File size: 11,170 Bytes
4e38daf |
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 |
import torch
import spacy
import en_core_web_sm
from torch import nn
import math
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
from transformers import AutoModel, TrainingArguments, Trainer, RobertaTokenizer, RobertaModel
from transformers import AutoTokenizer
model_checkpoint = "ehsanaghaei/SecureBERT"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)
roberta_model = RobertaModel.from_pretrained(model_checkpoint).to(device)
nlp = en_core_web_sm.load()
pos_spacy_tag_list = ["ADJ","ADP","ADV","AUX","CCONJ","DET","INTJ","NOUN","NUM","PART","PRON","PROPN","PUNCT","SCONJ","SYM","VERB","SPACE","X"]
ner_spacy_tag_list = [bio + entity for entity in list(nlp.get_pipe('ner').labels) for bio in ["B-", "I-"]] + ["O"]
dep_spacy_tag_list = list(nlp.get_pipe("parser").labels)
event_nugget_tag_list = ["Databreach", "Ransom", "PatchVulnerability", "Phishing", "DiscoverVulnerability"]
arg_nugget_relative_pos_tag_list = ["before-same-sentence", "before-differ-sentence", "after-same-sentence", "after-differ-sentence"]
class CustomRobertaWithPOS(nn.Module):
def __init__(self, num_classes):
super(CustomRobertaWithPOS, self).__init__()
self.num_classes = num_classes
self.pos_embed = nn.Embedding(len(pos_spacy_tag_list), 16)
self.ner_embed = nn.Embedding(len(ner_spacy_tag_list), 8)
self.dep_embed = nn.Embedding(len(dep_spacy_tag_list), 8)
self.depth_embed = nn.Embedding(17, 8)
self.subtype_embed = nn.Embedding(len(event_nugget_tag_list), 2)
self.dist_embed = nn.Embedding(11, 6)
self.relative_pos_embed = nn.Embedding(len(arg_nugget_relative_pos_tag_list), 2)
self.roberta = roberta_model
self.dropout1 = nn.Dropout(0.2)
self.fc1 = nn.Linear(self.roberta.config.hidden_size + 50, num_classes)
def forward(self, input_ids, attention_mask, pos_spacy, ner_spacy, dep_spacy, depth_spacy, nearest_nugget_subtype, nearest_nugget_dist, arg_nugget_relative_pos):
outputs = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
last_hidden_output = outputs.last_hidden_state
pooler_output = outputs.pooler_output
pooler_output_unsqz = pooler_output.unsqueeze(1)
pooler_output_fin = pooler_output_unsqz.expand(-1, last_hidden_output.shape[1], -1)
pos_mask = pos_spacy != -100
pos_embed_masked = self.pos_embed(pos_spacy[pos_mask])
pos_embed = torch.zeros((pos_spacy.shape[0], pos_spacy.shape[1], 16), dtype=torch.float).to(device)
pos_embed[pos_mask] = pos_embed_masked
ner_mask = ner_spacy != -100
ner_embed_masked = self.ner_embed(ner_spacy[ner_mask])
ner_embed = torch.zeros((ner_spacy.shape[0], ner_spacy.shape[1], 8), dtype=torch.float).to(device)
ner_embed[ner_mask] = ner_embed_masked
dep_mask = dep_spacy != -100
dep_embed_masked = self.dep_embed(dep_spacy[dep_mask])
dep_embed = torch.zeros((dep_spacy.shape[0], dep_spacy.shape[1], 8), dtype=torch.float).to(device)
dep_embed[dep_mask] = dep_embed_masked
depth_mask = depth_spacy != -100
depth_embed_masked = self.depth_embed(depth_spacy[depth_mask])
depth_embed = torch.zeros((depth_spacy.shape[0], depth_spacy.shape[1], 8), dtype=torch.float).to(device)
depth_embed[dep_mask] = depth_embed_masked
nearest_nugget_subtype_mask = nearest_nugget_subtype != -100
nearest_nugget_subtype_embed_masked = self.subtype_embed(nearest_nugget_subtype[nearest_nugget_subtype_mask])
nearest_nugget_subtype_embed = torch.zeros((nearest_nugget_subtype.shape[0], nearest_nugget_subtype.shape[1], 2), dtype=torch.float).to(device)
nearest_nugget_subtype_embed[dep_mask] = nearest_nugget_subtype_embed_masked
nearest_nugget_dist_mask = nearest_nugget_dist != -100
nearest_nugget_dist_embed_masked = self.dist_embed(nearest_nugget_dist[nearest_nugget_dist_mask])
nearest_nugget_dist_embed = torch.zeros((nearest_nugget_dist.shape[0], nearest_nugget_dist.shape[1], 6), dtype=torch.float).to(device)
nearest_nugget_dist_embed[dep_mask] = nearest_nugget_dist_embed_masked
arg_nugget_relative_pos_mask = arg_nugget_relative_pos != -100
arg_nugget_relative_pos_embed_masked = self.relative_pos_embed(arg_nugget_relative_pos[arg_nugget_relative_pos_mask])
arg_nugget_relative_pos_embed = torch.zeros((arg_nugget_relative_pos.shape[0], arg_nugget_relative_pos.shape[1], 2), dtype=torch.float).to(device)
arg_nugget_relative_pos_embed[dep_mask] = arg_nugget_relative_pos_embed_masked
features_concat = torch.cat((last_hidden_output, pos_embed, ner_embed, dep_embed, depth_embed, nearest_nugget_subtype_embed, nearest_nugget_dist_embed, arg_nugget_relative_pos_embed), 2).to(device)
features_concat = self.dropout1(features_concat)
logits = self.fc1(features_concat)
return logits
def tokenize_and_align_labels_with_pos_ner_dep(examples, tokenizer, label_all_tokens = True):
tokenized_inputs = tokenizer(examples["tokens"], padding='max_length', truncation=True, is_split_into_words=True)
#tokenized_inputs.pop('input_ids')
ner_spacy = []
pos_spacy = []
dep_spacy = []
depth_spacy = []
nearest_nugget_subtype = []
nearest_nugget_dist = []
arg_nugget_relative_pos = []
for i, (pos, ner, dep, depth, subtype, dist, relative_pos) in enumerate(zip(examples["pos_spacy"],
examples["ner_spacy"],
examples["dep_spacy"],
examples["depth_spacy"],
examples["nearest_nugget_subtype"],
examples["nearest_nugget_dist"],
examples["arg_nugget_relative_pos"])):
word_ids = tokenized_inputs.word_ids(batch_index=i)
previous_word_idx = None
ner_spacy_ids = []
pos_spacy_ids = []
dep_spacy_ids = []
depth_spacy_ids = []
nearest_nugget_subtype_ids = []
nearest_nugget_dist_ids = []
arg_nugget_relative_pos_ids = []
for word_idx in word_ids:
