Initial upload: Cybersecurity NER model with RoBERTa backbone

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+ner/model filter=lfs diff=lfs merge=lfs -text
+transformer/model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,117 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: spacy
+tags:
+- spacy
+- ner
+- named-entity-recognition
+- cybersecurity
+- infosec
+- security
+- token-classification
+pipeline_tag: token-classification
+datasets:
+- custom
+model-index:
+- name: cybersec-ner-roberta
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    metrics:
+    - type: f1
+      value: 0.694
+      name: F1
+    - type: precision
+      value: 0.691
+      name: Precision
+    - type: recall
+      value: 0.698
+      name: Recall
+---
+
+# Cybersecurity NER Model
+
+spaCy NER model with RoBERTa transformer backbone, trained for cybersecurity entity extraction.
+
+## Entity Types (9)
+
+| Entity | Description | F1 Score |
+|--------|-------------|----------|
+| SECURITY_ROLE | Job titles (CISO, SOC Analyst, Pentester) | 57.8% |
+| TECHNICAL_SKILL | Skills (Incident Response, Threat Hunting) | 54.7% |
+| SECURITY_TOOL | Tools (Splunk, CrowdStrike, Metasploit) | 100% |
+| CERTIFICATION | Certs (CISSP, OSCP, CEH) | 100% |
+| FRAMEWORK | Frameworks (NIST, MITRE ATT&CK, ISO 27001) | 100% |
+| THREAT_TYPE | Threats (APT, ransomware, phishing) | 90% |
+| ATTACK_TECHNIQUE | Attacks (SQL injection, XSS, RCE) | 100% |
+| REGULATION | Regulations (GDPR, HIPAA, PCI-DSS) | 100% |
+| SECURITY_DOMAIN | Domains (Cloud Security, Network Security) | 13% |
+
+**Overall: F1 69.4% | Precision 69.1% | Recall 69.8%**
+
+## Training Data
+
+- 1,500+ unique cybersecurity entities
+- 1,000 synthetic training examples (CVs, job descriptions)
+- Domain-adapted RoBERTa on 40K security texts
+
+## Usage
+
+```python
+import spacy
+
+# Load model
+nlp = spacy.load("path/to/model")
+
+# Extract entities
+doc = nlp("CISO with CISSP certification, expert in Splunk SIEM and threat hunting")
+
+for ent in doc.ents:
+    print(f"{ent.text}: {ent.label_}")
+```
+
+Output:
+```
+CISO: SECURITY_ROLE
+CISSP: CERTIFICATION
+Splunk: SECURITY_TOOL
+threat hunting: TECHNICAL_SKILL
+```
+
+## Requirements
+
+```
+spacy>=3.8.0
+spacy-transformers>=1.3.0
+```
+
+## Use Cases
+
+- Threat intelligence parsing
+- Security talent matching (CV/job analysis)
+- Skills inventory extraction
+- Compliance document analysis
+
+## Limitations
+
+- SECURITY_DOMAIN has low recall (7%) - needs more training data
+- SECURITY_ROLE and TECHNICAL_SKILL F1 below target - ongoing improvement
+- Trained primarily on English text
+
+## License
+
+Apache 2.0
+
+## Citation
+
+```bibtex
+@misc{cybersec-ner-2024,
+  author = {PKI},
+  title = {Cybersecurity NER Model},
+  year = {2024},
+  publisher = {HuggingFace},
+}
+```

