Update README.md

README.md

@@ -9,11 +9,11 @@ datasets:
 inference: false
 ---
 
-## English NER in Flair (default model)
+## French NER in Flair (default model)
 
-This is the standard 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
+This is the standard 4-class NER model for French that ships with [Flair](https://github.com/flairNLP/flair/).
 
-F1-Score: **92,98** (CoNLL-03)
+F1-Score: **90,61** (WikiNER)
 
 Predicts 4 tags:
 
@@ -37,10 +37,10 @@ from flair.data import Sentence
 from flair.models import SequenceTagger
 
 # load tagger
-tagger = SequenceTagger.load("flair/ner-english")
+tagger = SequenceTagger.load("flair/ner-french")
 
 # make example sentence
-sentence = Sentence("George Washington went to Washington")
+sentence = Sentence("George Washington est allé à Washington")
 
 # predict NER tags
 tagger.predict(sentence)
@@ -58,11 +58,11 @@ for entity in sentence.get_spans('ner'):
 
 This yields the following output:
 ```
-Span [1,2]: "George Washington"   [− Labels: PER (0.9968)]
-Span [5]: "Washington"   [− Labels: LOC (0.9994)]
+Span [1,2]: "George Washington"   [− Labels: PER (0.7394)]
+Span [6]: "Washington"   [− Labels: LOC (0.9161)]
 ```
 
-So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*". 
+So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington est allé à Washington*". 
 
 
 ---
@@ -73,11 +73,11 @@ The following Flair script was used to train this model:
 
 ```python
 from flair.data import Corpus
-from flair.datasets import CONLL_03
+from flair.datasets import WIKINER_FRENCH
 from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
 
 # 1. get the corpus
-corpus: Corpus = CONLL_03()
+corpus: Corpus = WIKINER_FRENCH()
 
 # 2. what tag do we want to predict?
 tag_type = 'ner'
@@ -89,13 +89,13 @@ tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
 embedding_types = [
 
     # GloVe embeddings
-    WordEmbeddings('glove'),
+    WordEmbeddings('fr'),
 
     # contextual string embeddings, forward
-    FlairEmbeddings('news-forward'),
+    FlairEmbeddings('fr-forward'),
 
     # contextual string embeddings, backward
-    FlairEmbeddings('news-backward'),
+    FlairEmbeddings('fr-backward'),
 ]
 
 # embedding stack consists of Flair and GloVe embeddings
@@ -115,7 +115,7 @@ from flair.trainers import ModelTrainer
 trainer = ModelTrainer(tagger, corpus)
 
 # 7. run training
-trainer.train('resources/taggers/ner-english',
+trainer.train('resources/taggers/ner-french',
               train_with_dev=True,
               max_epochs=150)
 ```