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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from sentence_transformers.util import cos_sim | |
| from sentence_transformers import SentenceTransformer | |
| from bokeh.plotting import figure, output_notebook, show, save | |
| from bokeh.io import output_file, show | |
| from bokeh.models import ColumnDataSource, HoverTool | |
| from sklearn.manifold import TSNE | |
| def load_model(): | |
| model = SentenceTransformer('hackathon-pln-es/paraphrase-spanish-distilroberta') | |
| model.eval() | |
| return model | |
| st.title("Sentence Embedding for Spanish with Bertin") | |
| st.write("Sentence embedding for spanish trained according to instructions in the paper [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/pdf/2004.09813.pdf) and the [documentation](https://www.sbert.net/examples/training/multilingual/README.html) accompanying its companion python package. We have used the strongest available pretrained English Bi-Encoder ([paraphrase-mpnet-base-v2](https://www.sbert.net/docs/pretrained_models.html#sentence-embedding-models)) as a teacher model, and the pretrained Spanish [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) as the student model. Used for Sentence Textual Similarity. Based on the model hackathon-pln-es/paraphrase-spanish-distilroberta.") | |
| st.write("Introduce two sentence to see their cosine similarity and a graph showing them in the embedding space.") | |
| st.write("Authors: Anibal Pérez, Emilio Tomás Ariza, Lautaro Gesuelli Pinto y Mauricio Mazuecos.") | |
| sent1 = st.text_area('Enter sentence 1') | |
| sent2 = st.text_area('Enter sentence 2') | |
| if st.button('Compute similarity'): | |
| if sent1 and sent2: | |
| model = load_model() | |
| encodings = model.encode([sent1, sent2]) | |
| sim = cos_sim(encodings[0], encodings[1]).numpy().tolist()[0][0] | |
| st.text('Cosine Similarity: {0:.4f}'.format(sim)) | |
| else: | |
| st.write('Missing a sentences') | |
| else: | |
| pass | |