(text,use_dict=False))print(t2i.sequence_to_text(t2i.text_to_sequence(text,use_dict=False)))if__name__=="__main__":t2i=Text2Id()print("Supported symbols: ",t2i.get_symbols())text="MÃ¼ller said 11 12 13"run_sample(t2i,text)text="Hello 1234."run_sample(t2i,text)
Package: text2integer
The package is used to convert texts to integers. You can use the below examples to get startedpip install text2integerfrom text_2_integer.t2i import text_2_integerti = text_2_integer.text_2_int()ti.text2int("five lakh",scales_use='hi')
#500000
ti.text2int("paanch lakh",scales_use='hi')
#500000ti.text2int("nine point five lakh",scales_use='hi')
#950000ti.text2int("paanch hazaar",scales_use='hi')
#5000We support hindi/english languages and the work is in developmental phaseLicenseMIT License
License: MIT + file text_2_integer.py
Package: text2ipa
Installation:Windowspython -m pip install text2ipamacOSsudo pip3 install text2ipaLinuxpip install text2ipaFeaturesTwo options Language English and FrenchExamplesExample 1: Convert a textFunction:get_IPA(): Converting a text to IPA with the following parametersParameters:text: The text you want to convert to IPAlanguage: Choose between English US, English UK and French ('en' or 'fr')For instance:fromtext2ipaimportget_IPA# Convert 'hello world' to English US International Alphabettext='hello world'language='en'ipa=get_IPA(text,language)# Convert 'je parle un peu français' to IPAtext='je parle un peu français'language='fr'fr_ipa=get_IPA(text,language)print(ipa)print(fr_ipa)>> hɛˈloʊ wərld
>> ʒə paʀlɛ yn pəy fʀɑ̃sɛExample 2: Convert a bulkFunction:get_IPAs(): Convert the list of texts to IPA return the list of IPAsParameters:bulk: The list of text want to convert to IPAlanguage: Choose between English US and English UK ('en' or 'fr')For instance:fromtext2ipaimportget_IPAsbulk=['how are you?','how it\'s going?','that\'s good']language='br'# Convert a list of text to English UK IPAIPAs=get_IPAs(bulk,language)foripainIPAs:print(ipa)>> haʊ ɑː juː?
>> haʊ ɪts ˈgəʊɪŋ?
>> ðæts gʊdLog ChangesV1.0.0 : Create new with 2 functionsget_IPA()andget_IPAs()V1.2.0 : Update comment and guideline in functions, fixed ERROR for setup with the other Python versionsV1.3.0 : Fixed MISSING install requires and update information for Python versionsV1.4.0 : Update building & testing for this packageV1.4.1 : Fixed Error Importget_IPA()andget_IPAsV2.0.1 : New feature working with FrenchV2.0.2 : Update dependencies
V2.1.2 : Fixing for some issues, remove English US
Package: text2mapviewer
This is a vesy simple way to map your text data usingAltas from NOMICusing the libclick.You have to create an account to get API_KEY NOMIC.<< Atlas enables you to:Store, update and organize multi-million point datasets of unstructured text, images and embeddings.Visually interact with your datasets from a web browser.Run semantic search and vector operations over your datasets.
Use Atlas to:Visualize, interact, collaborate and share large datasets of text and embeddings.Collaboratively clean, tag and label your datasetsBuild high-availability apps powered by semantic searchUnderstand and debug the latent space of your AI model trains  >>How to useInstallationTo install the necessary dependencies, run the following command:python-mvenvmymapenvsourcemymapenv/bin/activate
pipinstall--upgradepippipinstalltext2mapviewerLogin NOMIC serverLogin/create your Nomic account:nomicloginIf you have already your account :nomiclogin[YOUR_API_TOKEN_NOMIC_HERE]Examples :from NOMIC and with lib text2mapviewerfromtext2mapviewer.examples.map_embeddingimportproject# Use the projet from the lib text2mapviewerprint(project)With the libclickafter clone this ripopythonscr/text2mapviewer/examples/map_embedding_click.py--num_embeddings10000--embedding_dim256The Animation OuputSupported Transformer Models from Hugging FaceThis project supports a variety of transformer models, including models from the Hugging Face Model Hub and sentence-transformers. Below are some examples:
- Hugging Face Model: 'prajjwal1/bert-mini'
- Hugging Face Model: 'Sahajtomar/french_semantic'  (french version for semantic search embedding)
