Commit
·
cb8b12f
1
Parent(s):
21c1379
Upload model
Browse files- config.json +159 -0
- configuration.py +9 -0
- model.py +88 -2
- pytorch_model.bin +2 -2
config.json
CHANGED
@@ -2,13 +2,172 @@
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"architectures": [
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"CybersecurityKnowledgeGraphModel"
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],
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"auto_map": {
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"AutoConfig": "configuration.CybersecurityKnowledgeGraphConfig",
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"AutoModelForTokenClassification": "model.CybersecurityKnowledgeGraphModel"
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},
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"event_argument_model_path": "cybersecurity_knowledge_graph/argument_model_state_dict.pth",
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"event_nugget_model_path": "cybersecurity_knowledge_graph/nugget_model_state_dict.pth",
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"event_realis_model_path": "cybersecurity_knowledge_graph/realis_model_state_dict.pth",
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"torch_dtype": "float32",
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"transformers_version": "4.33.2"
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}
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"architectures": [
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"CybersecurityKnowledgeGraphModel"
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],
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"arg_2_role": {
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"CVE": [
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"CVE"
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],
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"Capabilities": [
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"Attack-Pattern",
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"Capabilities",
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"Issues-Addressed"
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],
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"Data": [
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"Compromised-Data",
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"Trusted-Entity"
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],
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"Device": [
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"Vulnerable_System",
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"Victim",
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"Supported_Platform"
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],
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"File": [
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"Tool",
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"Trusted-Entity"
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],
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"GPE": [
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"Place"
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],
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"Malware": [
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"Tool"
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],
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"Money": [
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"Price",
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"Damage-Amount"
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],
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"Number": [
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"Number-of-Data",
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"Number-of-Victim"
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],
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"Organization": [
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"Victim",
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"Releaser",
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"Discoverer",
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"Attacker",
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"Vulnerable_System_Owner",
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"Trusted-Entity"
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],
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"PII": [
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"Compromised-Data",
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"Trusted-Entity"
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],
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"Patch": [
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"Patch"
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],
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"PaymentMethod": [
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"Payment-Method"
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],
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"Person": [
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"Victim",
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"Attacker",
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"Discoverer",
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"Releaser",
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"Trusted-Entity",
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"Vulnerable_System_Owner"
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],
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"Purpose": [
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"Purpose"
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],
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"Software": [
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"Vulnerable_System",
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"Victim",
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"Trusted-Entity",
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"Supported_Platform"
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],
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"System": [
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"Victim",
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"Supported_Platform",
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"Vulnerable_System",
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"Trusted-Entity"
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],
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"Time": [
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"Time"
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],
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"Version": [
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"Patch-Number",
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"Vulnerable_System_Version"
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],
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"Vulnerability": [
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"Vulnerability"
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],
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"Website": [
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"Trusted-Entity",
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"Tool",
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"Vulnerable_System",
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"Victim",
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"Supported_Platform"
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]
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},
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"auto_map": {
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"AutoConfig": "configuration.CybersecurityKnowledgeGraphConfig",
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"AutoModelForTokenClassification": "model.CybersecurityKnowledgeGraphModel"
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},
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"event_args_list": [
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"O",
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"B-System",
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"I-System",
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"B-Organization",
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"B-Money",
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"I-Money",
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"B-Device",
