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kermitt2 · Aug 12, 2020 · 7ddbd46 · 7ddbd46
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diff --git a/doc/End-to-end-evaluation.md b/doc/End-to-end-evaluation.md
@@ -2,7 +2,7 @@
 
 Individual models can be evaluated as explained in [Training the different models of Grobid](Training-the-models-of-Grobid.md).
 
-For an end-to-end evaluation, covering the whole extraction process from the parsing of PDF to the end result of the cascading of several CRF models, GROBID includes two possible evaluation progresses:
+For an end-to-end evaluation, covering the whole extraction process from the parsing of PDF to the end result of the cascading of several sequence labelling models, GROBID includes two possible evaluation progresses:
 
 * against a set of JATS-encoded (NLM) articles, such as [PubMed Central](http://www.ncbi.nlm.nih.gov/pmc) or [bioRxiv](https://www.biorxiv.org). For its publications, PubMed Central aprovides both PDF and fulltext XML files in the [NLM](http://www.ncbi.nlm.nih.gov/pmc/pmcdoc/tagging-guidelines/article/style.html) format. Keeping in mind some limits described bellow, it is possible to estimate the ability of Grobid to extract and normalize the content of the PDF documents for matching the quality of the NLM file. bioRxiv is used in Grobid to evaluate more precisely performance on preprint articles. 
 

diff --git a/doc/Training-the-models-of-Grobid.md b/doc/Training-the-models-of-Grobid.md
@@ -2,7 +2,7 @@
 
 ## Models
 
-Grobid uses different CRF models depending on the labeling task to be realized. For a complex extraction and parsing tasks (for instance header extraction and parsing), several models are used in cascade. The current models are the following ones:
+Grobid uses different sequence labelling models depending on the labeling task to be realized. For a complex extraction and parsing tasks (for instance header extraction and parsing), several models are used in cascade. The current models are the following ones:
 
 * affiliation-address
 
@@ -129,4 +129,3 @@ If you wish to maintain the training corpus as gold standard, these automaticall
 ## Training guidelines
 
 Annotation guidelines for creating the training data corresponding to the different GROBID models are available from the [following page](training/General-principles.md).
-