- Sentence-Transformers Model: 'sentence-transformers/all-MiniLM-L6-v2' etc...Please ensure that the model you choose is compatible with the project requirements and adjust the--transformer_model_nameoption accordingly.To map your text/csv  filespipinstall-rrequirements.txt
pythonmain.py--transformer-model-nameMODEL_NAME--cache_dirCACHE_DIR--batch-sizeBATCH_SIZE--file-pathFILE_PATHNOTE: for the CACHE_DIR : you can setup it like ==>exportTRANSFORMERS_CACHE=/path_to_your/transformers_cacheGive a fidback.
Package: text2math
A simple package designed to be used for demonstrating basic Natural Language Processing (NLP) feature engineering in Python.## More Info:### Practice Dataset[Stack Exchange Data Dump](https://archive.org/details/stackexchange)### Text Encoding[The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!)by Joel Spolsky](http://www.joelonsoftware.com/articles/Unicode.html)#### Packages[`chardet`](https://pypi.python.org/pypi/chardet) - Universal encoding detector for Python 2 and 3[`cchardet`](https://pypi.python.org/pypi/cchardet/1.0.0) - Universal encoding detector. This library is faster than chardet[`ftfy`](http://ftfy.readthedocs.org/en/latest/#) - fixes text for you[`unidecode`](https://pypi.python.org/pypi/Unidecode) - ASCII transliterations of Unicode text### Natural Language Processing[Care and Feeding of Topic Models: Problems, Diagnostics, and Improvementes](http://www.people.fas.harvard.edu/~airoldi/pub/books/b02.AiroldiBleiEroshevaFienberg2014HandbookMMM/Ch12_MMM2014.pdf)### Functional Programing in Python[Functional programming in PythonExamine the functional aspects of Python: which options work well and which ones you should avoidBy David Mertz](https://www.oreilly.com/ideas/functional-programming-in-python)#### Packages[`toolz`](http://toolz.readthedocs.org/en/latest/) - Toolz provides a set of utility functions for iterators, functions, and dictionaries.[`functools`](https://docs.python.org/2/library/functools.html#module-functools) - Higher-order functions and operations on callable objects.[`itertools`](https://docs.python.org/2/library/itertools.html#module-itertools) - Functions creating iterators for efficient looping.
Package: text2midi
text2midiA program that takes text and converts it to MIDI artUsageFor now, you can runtext2midias follows. This will take the first argument (the message string), and generate the MIDI file that draws out the given message string.python-mtext2midi"<message_string>"path/to/output/file.midFor example, you could run something like,python-mtext2midi"Hello, World\!"hello_world.midNow, to actually see this MIDI file (in piano roll) you can use the DAW of your choice. Logic Pro X, Reaper, GarageBand (?) would work just fine.
Package: text2music
Text-to-Music GenerationThis is the Github Repository for the project "Text-to-Music Generation".To have some intuition about how this works, check this huggingface space:https://huggingface.co/spaces/Mubert/Text-to-MusicCurrently, we are in a stage where we should check related work and communicate ideas. Later, once we have some creative ideas, we can make plans and start implementation.This is an open-source project, so you're welcome to come up with ideas at any time or directly contribute to the GitHub repository.You're welcome to join ourdiscord serverto share your thoughts and report feedbacks!Roadmapcheck related workmake a plan/timelinestart implementationQuick StartRecommended Python Version: 3.7installation:pip install text2musicexperimental example:importtext2musicast2mt2m.experimental.mubert_generate('I am quite happy today',10,'test.mp3')ContributionIf you have any ideas about text-to-music, feel free to discuss in the discord server!Or, if you want to contribute the codes, simply open a pull request!We appreciate your contribution!Active Line of Workwe are collecting real user prompts for later dataset building. If you would like to share your ideas, fill thisgoogle formand submit. We really appreciate your help and will add you to the "wall of love" later:)We are currently building our website:https://nlp-guild.github.io/text2music/. You're welcome to contribute 馃檪
Package: text2numb
