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+# CoNLL
+
+??? abstract "TLDR"
+
+    ```{ .python .no-check }
+    import edsnlp
+
+    stream = edsnlp.data.read_conll(path)
+    stream = stream.map_pipeline(nlp)
+    ```
+
+You can easily integrate CoNLL formatted files into your project by using EDS-NLP's CoNLL reader.
+
+There are many CoNLL formats corresponding to different shared tasks, but one of the most common is the CoNLL-U format, which is used for dependency parsing. In CoNLL files, each line corresponds to a token and contains various columns with information about the token, such as its index, form, lemma, POS tag, and dependency relation.
+
+EDS-NLP lets you specify the name of the `columns` if they are different from the default CoNLL-U format. If the `columns` parameter is unset, the reader looks for a comment containing `# global.columns` to infer the column names. Otherwise, the columns are
+
+```
+ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC
+```
+
+A typical CoNLL file looks like this:
+
+```{ title="sample.conllu" }
+1	euh	euh	INTJ	_	_	5	discourse	_	SpaceAfter=No
+2	,	,	PUNCT	_	_	1	punct	_	_
+3	il	lui	PRON	_	Gender=Masc|Number=Sing|Person=3|PronType=Prs	5	expl:subj	_	_
+...
+```
+
+## Reading CoNLL files {: #edsnlp.data.conll.read_conll }
+
+::: edsnlp.data.conll.read_conll
+    options:
+        heading_level: 3
+        show_source: false
+        show_toc: false
+        show_bases: false