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import streamlit as st |
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from sentence_transformers.util import cos_sim |
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from sentence_transformers import SentenceTransformer |
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@st.cache |
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def load_model(): |
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model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli') |
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model.eval() |
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return model |
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st.title("Sentence Embedding for Spanish with Bertin") |
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st.write("Sentence embedding for spanish trained on NLI. Used for Sentence Textual Similarity. Based on the model hackathon-pln-es/bertin-roberta-base-finetuning-esnli.") |
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sent1 = st.text_area('Enter sentence 1') |
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sent2 = st.text_area('Enter sentence 2') |
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if st.button('Compute similarity'): |
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if sent1 and sent2: |
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model = load_model() |
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encodings = model.encode([sent1, sent2]) |
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sim = cos_sim(encodings[0], encodings[1]).numpy().tolist()[0][0] |
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st.text('Cosine Similarity: {0:.4f}'.format(sim)) |
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else: |
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st.write('Missing a sentences') |
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else: |
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pass |
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