# Special tokens have a word id that is None. We set the label to -100 so they are automatically
# ignored in the loss function.
if word_idx is None:
ner_spacy_ids.append(-100)
pos_spacy_ids.append(-100)
dep_spacy_ids.append(-100)
depth_spacy_ids.append(-100)
nearest_nugget_subtype_ids.append(-100)
nearest_nugget_dist_ids.append(-100)
arg_nugget_relative_pos_ids.append(-100)
# We set the label for the first token of each word.
elif word_idx != previous_word_idx:
ner_spacy_ids.append(ner[word_idx])
pos_spacy_ids.append(pos[word_idx])
dep_spacy_ids.append(dep[word_idx])
depth_spacy_ids.append(depth[word_idx])
nearest_nugget_subtype_ids.append(subtype[word_idx])
nearest_nugget_dist_ids.append(dist[word_idx])
arg_nugget_relative_pos_ids.append(relative_pos[word_idx])
# For the other tokens in a word, we set the label to either the current label or -100, depending on
# the label_all_tokens flag.
else:
ner_spacy_ids.append(ner[word_idx] if label_all_tokens else -100)
pos_spacy_ids.append(pos[word_idx] if label_all_tokens else -100)
dep_spacy_ids.append(dep[word_idx] if label_all_tokens else -100)
depth_spacy_ids.append(depth[word_idx] if label_all_tokens else -100)
nearest_nugget_subtype_ids.append(subtype[word_idx] if label_all_tokens else -100)
nearest_nugget_dist_ids.append(dist[word_idx] if label_all_tokens else -100)
arg_nugget_relative_pos_ids.append(relative_pos[word_idx] if label_all_tokens else -100)
previous_word_idx = word_idx
ner_spacy.append(ner_spacy_ids)
pos_spacy.append(pos_spacy_ids)
dep_spacy.append(dep_spacy_ids)
depth_spacy.append(depth_spacy_ids)
nearest_nugget_subtype.append(nearest_nugget_subtype_ids)
nearest_nugget_dist.append(nearest_nugget_dist_ids)
arg_nugget_relative_pos.append(arg_nugget_relative_pos_ids)
tokenized_inputs["pos_spacy"] = pos_spacy
tokenized_inputs["ner_spacy"] = ner_spacy
tokenized_inputs["dep_spacy"] = dep_spacy
tokenized_inputs["depth_spacy"] = depth_spacy
tokenized_inputs["nearest_nugget_subtype"] = nearest_nugget_subtype
tokenized_inputs["nearest_nugget_dist"] = nearest_nugget_dist
tokenized_inputs["arg_nugget_relative_pos"] = arg_nugget_relative_pos
return tokenized_inputs
def find_nearest_nugget_features(doc, start_idx, end_idx, event_nuggets):
nearest_subtype = None
nearest_dist = math.inf
relative_pos = None
mid_idx = (end_idx + start_idx) / 2
for nugget in event_nuggets:
mid_nugget_idx = (nugget["startOffset"] + nugget["endOffset"]) / 2
dist = abs(mid_nugget_idx - mid_idx)
if dist < nearest_dist:
nearest_dist = dist
nearest_subtype = nugget["subtype"]
for sent in doc.sents:
if between_idxs(mid_idx, sent.start_char, sent.end_char) and between_idxs(mid_nugget_idx, sent.start_char, sent.end_char):
if mid_idx < mid_nugget_idx:
relative_pos = "before-same-sentence"
else:
relative_pos = "after-same-sentence"
break
elif between_idxs(mid_nugget_idx, sent.start_char, sent.end_char) and mid_idx > mid_nugget_idx:
relative_pos = "after-differ-sentence"
break
elif between_idxs(mid_idx, sent.start_char, sent.end_char) and mid_idx < mid_nugget_idx:
relative_pos = "before-differ-sentence"
break
nearest_dist = int(min(10, nearest_dist // 20))
return nearest_subtype, nearest_dist, relative_pos
def find_dep_depth(token):
depth = 0
current_token = token
while current_token.head != current_token:
depth += 1
current_token = current_token.head
return min(depth, 16)
def between_idxs(idx, start_idx, end_idx):
return idx >= start_idx and idx <= end_idx |