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') \ No newline at end of file +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 \ No newline at end of file