Spaces:
Running
Running
File size: 8,263 Bytes
8504fa5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 |
import streamlit as st
import pandas as pd
import numpy as np
from nltk.tokenize import sent_tokenize
# Split the text into sentences. Necessary for NLI models
def split_sentences(text):
return sent_tokenize(text)
###### Prompting
def query_model_prompting(model, text, prompt_with_mask, top_k, targets):
"""Query the prompting model
:param model: Prompting model object
:type model: Huggingface pipeline object
:param text: Event description (context)
:type text: str
:param prompt_with_mask: Prompt with a mask
:type prompt_with_mask: str
:param top_k: Number of tokens to output
:type top_k: integer
:param targets: Restrict the answer to these possible tokens
:type targets: list
:return: Results of the prompting model
:rtype: list of dict
"""
sequence = text + prompt_with_mask
output_tokens = model(sequence, top_k=top_k, targets=targets)
return output_tokens
def do_sentence_entailment(sentence, hypothesis, model):
"""Concatenate context and hypothesis then perform entailment
:param sentence: Event description (context), 1 sentence
:type sentence: str
:param hypothesis: Mask filled with a token
:type hypothesis: str
:param model: NLI Model
:type model: Huggingface pipeline
:return: DataFrame containing the result of the entailment
:rtype: pandas DataFrame
"""
text = sentence + '</s></s>' + hypothesis
res = model(text, return_all_scores=True)
df_res = pd.DataFrame(res[0])
df_res['label'] = df_res['label'].apply(lambda x: x.lower())
df_res.columns = ["Label", "Score"]
return df_res
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
return np.exp(x) / np.sum(np.exp(x), axis=0)
######### NLI + PROMPTING
def do_text_entailment(text, hypothesis, model):
"""
Do entailment for each sentence of the event description as
model was trained on sentence pair
:param text: Event Description (context)
:type text: str
:param hypothesis: Mask filled with a token
:type hypothesis: str
:param model: Model NLI
:type model: Huggingface pipeline
:return: List of entailment results for each sentence of the text
:rtype: list
"""
text_entailment_results = []
for i, sentence in enumerate(split_sentences(text)):
df_score = do_sentence_entailment(sentence, hypothesis, model)
text_entailment_results.append((sentence, hypothesis, df_score))
return text_entailment_results
def get_true_entailment(text_entailment_results, nli_limit):
"""
From the result of each sentence entailment, extract the maximum entailment score and
check if it's higher than the entailment threshold.
"""
true_hypothesis_list = []
max_score = 0
for sentence_entailment in text_entailment_results:
df_score = sentence_entailment[2]
score = df_score[df_score["Label"] == 'entailment']["Score"].values.max()
if score > max_score:
max_score = score
if max_score > nli_limit:
true_hypothesis_list.append((sentence_entailment[1], np.round(max_score,2)))
return list(set(true_hypothesis_list))
def prompt_to_nli(text, prompt, model_prompting, nli_model, nlp, top_k=10, nli_limit=0.5, remove_lemma=False):
"""
Apply the PR-ENT pipeline
:param text: Event description
:type text: str
:param prompt: Prompt with mask
:type prompt: str
:param model_prompting: Prompting Model
:type model_prompting: Huggingface pipeline
:param nli_model: NLI Model
:type nli_model: Huggingface pipeline
:param top_k: Number of words output by the prompting model
:type top_k: int
:param nli_limit: Entailment threshold
:type nli_limit: float
:return: Results of the pipeline
:rtype: list
"""
prompt_masked = prompt.format(model_prompting.tokenizer.mask_token)
label = []
output_prompting = query_model_prompting(model_prompting, text, prompt_masked, top_k, targets=None)
if remove_lemma:
output_prompting = filter_prompt_output_by_lemma(prompt, output_prompting, nlp)
for token in output_prompting:
hypothesis = prompt.format(token['token_str'])
text_entailment_results = do_text_entailment(text, hypothesis, nli_model)
true_hypothesis_list = get_true_entailment(text_entailment_results, nli_limit)
if len(true_hypothesis_list) > 0:
label.append(((token['token_str'], token['score']), true_hypothesis_list[0]))
return label
def display_nli_pr_results_as_list(title, list_results):
"""
Display the list of entailment results as a streamlit choice list
"""
st.markdown(
"""
<style>
span[data-baseweb="tag"] {
background-color: red !important;
}
</style>
""",
unsafe_allow_html=True,
)
prompt_list = st.multiselect(
title,
list_results
,
list_results, key='results_mix')
##### QA
def question_answering(model, text, questions_list, to_print=True):
"""
Apply question answering model
:param model: QA Model
:type model: Huggingface pipeline
:param text: Event description (context)
:type text: str
:param question: Question to answer
:type question: str
:return: Tuple containing the answer and the confidence score
:rtype: tuple
"""
for question in questions_list:
QA_input = {
'question': question,
'context': text}
res = model(QA_input, handle_impossible_answer=False)
if to_print:
st.write("Question: {}".format(question))
st.write("Answer: {}".format(res["answer"]))
return res["answer"], res["score"]
### Prompt + NLI + QA
def get_who_what_whom_qa(text, tokens, model_qa):
who_what_whom = []
if not tokens:
res_dict = {"Actor":'', "Action":'', "Target": ''}
st.write("No entailed tokens.")
else:
for token in tokens:
# res_dict = {"who":'', "did_what":token, "to_whom": '', "qa_score": []}
res_dict = {"Actor":'', "Action":token, "Target": ''}
if token[-3:] == 'ing':
perp,score_p = question_answering(model_qa, text, ["Who was {}?".format(token)], to_print=False)
else:
perp,score_p = question_answering(model_qa, text, ["Who {} people?".format(token)], to_print=False)
if perp:
res_dict["Actor"] = perp + ' [' + str(np.round(score_p*100,1)) + '%]'
else:
res_dict["Actor"] = 'N/A' + ' [' + str(np.round(score_p*100,1)) + '%]'
victim,score_v = question_answering(model_qa, text, ["Who was {}?".format(token)], to_print=False)
if victim:
res_dict["Target"] = victim + ' [' + str(np.round(score_v*100,1)) + '%]'
else:
res_dict["Target"] = 'N/A' + ' [' + str(np.round(score_v*100,1)) + '%]'
who_what_whom.append(res_dict)
return who_what_whom
def remove_similar_lemma_from_list(prompt, list_words, nlp):
## Compute a dictionnary with the lemma for all tokens
## If there is a duplicate lemma then the dictionnary value will be a list of the corresponding tokens
lemma_dict = {}
for each in list_words:
mask_filled = nlp(prompt.strip('.').format(each))
lemma_dict.setdefault([x.lemma_ for x in mask_filled][-1],[]).append(each)
## Get back the list of tokens
## If multiple tokens available then take the shortest one
new_token_list = []
for key in lemma_dict.keys():
if len(lemma_dict[key]) >= 1:
new_token_list.append(min(lemma_dict[key], key=len))
else:
raise ValueError("Lemma dict has 0 corresponding words")
return new_token_list
def filter_prompt_output_by_lemma(prompt, output_prompting, nlp):
"""
Remove all similar lemmas from the prompt output (e.g. "protest", "protests")
"""
list_words = [x['token_str'] for x in output_prompting]
new_token_list = remove_similar_lemma_from_list(prompt, list_words, nlp)
return [x for x in output_prompting if x['token_str'] in new_token_list] |