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| from gensim.models import KeyedVectors | |
| import gradio as gr | |
| word_vectors_path = "classical_bo.wordvectors" | |
| wv = KeyedVectors.load(str(word_vectors_path), mmap='r') | |
| def format_to_html(sim_ouput): | |
| html = "" | |
| for word, sim in sim_output: | |
| html += f"<div>{word}: {round(sim, 4)}</div>" | |
| return "<div>" + html + "</div>" | |
| def find_most_similar(word): | |
| sim_output = wv.most_similar(word) | |
| return format_to_html(sim_ouput) | |
| title = "Classical Bo Word2Vec word vectors" | |
| examples = ['སྟོབས་'] | |
| demo = gr.Interface( | |
| fn=find_most_similar, | |
| inputs="text", | |
| outputs="html", | |
| title=title, | |
| examples=examples | |
| ).launch() |