Update README.md

README.md

@@ -88,7 +88,7 @@ sparse_vector = get_sparse_vector(feature, output)
 
 # get similarity score
 sim_score = torch.matmul(sparse_vector[0],sparse_vector[1])
-print(sim_score)   # tensor(22.3299, grad_fn=<DotBackward0>)
+print(sim_score)   # tensor(38.6112, grad_fn=<DotBackward0>)
 
 
 query_token_weight, document_query_token_weight = transform_sparse_vector_to_dict(sparse_vector)
@@ -99,27 +99,55 @@ for token in sorted(query_token_weight, key=lambda x:query_token_weight[x], reve
 
         
 # result:
-# score in query: 2.9262, score in document: 2.1335, token: ny
-# score in query: 2.5206, score in document: 1.5277, token: weather
-# score in query: 2.0373, score in document: 2.3489, token: york
-# score in query: 1.5786, score in document: 0.8752, token: cool
-# score in query: 1.4636, score in document: 1.5132, token: current
-# score in query: 0.7761, score in document: 0.8860, token: season
-# score in query: 0.7560, score in document: 0.6726, token: 2020
-# score in query: 0.7222, score in document: 0.6292, token: summer
-# score in query: 0.6888, score in document: 0.6419, token: nina
-# score in query: 0.6451, score in document: 0.8200, token: storm
-# score in query: 0.4698, score in document: 0.7635, token: brooklyn
-# score in query: 0.4562, score in document: 0.1208, token: julian
-# score in query: 0.3484, score in document: 0.3903, token: wow
-# score in query: 0.3439, score in document: 0.4160, token: usa
-# score in query: 0.2751, score in document: 0.8260, token: manhattan
-# score in query: 0.2013, score in document: 0.7735, token: fog
-# score in query: 0.1989, score in document: 0.2961, token: mood
-# score in query: 0.1653, score in document: 0.3437, token: climate
-# score in query: 0.1191, score in document: 0.1533, token: nature
-# score in query: 0.0665, score in document: 0.0600, token: temperature
-# score in query: 0.0552, score in document: 0.3396, token: windy
+# score in query: 2.7273, score in document: 2.9088, token: york
+# score in query: 2.5734, score in document: 0.9208, token: now
+# score in query: 2.3895, score in document: 1.7237, token: ny
+# score in query: 2.2184, score in document: 1.2368, token: weather
+# score in query: 1.8693, score in document: 1.4146, token: current
+# score in query: 1.5887, score in document: 0.7450, token: today
+# score in query: 1.4704, score in document: 0.9247, token: sunny
+# score in query: 1.4374, score in document: 1.9737, token: nyc
+# score in query: 1.4347, score in document: 1.6019, token: currently
+# score in query: 1.1605, score in document: 0.9794, token: climate
+# score in query: 1.0944, score in document: 0.7141, token: upstate
+# score in query: 1.0471, score in document: 0.5519, token: forecast
+# score in query: 0.9268, score in document: 0.6692, token: verve
+# score in query: 0.9126, score in document: 0.4486, token: huh
+# score in query: 0.8960, score in document: 0.7706, token: greene
+# score in query: 0.8779, score in document: 0.7120, token: picturesque
+# score in query: 0.8471, score in document: 0.4183, token: pleasantly
+# score in query: 0.8079, score in document: 0.2140, token: windy
+# score in query: 0.7537, score in document: 0.4925, token: favorable
+# score in query: 0.7519, score in document: 2.1456, token: rain
+# score in query: 0.7277, score in document: 0.3818, token: skies
+# score in query: 0.6995, score in document: 0.8593, token: lena
+# score in query: 0.6895, score in document: 0.2410, token: sunshine
+# score in query: 0.6621, score in document: 0.3016, token: johnny
+# score in query: 0.6604, score in document: 0.1933, token: skyline
+# score in query: 0.6117, score in document: 0.2197, token: sasha
+# score in query: 0.5962, score in document: 0.0414, token: vibe
+# score in query: 0.5381, score in document: 0.7560, token: hardly
+# score in query: 0.4582, score in document: 0.4243, token: prevailing
+# score in query: 0.4539, score in document: 0.5073, token: unpredictable
+# score in query: 0.4350, score in document: 0.8463, token: presently
+# score in query: 0.3674, score in document: 0.2496, token: hail
+# score in query: 0.3324, score in document: 0.5506, token: shivered
+# score in query: 0.3281, score in document: 0.1964, token: wind
+# score in query: 0.3052, score in document: 0.5785, token: rudy
+# score in query: 0.2797, score in document: 0.0357, token: looming
+# score in query: 0.2712, score in document: 0.0870, token: atmospheric
+# score in query: 0.2471, score in document: 0.3490, token: vicky
+# score in query: 0.2247, score in document: 0.2383, token: sandy
+# score in query: 0.2154, score in document: 0.5737, token: crowded
+# score in query: 0.1723, score in document: 0.1857, token: chilly
+# score in query: 0.1700, score in document: 0.4110, token: blizzard
+# score in query: 0.1183, score in document: 0.0613, token: ##cken
+# score in query: 0.0923, score in document: 0.6363, token: unrest
+# score in query: 0.0624, score in document: 0.2127, token: russ
+# score in query: 0.0558, score in document: 0.5542, token: blackout
+# score in query: 0.0549, score in document: 0.1589, token: kahn
+# score in query: 0.0160, score in document: 0.0566, token: 2020
+# score in query: 0.0125, score in document: 0.3753, token: nighttime
 ```
 
 The above code sample shows an example of neural sparse search. Although there is no overlap token in original query and document, but this model performs a good match.