diff --git a/train.py b/train.py
new file mode 100644
index 0000000..c6a463b
--- /dev/null
+++ b/train.py
@@ -0,0 +1,68 @@
+from flair.data import Sentence
+from flair.models import SequenceTagger
+from tqdm import tqdm
+
+# --------------------- VARIABLES ---------------------- #
+
+
+
+# ----------------- DATA PREPROCESSING ----------------- #
+
+# def convert_to_BIO_format(directory):
+# 	"""
+# 	Read data in BIO format.
+# 	"""
+# 	_in = open(directory + "/in.tsv", "r").read().split(" ")
+# 	_expected = open(directory + "/expected.tsv", "r").read().split(" ")
+
+# 	_lines = list(map(lambda x: f"{x[0]} {x[1]}", zip(_in, _expected)))
+# 	_lines = list(map(lambda x: "" if x.startswith("</S>") else x, _lines))
+
+# 	open(directory + "/in_bio.tsv", "w").write("\n".join(_lines))
+
+# convert_to_BIO_format("dev-0")
+
+# ---------------------- GENERATE PREDICTIONS ---------------------- #
+DIRECTORY = "dev-0"
+
+# load tagger
+tagger = SequenceTagger.load("flair/ner-english")
+batches = open(f"{DIRECTORY}/in.tsv", "r").read().split("\n")[:-1]
+results = []
+
+with tqdm(total=len(batches)) as pbar:
+	for batch in batches:
+		batch_result = []
+
+		for sentence in batch.split("</S>"):
+			raw_sentence = sentence
+			sentence = sentence.strip()
+
+			if sentence == "":
+				continue
+
+			sentence = Sentence(sentence, use_tokenizer=False)
+
+			tagger.predict(sentence)
+
+			out = ["O"] * len(sentence)
+
+			for entity in sentence.get_spans('ner'):
+
+				is_first = True
+				for token in entity:
+					if is_first:
+						out[token.idx - 1] = f"B-{entity.tag}"
+					else:
+						out[token.idx - 1] = f"I-{entity.tag}"
+
+					is_first = False
+			batch_result.append(" ".join(out))
+	
+		results.append(" O ".join(batch_result) + " O")
+
+		pbar.update(1)
+
+open(f"{DIRECTORY}/out.tsv", "w").write("\n".join(results))
+
+# 456