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"B-Person",
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"I-Person",
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"B-Vulnerability",
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"I-Vulnerability",
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"B-Capabilities",
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"I-Capabilities",
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"I-Organization",
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"B-PaymentMethod",
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"I-PaymentMethod",
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"B-Data",
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"I-Data",
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"B-Number",
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"I-Number",
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"B-Malware",
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"I-Malware",
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"B-PII",
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"I-PII",
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"B-CVE",
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"I-CVE",
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"B-Purpose",
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"I-Purpose",
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"B-File",
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"I-File",
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"I-Device",
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"B-Time",
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"I-Time",
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"B-Software",
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"I-Software",
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"B-Patch",
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"I-Patch",
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"B-Version",
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"I-Version",
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"B-Website",
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"I-Website",
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"B-GPE",
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"I-GPE"
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],
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"event_argument_model_path": "cybersecurity_knowledge_graph/argument_model_state_dict.pth",
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"event_nugget_list": [
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"O",
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"B-Ransom",
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"I-Ransom",
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"B-DiscoverVulnerability",
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"I-DiscoverVulnerability",
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"B-PatchVulnerability",
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"I-PatchVulnerability",
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"B-Databreach",
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"I-Databreach",
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"B-Phishing",
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"I-Phishing"
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],
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"event_nugget_model_path": "cybersecurity_knowledge_graph/nugget_model_state_dict.pth",
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"event_realis_model_path": "cybersecurity_knowledge_graph/realis_model_state_dict.pth",
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"realis_list": [
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"O",
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"Generic",
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"Other",
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"Actual"
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],
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"torch_dtype": "float32",
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"transformers_version": "4.33.2"
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}
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configuration.py
CHANGED
@@ -1,6 +1,9 @@
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from transformers import PretrainedConfig
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import torch
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class CybersecurityKnowledgeGraphConfig(PretrainedConfig):
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def __init__(
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self.event_nugget_model_path = event_nugget_model_path
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self.event_argument_model_path = event_argument_model_path
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self.event_realis_model_path = event_realis_model_path
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super().__init__(**kwargs)
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from transformers import PretrainedConfig
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import torch
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from cybersecurity_knowledge_graph.utils import event_args_list, event_nugget_list, realis_list, arg_2_role
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class CybersecurityKnowledgeGraphConfig(PretrainedConfig):
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def __init__(
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self.event_nugget_model_path = event_nugget_model_path
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self.event_argument_model_path = event_argument_model_path
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self.event_realis_model_path = event_realis_model_path
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self.event_nugget_list = event_nugget_list
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self.event_args_list = event_args_list
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self.realis_list = realis_list
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self.arg_2_role = arg_2_role
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super().__init__(**kwargs)
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model.py
CHANGED
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from transformers import PreTrainedModel
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import torch
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from cybersecurity_knowledge_graph.nugget_model_utils import CustomRobertaWithPOS as NuggetModel
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from cybersecurity_knowledge_graph.args_model_utils import CustomRobertaWithPOS as ArgumentModel
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def __init__(self, config):
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super().__init__(config)
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self.event_nugget_model_path = config.event_nugget_model_path
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self.event_argument_model_path = config.event_argument_model_path
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self.event_realis_model_path = config.event_realis_model_path
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self.event_realis_model.load_state_dict(torch.load(self.event_realis_model_path))
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self.event_argument_model.load_state_dict(torch.load(self.event_argument_model_path))
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def forward(self, text):
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nugget_dataloader, _ = self.event_nugget_dataloader(text)
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realis_pred = self.forward_model(self.event_realis_model, realis_dataloader)
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argument_preds[idx] = argument_pred
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realis_preds[idx] = realis_pred
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-
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def forward_model(self, model, dataloader):
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predicted_label = []
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for batch in dataloader:
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with torch.no_grad():
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logits = model(**batch)
