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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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from sentence_transformers.util import cos_sim |
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from sentence_transformers import SentenceTransformer |
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from bokeh.plotting import figure, output_notebook, show, save |
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from bokeh.io import output_file, show |
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from bokeh.models import ColumnDataSource, HoverTool |
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from sklearn.manifold import TSNE |
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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.cache |
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def load_plot_data(): |
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embs = np.load('semeval2015-embs.npy') |
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data = pd.read_csv('semeval2015-data.csv') |
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return embs, data |
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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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st.write("Introduce two sentence to see their cosine similarity and a graph showing them in the embedding space.") |
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st.write("Authors: Anibal Pérez, Emilio Tomás Ariza, Lautaro Gesuelli y Mauricio Mazuecos.") |
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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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print('Generating visualization...') |
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sentembs, data = load_plot_data() |
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X_embedded = TSNE(n_components=2, learning_rate='auto', |
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init='random').fit_transform(np.concatenate([sentembs, encodings], axis=0)) |
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data = data.append({'sent': sent1, 'color': '#F0E442'}, ignore_index=True) |
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data = data.append({'sent': sent2, 'color': '#D55E00'}, ignore_index=True) |
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data['x'] = X_embedded[:,0] |
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data['y'] = X_embedded[:,1] |
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source = ColumnDataSource(data) |
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p = figure(title="Embeddings in space") |
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p.circle( |
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x='x', |
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y='y', |
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legend_label="Objects", |
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color='color', |
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fill_alpha=0.5, |
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line_color="blue", |
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size=14, |
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source=source |
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) |
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p.add_tools(HoverTool( |
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tooltips=[ |
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('sent', '@sent') |
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], |
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formatters={ |
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'@sent': 'printf' |
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}, |
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mode='mouse' |
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)) |
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st.bokeh_chart(p, use_container_width=True) |
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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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