Introductiontext2numb is a simple python module for converting text to numbers and back again. It can be used for cryptography, compression or anything else that you can think of.Basic TutorialIn order to convert text into numbers, you have to call thetext2numb(...)function. An example is shown below:>>> import text2numb>>> num = text2numb.text2numb('Hello, world!')>>> num5735816763073854953388147237921In order to convert this back to plain text, all we need to do is call thenumb2text(...)function.>>> txt = text2numb.numb2text(num)>>> txtHello, world!The Setup FunctionEvery time text2numb is imported, a function calledsetup(...)is run. This function sets theint_max_str_sizeusing the sys functionsys.set_int_max_str_size. It is important that this is called, otherwise errors may occour when trying to convert large amounts of data. However, if for whatever reason you need to change this back to the origional value, you can by usingtext2numb.setup(text2numb.max_before_setup)Backend Workings ⚙️The code behind text2numb uses the base64 module base-changing functions in order to convert text to numbers. Text is encoded using base64's implementation of base 16, then decoded using functions defined within the module. The result is a number! And by reversing the process, this module can convert numbers back to text.If you want to convert a number into any base, you can use the_int2base(...)function. An example is shown below:>>> text2numb._int2base(14, 9)'15'In order to convert this back, you can use:>>> int('15', 9)14In addition to_int2base(...), there are two other backend functions -_enc_num(...)and_dec_num(...). These functions are called bytext2numb(...)andnumb2text(...), and serve as the actual computing functions. If for whatever reason you want to call them directly, they take the same arguments astext2numb(...)andnumb2text(...)("string" for_enc_num(...)and "integer" for_dec_num(...).CreditsThe credits for the function "_int2base" go to:Alex Huszagh & Alex Martellion StackOverflow.comTheir StackOverflow awnser (and the question) can be found here:https://stackoverflow.com/questions/2267362/how-to-convert-an-integer-to-a-string-in-any-base?r=SearchResults&s=1%7C235.2771The author of this python module did not ask the origional question.The credits for the rest of the code go to:Pigeon Nation
Package: text2numde
text2numdeThis python package converts numbers written as text into float or integer values.It can furthermore:Validate a text to see if it represents a numbersReplace numbers in sentences without modifying the sentence around the numbers.Example usage>>>fromtext2numdeimporttext2num,is_number,sentence2num>>>text2numde('Eins')1>>>text2num('Einhundertunddrei')103>>>text2numd'Vierzehnkommaachtneundrei')14.893>>>is_number('Einhundertunddrei')True>>>is_number('Zehnzehnhundert')False>>>sentence2num('Ich habe zwei Hunde und vierzig VÃ¶gel.')Ichhabe2Hundeund40VÃ¶gel.
Package: text2phonemesequence
Text2PhonemeSequence: A Python Library to convert text to phoneme sequences used forXPhoneBERTInstallationUsage exampleInstallationTo installText2PhonemeSequence, users have to run the following command:$ pip install text2phonemesequenceUsage exampleThe library usesCharsiuG2Pandsegmentstoolkits to convert text to phoneme sequences. Users can find the information onpretrained_g2p_modelandlanguagein theCharsiuG2Prepository. For languages where words are not separated by spaces such as Vietnamese and Chinese, users need to use an external tokenizer before feeding the dataset or sentences into ourText2PhonemeSequencelibrary.fromtext2phonemesequenceimportText2PhonemeSequence# Load Text2PhonemeSequencemodel=Text2PhonemeSequence(pretrained_g2p_model='charsiu/g2p_multilingual_byT5_small_100',language='eng-us',is_cuda=False)# Convert a raw corpusmodel.infer_dataset(input_file="/absolute/path/to/input/file",output_file="/absolute/path/to/output/file",batch_size=64)# batch_size is the number of words fed into the CharsiuG2P toolkit per times.# Convert a raw sentencemodel.infer_sentence("The overwhelming majority of people in this country know how to sift the wheat from the chaff in what they hear and what they read .")##Output: "ˈθ i ▁ ˈo ʊ v ɝ ˌw ɛ ɫ m ɪ ŋ ▁ m ə ˈd ʒ ɔ ɹ ə t i ▁ ˈɑ f ▁ ˈp i p ə ɫ ▁ ˈɪ n ▁ ˈθ ɪ s ▁ ˈk a ʊ n t ɹ i ▁ ˈn o ʊ ▁ ˈh o ʊ ▁ ˈt o ʊ ▁ ˈs ɪ f t ▁ ˈθ i ▁ ˈw i t ▁ ˈf ɹ ɑ m ▁ ˈθ i ▁ ˈt ʃ æ f ▁ ˈɪ n ▁ ˈw æ t ▁ ˈθ e ɪ ▁ ˈh ɪ ɹ ▁ ˈæ n d ▁ ˈw æ t ▁ ˈθ e ɪ ▁ ˈɹ ɛ d ▁ ."
