diff --git a/script/fasttext_001.py b/script/fasttext_001.py
index ad954da6dfe2352736305af718cb826f4d5cb9f1..08de961eca34f88c52a185e5de02020c3fd02a3d 100644
--- a/script/fasttext_001.py
+++ b/script/fasttext_001.py
@@ -135,4 +135,32 @@ latinise_skip.save_model('/dump/wordvec/latinise_skip.bin')
 
 # Train and save LatinISE CBOW model
 latinise_cbow = fasttext.train_unsupervised(latinise_lemma_path, model='cbow')
-latinise_cbow.save_model('/dump/wordvec/latinise_cbow.bin')
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+latinise_cbow.save_model('/dump/wordvec/latinise_cbow.bin')
+
+################
+#| Evaluation \#
+################
+
+# Import the eval function and gensim's load_facebook_model function
+from pyDigest import eval
+from gensim.models.fasttext import load_facebook_model
+
+# Load the pre-trained fasttext model
+lasla_path = '/home/mribary/Documents/wordvec/lasla_skip.bin'
+lasla_model = load_facebook_model(lasla_path)
+print(eval(lasla_model)) # 85.57% matches LiLa's synonyms
+
+# Load the pre-trained fasttext model
+latinise_path = '/home/mribary/Documents/wordvec/latinise_skip.bin'
+latinise_model = load_facebook_model(latinise_path)
+print(eval(latinise_model)) # 87.86% matches LiLa's synonyms
+
+# Load the pre-trained fasttext model
+romtext_path = '/home/mribary/Documents/wordvec/romtext_skip.bin'
+romtext_model = load_facebook_model(romtext_path)
+print(eval(romtext_model)) # 66.68% matches LiLa's synonyms
+
+# Load the pre-trained fasttext model
+digest_path = '/home/mribary/Documents/wordvec/digest_skip.bin'
+digest_model = load_facebook_model(digest_path)
+print(eval(digest_model)) # 62.76% matches LiLa's synonyms
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