| import streamlit as st | |
| from sentence_transformers.util import cos_sim | |
| from sentence_transformers import SentenceTransformer | |
| def process(sent1, sent2): | |
| if sent1 and sent2: | |
| encodings = model.encode([sent1, sent2]) | |
| sim = cos_sim(encodings[0], encodings[1]).numpy().tolist()[0][0] | |
| st.write('Cosine Similarity: {0:.4f}'.format(sim)) | |
| st.title("Sentence Embedding for Spanish with Bertin") | |
| st.write("Sentence embedding for spanish trained on NLI. Used for Sentence Textual Similarity.") | |
| model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli') | |
| sent1 = st.text_area('Enter sentence 1') | |
| sent2 = st.text_area('Enter sentence 2') | |
| st.button('Compute similarity', key=None, help=None, on_click=process, args=(sent1, sent2)) | |