<jupyter_start><jupyter_text>[deplacy](https://koichiyasuoka.github.io/deplacy/) ⲉⲛⲁⲛⲁⲗⲩ ⲙⲡⲥⲩⲛⲧⲁⲜ  ϩⲛ[Stanza](https://stanfordnlp.github.io/stanza)<jupyter_code>!pip install deplacy stanza
import stanza
stanza.download("cop")
nlp=stanza.Pipeline("cop")
doc=nlp("ⲙⲟⲟϣⲉ ϩⲱⲥ ϣⲏⲣⲉ ⲙⲡⲟⲩⲟⲉⲓⲛ ·")
import deplacy
deplacy.render(doc,WordRight=True)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc))<jupyter_output><empty_output><jupyter_text>ϩⲛ[spaCy-Coptic](https://github.com/KoichiYasuoka/spaCy-Coptic)<jupyter_code>!pip install deplacy spacy_coptic
import spacy_coptic
nlp=spacy_coptic.load()
doc=nlp("ⲙⲟⲟϣⲉ ϩⲱⲥ ϣⲏⲣⲉ ⲙⲡⲟⲩⲟⲉⲓⲛ ·")
import deplacy
deplacy.render(doc,WordRight=True)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc))<jupyter_output><empty_output><jupyter_text>ϩⲛ[spacy-udpipe](https://github.com/TakeLab/spacy-udpipe)<jupyter_code>!pip install deplacy spacy-udpipe
import spacy_udpipe
spacy_udpipe.download("coptic")
nlp=spacy_udpipe.load("coptic")
doc=nlp("ⲙⲟⲟϣⲉ ϩⲱⲥ ϣⲏⲣⲉ ⲙⲡⲟⲩⲟⲉⲓⲛ ·")
import deplacy
deplacy.render(doc,WordRight=True)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc))<jupyter_output><empty_output><jupyter_text>ϩⲛ[UDPipe 2](http://ufal.mff.cuni.cz/udpipe/2)<jupyter_code>!pip install deplacy
def nlp(t):
  import urllib.request,urllib.parse,json
  with urllib.request.urlopen("https://lindat.mff.cuni.cz/services/udpipe/api/process?model=cop&tokenizer&tagger&parser&data="+urllib.parse.quote(t)) as r:
    return json.loads(r.read())["result"]
doc=nlp("ⲙⲟⲟϣⲉ ϩⲱⲥ ϣⲏⲣⲉ ⲙⲡⲟⲩⲟⲉⲓⲛ ·")
import deplacy
deplacy.render(doc,WordRight=True)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1493212669683258, 'fraction_numerical': 0.0011312217194570137, 'mean_word_length': 9.22543352601156, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, 'https://': 5, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '390657', 'license': 'MIT', 'n_tokens_mistral': 1014, 'n_tokens_neox': 980, 'n_words': 85, 'path': 'doc/cop.ipynb', 'repo_name': 'KoichiYasuoka/deplacy', 'size': '1768'}