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-
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batch_predicted_label = logits.argmax(-1)
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predicted_label.append(batch_predicted_label)
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return torch.cat(predicted_label, dim=-1)
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from transformers import PreTrainedModel
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import torch
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import joblib, os
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import numpy as np
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from sentence_transformers import SentenceTransformer
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from transformers import AutoTokenizer
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from cybersecurity_knowledge_graph.nugget_model_utils import CustomRobertaWithPOS as NuggetModel
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from cybersecurity_knowledge_graph.args_model_utils import CustomRobertaWithPOS as ArgumentModel
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def __init__(self, config):
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super().__init__(config)
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self.tokenizer = AutoTokenizer.from_pretrained("ehsanaghaei/SecureBERT")
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self.event_nugget_model_path = config.event_nugget_model_path
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self.event_argument_model_path = config.event_argument_model_path
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self.event_realis_model_path = config.event_realis_model_path
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self.event_realis_model.load_state_dict(torch.load(self.event_realis_model_path))
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self.event_argument_model.load_state_dict(torch.load(self.event_argument_model_path))
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role_classifiers = {}
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folder_path = '/cybersecurity_knowledge_graph/arg_role_models'
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for filename in os.listdir(os.getcwd() + folder_path):
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if filename.endswith('.joblib'):
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file_path = os.getcwd() + os.path.join(folder_path, filename)
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clf = joblib.load(file_path)
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arg = filename.split(".")[0]
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role_classifiers[arg] = clf
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self.role_classifiers = role_classifiers
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self.embed_model = SentenceTransformer('sentence_transformer')
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self.event_nugget_list = config.event_nugget_list
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self.event_args_list = config.event_args_list
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self.realis_list = config.realis_list
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self.arg_2_role = config.arg_2_role
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def forward(self, text):
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nugget_dataloader, _ = self.event_nugget_dataloader(text)
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realis_pred = self.forward_model(self.event_realis_model, realis_dataloader)
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argument_preds[idx] = argument_pred
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realis_preds[idx] = realis_pred
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attention_mask = [batch["attention_mask"] for batch in nugget_dataloader]
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attention_mask = torch.cat(attention_mask, dim=-1)
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input_ids = [batch["input_ids"] for batch in nugget_dataloader]
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input_ids = torch.cat(input_ids, dim=-1)
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output = {"nugget" : nugget_pred, "argument" : argument_preds, "realis" : realis_preds, "input_ids" : input_ids, "attention_mask" : attention_mask}
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no_of_batch = output['input_ids'].shape[0]
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structured_output = []
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for b in range(no_of_batch):
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token_mask = [True if self.tokenizer.decode(token) not in self.tokenizer.all_special_tokens else False for token in output['input_ids'][b]]
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filtered_ids = output['input_ids'][b][token_mask]
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filtered_tokens = [self.tokenizer.decode(token) for token in filtered_ids]
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filtered_nuggets = output['nugget'][b][token_mask]
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filtered_args = output['argument'][b][token_mask]
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filtered_realis = output['realis'][b][token_mask]
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batch_output = [{"id" : id.item(), "token" : token, "nugget" : self.event_nugget_list[int(nugget.item())], "argument" : self.event_args_list[int(arg.item())], "realis" : self.realis_list[int(realis.item())]}
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for id, token, nugget, arg, realis in zip(filtered_ids, filtered_tokens, filtered_nuggets, filtered_args, filtered_realis)]
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structured_output.extend(batch_output)
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args = [(idx, item["argument"], item["token"]) for idx, item in enumerate(structured_output) if item["argument"]!= "O"]
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entities = []
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current_entity = None
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for position, label, token in args:
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if label.startswith('B-'):
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if current_entity is not None:
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entities.append(current_entity)
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113 |
+
current_entity = {'label': label[2:], 'text': token.replace(" ", ""), 'start': position, 'end': position}
|
114 |
+
elif label.startswith('I-'):
|
115 |
+
if current_entity is not None:
|
116 |
+
current_entity['text'] += ' ' + token.replace(" ", "")
|
117 |
+
current_entity['end'] = position
|
118 |
+
|
119 |
+
for entity in entities:
|
120 |
+
context = self.tokenizer.decode([item["id"] for item in structured_output[max(0, entity["start"] - 15) : min(len(structured_output), entity["end"] + 15)]])
|
121 |
+
entity["context"] = context
|
122 |
+
|
123 |
+
for entity in entities:
|
124 |
+
if len(self.arg_2_role[entity["label"]]) > 1:
|
125 |
+
sent_embed = self.embed_model.encode(entity["context"])
|
126 |
+
arg_embed = self.embed_model.encode(entity["text"])
|
127 |
+
embed = np.concatenate((sent_embed, arg_embed))
|
128 |
+
|
129 |
+
arg_clf = self.role_classifiers[entity["label"]]
|
130 |
+
role_id = arg_clf.predict(embed.reshape(1, -1))
|
131 |
+
role = self.arg_2_role[entity["label"]][role_id[0]]
|
132 |
+
|
133 |
+
entity["role"] = role
|
134 |
+
else:
|
135 |
+
entity["role"] = self.arg_2_role[entity["label"]][0]
|
136 |
+
|
137 |
+
for item in structured_output:
|
138 |
+
item["role"] = "O"
|
139 |
+
for entity in entities:
|
140 |
+
for i in range(entity["start"], entity["end"] + 1):
|
141 |
+
structured_output[i]["role"] = entity["role"]
|
142 |
+
return structured_output
|
143 |
|
144 |
def forward_model(self, model, dataloader):
|
145 |
predicted_label = []
|
146 |
for batch in dataloader:
|
147 |
with torch.no_grad():
|
148 |
logits = model(**batch)
|
|
|
149 |
batch_predicted_label = logits.argmax(-1)
|
150 |
predicted_label.append(batch_predicted_label)
|
151 |
return torch.cat(predicted_label, dim=-1)
|
pytorch_model.bin
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5b14783587eb16be4dac27e2bc3b5d738ee1772b44cf48d0240edef88aaee6e9
|
3 |
+
size 1587052173
|