Update README.md

README.md

@@ -57,3 +57,60 @@ yields the following output:
 Span [1,2]: "George Washington"   [− Labels: PER (0.9968)]
 Span [5]: "Washington"   [− Labels: LOC (0.9994)]
 ```
+
+
+### Script to train this model
+
+The following Flair script was used to train this model: 
+
+```python
+from flair import set_seed
+from flair.data import Corpus
+from flair.datasets import CONLL_03
+from flair.embeddings import TokenEmbeddings, WordEmbeddings, StackedEmbeddings, FlairEmbeddings
+from typing import List
+
+
+# 1. get the corpus
+corpus: Corpus = CONLL_03()
+
+# 2. what tag do we want to predict?
+tag_type = 'ner'
+
+# 3. make the tag dictionary from the corpus
+tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
+
+# 4. initialize embeddings
+embedding_types: List[TokenEmbeddings] = [
+
+    # GloVe embeddings
+    WordEmbeddings('glove'),
+
+    # contextual string embeddings, forward
+    FlairEmbeddings('news-forward'),
+
+    # contextual string embeddings, backward
+    FlairEmbeddings('news-backward'),
+]
+
+# embedding stack consists of Flair and GloVe embeddings
+embeddings = StackedEmbeddings(embeddings=embedding_types)
+
+# 5. initialize sequence tagger
+from flair.models import SequenceTagger
+
+tagger: SequenceTagger = SequenceTagger(hidden_size=256,
+                                        embeddings=embeddings,
+                                        tag_dictionary=tag_dictionary,
+                                        tag_type=tag_type)
+
+# 6. initialize trainer
+from flair.trainers import ModelTrainer
+
+trainer: ModelTrainer = ModelTrainer(tagger, corpus)
+
+# 7. run training
+trainer.train('resources/taggers/ner-english',
+              train_with_dev=True,
+              max_epochs=150)
+```