File size: 3,517 Bytes
a4ff801 1c3741b d532f55 1c3741b d532f55 a127264 d532f55 a127264 d532f55 a127264 d532f55 a127264 d532f55 1c3741b 25a02b8 1c3741b d532f55 a4ff801 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
import streamlit as st
import transformers
from transformers import pipeline, TokenClassificationPipeline, BertForTokenClassification , AutoTokenizer
x = st.text_area('enter')
#model.to("cpu")
tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-large-cased-v1.1", truncation = True, padding=True, model_max_length=512,)
model_checkpoint = BertForTokenClassification.from_pretrained("dexay/Ner2HgF", )
token_classifier = pipeline("token-classification", tokenizer = tokenizer,model=model_checkpoint, )
biotext = x
#split document or text into sentences
lstbiotext = []
flag = 0
tempsen = ""
for e in biotext:
tempsen += e
if e=="(":
flag = 1
if e==")":
flag = 0
if (e =="." or e =="?" or e ==":" ) and flag == 0 :
lstbiotext += [tempsen.strip()]
tempsen = ""
ddata = lstbiotext
#tokenized_dat = tokenize_function(ddata)
az = token_classifier(ddata)
#code to convert NER output to RE input compatible format
#tg_inorder are decoding of labels on which the model was fine tuned on
tg_inorder = ['O',
'B-HORMONE',
'B-EXP_PER',
'I-HORMONE',
'I-CANCER',
'I-EDC',
'B-RECEPTOR',
'B-CANCER',
'I-RECEPTOR',
'B-EDC',
'PAD']
lstSentEnc = []
lstSentbilbl = []
lstSentEnt = []
for itsent in az:
sentaz = itsent
ph = []
phl = []
for e in sentaz:
if e["word"][0]=="#" and len(ph)!=0:
ph[-1]+= e["word"][2:]
else:
ph += [e["word"]]
phl += [e["entity"]]
phltr = []
for e in phl:
phltr += [tg_inorder[int(e[-1])] if len(e)==7 else tg_inorder[int(e[-2:])]]
nwph = []
nwphltr = []
flag = 0
for i in range(len(phltr)-2):
if phltr[i]=="O" and flag != 3 :
nwph += [ph[i]]
nwphltr += [phltr[i]]
continue
elif flag == 3:
nwph[-1] += " "+ph[i]
flag = 1
continue
elif phltr[i][2:]==phltr[i+1][2:] and phltr[i+1][0]=="I" and flag == 0:
nwph += [ph[i]]
nwphltr += [phltr[i]]
flag = 1
continue
elif phltr[i][2:]==phltr[i+1][2:] and phltr[i+1][0]=="I" and flag == 1:
nwph[-1] += " "+ph[i]
continue
# xox with flag == 3
elif phltr[i][2:]==phltr[i+2][2:] and phltr[i+1]=="O" and phltr[i+2][0]=="I" and flag == 0:
nwph += [ph[i]]
nwphltr += [phltr[i]]
flag = 3
continue
elif phltr[i][2:]==phltr[i+2][2:] and phltr[i+1]=="O" and phltr[i+2][0]=="I" and flag == 1:
nwph[-1] += " "+ph[i]
flag = 3
continue
#\ xox
elif flag == 1:
nwph[-1] += " "+ph[i]
flag = 0
continue
else :
nwph += [ph[i]]
nwphltr += [phltr[i]]
continue
# nwph,nwphltr,len(nwph),len(nwphltr)
if nwphltr.count("O") <= len(nwphltr)-2:
for i in range(len(nwph)-1):
if nwphltr[i] != "O":
for j in range(i,len(nwph)):
if nwphltr[j] != "O" and nwphltr[j] != nwphltr[i] and {nwphltr[j], nwphltr[i]} != {"B-CANCER","B-RECEPTOR"}:
sen2ad = ""
for g in range(i):
sen2ad += nwph[g]+" "
sen2ad += "<e1>"+nwph[i]+"</e1> "
for t in range(i+1,j):
sen2ad += nwph[t]+" "
sen2ad += "<e2>"+nwph[j]+"</e2>"
if j<len(nwph):
for l in range(j+1,len(nwph)):
sen2ad += " "+nwph[l]
lstSentEnc += [sen2ad]
lstSentbilbl += [[nwphltr[i],nwphltr[j]]]
lstSentEnt += [[nwph[i],nwph[j]]]
#lstSentEnc,lstSentEnt,lstSentbilbl
if x:
out = token_classifier(x)
st.markdown(lstSentEnc)
|