Update README.md

README.md

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 # bert-rubriques
 
-CamemBERT-base fine-tuned for to classify the section of French news article. 6 categories are allowed: politics, society, international, sport, culture and economics.
-An article relating to political, social or economic events outside France should be classified in international - so that politics contains only French politics, etc. 
-Developed for the [Gallicagram](gallicagram.com) project, to avoid 'structure effects': a word could be more present in a corpus or a time period because the section where it tends to appear
+CamemBERT-base fine-tuned to annotate the section of French news article. 6 categories are allowed: politics, society, international, sport, culture and economics.
+An article relating to political, social or economic events outside France should be classified in international - so that politics contains only French politics, etc. Culture has no borders, so a Bob Dylan album should end up in the culture section. Economics includes both business news (e.g., some company making profit) or macroeconomics news (e.g. a recession). 
+Politics is strictly about political actors -- even though everything is political, of course.
+
+
+This has been developed for the [Gallicagram](gallicagram.com) project, to avoid 'structure effects': a word could be more present in a corpus or a time period because the section where it tends to appear
 is larger, and we might want to control for that. More generally, press corpora are really meta-corpora, and lexicometrics/cultural analytics shouldn't mix sports with politics.
 
-This model has been trained on 960 articles of Le Monde, 
+This model has been trained on 960 articles of Le Monde archives (1945-...) with the wonderful ActiveTigger, using active learning. 
+It is meant to work more broadly on any post-war French news articles - and in our experience, it does work. It is probably not as accurate outside of high-brow printed press - I would not use it on tabloid, social media or TV transcript.
 
 ## Training details
-Trained with the wonderful ActiveTigger, using active learning.
+
 - Base model: camembert/camembert-base
 - Epochs: 5
 - Batch size: 4 (gradient accumulation: 4)