Adding App

app.py

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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
+
+
+@st.cache
+def load_model():
+  model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
+  model.eval()
+  return model
+      
+@st.cache
+def load_plot_data():
+  embs = np.load('semeval2015-embs.npy')
+  data = pd.read_csv('semeval2015-data.csv')
+  return embs, data
+    
+st.title("Sentence Embedding for Spanish with Bertin")
+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.")
+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 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))
+    
+    print('Generating visualization...')
+    sentembs, data = load_plot_data()
+    X_embedded = TSNE(n_components=2, learning_rate='auto',
+                  init='random').fit_transform(np.concatenate([sentembs, encodings], axis=0))
+                  
+    data = data.append({'sent': sent1, 'color': '#F0E442'}, ignore_index=True) # sentence 1
+    data = data.append({'sent': sent2, 'color': '#D55E00'}, ignore_index=True) # sentence 2
+    data['x'] = X_embedded[:,0]
+    data['y'] = X_embedded[:,1]
+    
+    source = ColumnDataSource(data)
+    
+    p = figure(title="Embeddings in space")
+    p.circle(
+      x='x',
+      y='y',
+      legend_label="Objects",
+      #fill_color=["red"],
+      color='color',
+      fill_alpha=0.5,
+      line_color="blue",
+      size=14,
+      source=source
+    )
+    p.add_tools(HoverTool(
+      tooltips=[
+          ('sent', '@sent')
+      ],
+      formatters={
+          '@sent': 'printf'
+      },
+      mode='mouse'
+    ))
+    st.bokeh_chart(p, use_container_width=True)
+  else:
+      st.write('Missing a sentences')
+else:
+  pass
+
+

requirements.txt

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+sentence-transformers==2.2.0
+transformers==4.17.0
+torch==1.10.2
+sklearn==0.0
+bokeh==2.4.1