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import numpy as np |
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import gradio as gr |
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from sentence_transformers import SentenceTransformer, util |
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def load_model(model_name): |
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return SentenceTransformer(model_name) |
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def predict(original_sentence_input, *sentences_to_compare): |
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model_name = "sartifyllc/African-Cross-Lingua-Embeddings-Model" |
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model = load_model(model_name) |
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result = { |
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"Model Name": model_name, |
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"Original Sentence": original_sentence_input, |
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"Sentences to Compare": sentences_to_compare, |
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"Similarity Scores": {} |
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} |
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if original_sentence_input and sentences_to_compare: |
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sentences = [original_sentence_input] + list(sentences_to_compare) |
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embeddings = model.encode(sentences) |
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original_embedding = embeddings[0] |
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for i, sentence in enumerate(sentences_to_compare, start=1): |
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similarity_score = util.pytorch_cos_sim(original_embedding, embeddings[i]).item() |
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result["Similarity Scores"][f"Sentence {i}"] = similarity_score |
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return result |
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model_name_display = gr.Markdown(value="**Model Name**: sartifyllc/African-Cross-Lingua-Embeddings-Model") |
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original_sentence_input = gr.Textbox(lines=2, placeholder="Enter the original sentence here...", label="Original Sentence") |
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sentence_to_compare_inputs = gr.Dataset(components=[gr.Textbox(lines=2, placeholder="Enter the sentence you want to compare...", label=f"Sentence to Compare {i}") for i in range(1, 4)], label="Sentences to Compare", datatype="str") |
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inputs = [model_name_display, original_sentence_input, sentence_to_compare_inputs] |
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outputs = gr.JSON(label="Detailed Similarity Scores") |
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gr.Interface( |
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fn=predict, |
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title="African Cross-Lingua Embeddings Model's Demo", |
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description="Compute the semantic similarity across various sentences among any African Languages using African-Cross-Lingua-Embeddings-Model.", |
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inputs=inputs, |
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outputs=outputs, |
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cache_examples=False, |
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allow_flagging="never" |
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).launch(debug=True, share=True) |
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