Package: text2py
Library for processing structured text into python object (dict or list) using templates.Using:text2py-ttemplate.yml-iinput.txt-ooutput.txtimporttext2pyparser=text2py.Parser(template)output=parser.parse(input_file)Template format:Scalar value:-regexp:'Volume(?P<volume>\w+)Author(?P<author>\w+)'key:"{volume}"value:"{author}"{volume:author}Dict value-regexp:'Volume(?P<volume>\w+)Author(?P<author>\w+)Review(?P<review>)'key:"volumes.{volume}"values:-key:'author'value:"{author}"-key:'review'value:"{review}"{'volumes'{volume:{'author':author,'review':review}}}
Package: text2sentences
text2sentencesThis module can be used by tts modules or others.It split input text to sentences according the punctuation of native language, and detects each sentence's language id, sentence in a dialog or not(include in ""), start position in the source text, and if sentence is a new paragraph.DependentslangdetectInstallationpip install langdetect
pip install text2sentencesUsagesee the test/test.pyTestwe only test English, Chinese
Package: text2sql
Text2SQLHow many times have you pulled your hair apart writing a SQL query, now use natural language to convert to appropriate SQL and save your precious hair.Though this can be used as a standalone package, I highly recommend that you usestreamlitto play with the model interactively, to run it interactivelystreamlit run t2s.pyInstallationTo install this repo clone it then runsetup.pygit clone https://github.com/yashbonde/text2sql.git
cd text2sql/
pip3 install -e . #text2sqlParsingNew method of parsing to convert each DB to a graph network, red denotes foreign keys.Trainer FileSimple trainer file.DatasetsUsingCoSQL,Spider,Sparcdatasets, credit to the authors. There are a couple of things to note, we have in total 178 tables, but only 166 tables in training date and dev set has 20 tables.LicenseFollowing are the licensing things:I am releasing my part of code under MIT Licensetransformerscode is under Apache License, Version 2.0
Package: text2svg
Text2SVGThis is a small wrapper aoung Pango and Cairo which allows you to enter a text and get you svg files ready. This is wrapped using Cython.InstallationFor Windows and MacOS, wheels are provided which seems to be working. For linux it is not recommended to use themanylinuxwheels which was published to PyPi as it seems that it is working well. Instead you can install Pango and Cairo along with the header files for your package manage and run the below commandpipinstall--no-binary:all:text2svgOther users, can directly install using pip,pipinstalltext2svgand checking your installation by running the below example.ExampleThis is a small example on how it works.>>>fromtext2svgimport*>>>info=TextInfo("Hello World","hello.svg",50,50)>>>text2svg(info)This will simply create ahello.svgin the current working directory.DocumentationDocumentation is located athttps://text2svg.syrusdark.websiteLICENSEThis project along with its documentation is licensed under GPL-v3. SeeLICENSEfor details.
Package: text2tac
text2tactext2tac converts text to tactics. It lets the Coq proof assistant ask a transformer-type neural network for tactic suggestions.LinksFind the introduction to the Tactician ecosystemhereand the repository for this packagehere.InstallationThe pythontext2tacpackage can be installed withpip installin a standard way from git repo (we aren't yet on pypi.org). For developers a recommended way ispip install -e .from the cloned source repository which allows to develop directly on the source of the installed package. Our key (recommended/tested) pip dependencies arepython==3.10. We tested with 3.10, and 3.11 and on may break the dependencies. We recommend installing everything into a fresh conda environment (for example, one made byconda create --prefix ./text2tac_env python=3.10).Entry-pointsSee `text2tac-server'PreparationsFor training, see the text2tac/transformer folder.
Package: text2tags-lib
text2tagsText2tags is a Python library that uses a finetune of Llama-7b and llama cpp to predict Danbooru tags from natural text.InstallationUse the package manager [pip]pip install text2tags-libUsagefromtext2tagsimportTaggerLlamamodel=TaggerLlama()tags=model.predict_tags("Minato Aqua from hololive with pink and blue twintails in a blue maid outfit")print(', '.join(tags))DemosOther linksTraining Code and Data AnalysisBlogLicenseMIT
