Hugging Face's logo

Spaces:
NealCaren
/
paragraphs
Runtime error

App Files Community
paragraphs
File size: 4,994 Bytes 
ee21672
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
642f7f2
ee21672
 
 
 
 
 
 
 
 
 
 
642f7f2
ee21672
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
 
import streamlit as st

import numpy as np

import pickle
from collections import OrderedDict

from sentence_transformers import SentenceTransformer, CrossEncoder, util
import torch

from nltk.tokenize import sent_tokenize

import nltk
nltk.download('punkt')


if not torch.cuda.is_available():
    print("Warning: No GPU found. Please add GPU to your notebook")


import pandas as pd
st.title('Sociology Paragraph Search')

st.write('This page is a work-in-progress that allows you to search through articles recently published in a few sociology journals and retrieve the most relevant paragraphs. ')

st.markdown('''Notes:
* To get the best results, search like you are using Google. My best luck comes from phrases, such as "social movements and public opinion", "inequality in latin america", "race color skin tone measurement", "audit study experiment gender", "crenshaw intersectionality" or "logistic regression or linear probability model".
* The dataset currently includes only article published since 2016 in Social Forces, Social Problems, Sociology of Race and Ethnicity, Gender and Society, Socius, JHSB, and the American Sociological Review (approximately 100K paragraphs from 2K articles).
* The most relevant paragarph to your search is returned first, along with up to four other related paragraphs from that article.
* The most relevant sentence within each paragraph, as determined by math, is bolded.
* Behind the scenes, the semantic search uses [text embeddings](https://www.sbert.net) with a [retrieve & re-rank](https://colab.research.google.com/github/UKPLab/sentence-transformers/blob/master/examples/applications/retrieve_rerank/retrieve_rerank_simple_wikipedia.ipynb) process to find the best matches.
* Let [me](mailto:neal.caren@unc.edu) know what you think.
''')


def sent_trans_load():
    #We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
    bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
    bi_encoder.max_seq_length = 256     #Truncate long passages to 256 tokens, max 512
    return bi_encoder

def sent_cross_load():
    #We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
    return cross_encoder


@st.cache
def load_data():
    dfs = [pd.read_json(f'passages_{i}.jsonl', lines=True) for i in range(0,5)]
    df = pd.concat(dfs)
    df.reset_index(inplace=True, drop=True)
    return df


with st.spinner(text="Loading data..."):
    df = load_data()
    passages = df['text'].values

@st.cache
def load_embeddings():
    efs = [np.load(f'embeddings_{i}.pt.npy')  for i in range(0,5)]
    corpus_embeddings = np.concatenate(efs)
    return corpus_embeddings

with st.spinner(text="Loading embeddings..."):
    corpus_embeddings = load_embeddings()





def search(query, top_k=40):

    ##### Sematic Search #####
    # Encode the query using the bi-encoder and find potentially relevant passages
    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)


    hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
    hits = hits[0]  # Get the hits for the first query
    ##### Re-Ranking #####
    # Now, score all retrieved passages with the cross_encoder
    cross_inp = [[query, passages[hit['corpus_id']]] for hit in hits]
    cross_scores = cross_encoder.predict(cross_inp)

    # Sort results by the cross-encoder scores
    for idx in range(len(cross_scores)):
        hits[idx]['cross-score'] = cross_scores[idx]

    # Output of top-5 hits from re-ranker
    print("\n-------------------------\n")
    print("Search Results")
    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)

    hd = OrderedDict()
    for hit in hits[0:20]:

        row_id = hit['corpus_id']
        cite = df.loc[row_id]['cite']
        #graph = passages[row_id]
        graph = df.loc[row_id]['text']

        # Find best sentence
        ab_sentences= [s for s in sent_tokenize(graph)]
        cross_inp = [[query, s] for s in ab_sentences]
        cross_scores = cross_encoder.predict(cross_inp)
        thesis = pd.Series(cross_scores, ab_sentences).sort_values().index[-1]
        graph = graph.replace(thesis, f'**{thesis}**')

        if cite in hd:

          hd[cite].append(graph)
        else:
          hd[cite] = [graph]

    for cite, graphs in hd.items():
        cite = cite.replace(",  ", '. "').replace(', Social ', '", Social ')
        st.write(cite)
        for graph in graphs[:5]:
          st.write(f'* {graph}')
        st.write('')
       # print("\t{:.3f}\t{}".format(hit['cross-score'], passages[hit['corpus_id']].replace("\n", " ")))



search_query = st.text_input('Enter your search phrase:')
if search_query!='':
    with st.spinner(text="Searching and sorting results (may take up to 30 seconds)"):
        bi_encoder = sent_trans_load()
        cross_encoder = sent_cross_load()
        search(search_query)