Package: texta-anonymizer
Texta AnonymizerFor anonymizing entities in plaintexts.Installationpip install texta-anonymizerFrom Gitpip install git+https://git.texta.ee/texta/texta-anonymizer-python.gitTestingpython -m pytest -v testsDocumentationDocumentation is availablehere.Usage examplesImport Anonymizer and define input datafromtexta_anonymizer.anonymizerimportAnonymizertext="""A. Hitler läks koos oma sõbra Jossif Staliniga poodi.Adolf ostis kolm pakki suitsu ja Jossif neli saia.Adolf Hilteri ja J. Stalini majas elab kass.Hiljem liitus Hitleriga neli koera.Hitteler ja J. Stalen läksid magama.Ka Yossif Stalin oli kohal.Pärast läks A d o l f HITLER joonistama."""names=[{"first_name":"Adolf","last_name":"Hitler"},{"first_name":"Jossif","last_name":"Stalin"}]ExamplesExample 1: Default anonymization# -------------------------------------------------------- ##   Default anonymizer allows:#       1) fuzzy matching (typos)#       2) different forms (cases etc.)#       3) single last name detection#       4) single first name detection# -------------------------------------------------------- #anonymizer=Anonymizer()anonymized_text=anonymizer.anonymize(text,names)Output:>>> print(anonymized_text)
    M.P läks koos oma sõbra F.F-iga poodi.
    M.P ostis kolm pakki suitsu ja F.F neli saia.
    M.P ja F.F-i majas elab kass.
    Hiljem liitus M.P-ga neli koera.
    M.P ja F.F läksid magama.
    Ka F.F oli kohal.
    Pärast läks M.P joonistama.Example 2: Disable misspelled names replacementanonymizer=Anonymizer(replace_misspelled_names=False)anonymized_text=anonymizer.anonymize(text,names)Output>>> print(anonymized_text)
   O.W läks koos oma sõbra A.C-ga poodi.
   O.W ostis kolm pakki suitsu ja A.C neli saia.
   O.W Hilteri ja A.C majas elab kass.
   Hiljem liitus O.W-ga neli koera.
   Hitteler ja J. Stalen läksid magama.
   Ka Yossif A.C oli kohal.
   Pärast läks O.W joonistama.Example 3: Disable replacing single first and last namesanonymizer=Anonymizer(replace_single_first_names=False,replace_single_last_names=False)anonymized_text=anonymizer.anonymize(text,names)Output>>> print(anonymized_text)
   T.N läks koos oma sõbra U.W-ga poodi.
   Adolf ostis kolm pakki suitsu ja Jossif neli saia.
   T.N-i ja U.W majas elab kass.
   Hiljem liitus Hitleriga neli koera.
   Hitteler ja U.W läksid magama.
   Ka U.W oli kohal.
   Pärast läks T.N joonistama.
Package: texta-bert-tagger
TEXTA Bert TaggerInstallationUsing built packagepip install texta-bert-taggerUsing Gitpip install git+https://git.texta.ee/texta/texta-bert-tagger-python.gitTestingpython -m pytest -v testsDocumentationDocumentation for version 1.* is availablehere.Documentation for version 2.* is availablehere.Documentation for version 3.* is availablehere.Usage (for versions >=3..)Fine-tune BERT modelfromtexta_bert_tagger.taggerimportBertTaggerbert_tagger=BertTagger()data_sample={"good":["It was a nice day.","All was well."],"bad":["It was horrible.","What a disaster."]}# Train a model# pos_label - used in metrics (precision, recall, f1-score etc) calculations as true labelbert_tagger.train(data_sample,pos_label="bad",n_epochs=2)# Predictresult=bert_tagger.tag_text("How awful!")print(result)Output[{"prediction": "bad", "probability": 0.55200404, "attributions": {}}]
Package: texta-crf-extractor
TEXTA CRF ExtractorRequirementsPython >= 3.8SciPy installation for scikit-learn (requires BLAS & LAPACK system libraries).Installation:# For debian based systems (ex: debian:buster) to install binary dependencies.

apt-get update && apt-get install python3-scipy

# Install without MLP

pip install texta-crf-extractor

# Install with MLP 

pip install texta-crf-extractor[mlp]Usage:from texta_crf_extractor.crf_extractor import CRFExtractor
from texta_mlp.mlp import MLP

mlp = MLP(language_codes=["en"], default_language_code="en")

# prepare data
texts = ["foo", "bar"]
mlp_docs = [mlp.process(text) for text in texts]

# create extractor
extractor = CRFExtractor(mlp=mlp)

# train the CRF model
extractor.train(mlp_docs)

# tag something
extractor.tag("Tere maailm!")