config.cfg

@@ -0,0 +1,147 @@
+[paths]
+train = "../data/spacy/train.spacy"
+dev = "../data/spacy/dev.spacy"
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = "pytorch"
+seed = 42
+
+[nlp]
+lang = "en"
+pipeline = ["transformer","ner"]
+batch_size = 128
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+vectors = {"@vectors":"spacy.Vectors.v1"}
+
+[components]
+
+[components.ner]
+factory = "ner"
+incorrect_spans_key = null
+moves = null
+scorer = {"@scorers":"spacy.ner_scorer.v1"}
+update_with_oracle_cut_size = 100
+
+[components.ner.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
+
+[components.ner.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "transformer"
+
+[components.transformer]
+factory = "transformer"
+max_batch_items = 4096
+set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
+
+[components.transformer.model]
+@architectures = "spacy-transformers.TransformerModel.v3"
+name = "../models/roberta-cybersecurity"
+mixed_precision = true
+
+[components.transformer.model.get_spans]
+@span_getters = "spacy-transformers.strided_spans.v1"
+window = 128
+stride = 96
+
+[components.transformer.model.grad_scaler_config]
+
+[components.transformer.model.tokenizer_config]
+use_fast = true
+
+[components.transformer.model.transformer_config]
+
+[corpora]
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[training]
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 3
+patience = 1600
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 200
+frozen_components = []
+annotating_components = []
+before_to_disk = null
+before_update = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+size = 1000
+discard_oversize = false
+get_length = null
+tolerance = 0.2
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = true
+
+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = false
+eps = 0.00000001
+
+[training.optimizer.learn_rate]
+@schedules = "warmup_linear.v1"
+warmup_steps = 250
+total_steps = 20000
+initial_rate = 0.00005
+
+[training.score_weights]
+ents_f = 1.0
+ents_p = 0.0
+ents_r = 0.0
+ents_per_type = null
+
+[pretraining]
+
+[initialize]
+vectors = ${paths.vectors}
+init_tok2vec = ${paths.init_tok2vec}
+vocab_data = null
+lookups = null
+before_init = null
+after_init = null
+
+[initialize.components]
+
+[initialize.tokenizer]

meta.json

@@ -0,0 +1,100 @@
+{
+  "lang":"en",
+  "name":"cybersec-ner",
+  "version":"1.0.0",
+  "spacy_version":">=3.8.11,<3.9.0",
+  "description":"Cybersecurity NER model with RoBERTa backbone - extracts security roles, tools, certifications, threats, and more",
+  "author":"PKI",
+  "email":"",
+  "url":"https://huggingface.co/pki-here/cybersec-ner",
+  "license":"Apache-2.0",
+  "spacy_git_version":"e7a662a",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null,
+    "mode":"default"
+  },
+  "labels":{
+    "transformer":[
+
+    ],
+    "ner":[
+      "ATTACK_TECHNIQUE",
+      "CERTIFICATION",
+      "FRAMEWORK",
+      "REGULATION",
+      "SECURITY_DOMAIN",
+      "SECURITY_ROLE",
+      "SECURITY_TOOL",
+      "TECHNICAL_SKILL",
+      "THREAT_TYPE"
+    ]
+  },
+  "pipeline":[
+    "transformer",
+    "ner"
+  ],
+  "components":[
+    "transformer",
+    "ner"
+  ],
+  "disabled":[
+
+  ],
+  "performance":{
+    "ents_f":0.6943820225,
+    "ents_p":0.6912751678,
+    "ents_r":0.69751693,
+    "ents_per_type":{
+      "SECURITY_ROLE":{
+        "p":0.4748603352,
+        "r":0.7391304348,
+        "f":0.5782312925
+      },
+      "REGULATION":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
+      },
+      "SECURITY_DOMAIN":{
+        "p":1.0,
+        "r":0.0697674419,
+        "f":0.1304347826
+      },
+      "TECHNICAL_SKILL":{
+        "p":0.5981308411,
+        "r":0.5039370079,
+        "f":0.547008547
+      },
+      "SECURITY_TOOL":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
+      },
+      "CERTIFICATION":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
+      },
+      "THREAT_TYPE":{
+        "p":0.9,
+        "r":0.9,
+        "f":0.9
+      },
+      "FRAMEWORK":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
+      },
+      "ATTACK_TECHNIQUE":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
+      }
+    },
+    "transformer_loss":1749.1020674201,
+    "ner_loss":20168.375
+  }
+}

ner/cfg

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+{
+  "moves":null,
+  "update_with_oracle_cut_size":100,
+  "multitasks":[
+
+  ],
+  "min_action_freq":1,
+  "learn_tokens":false,
+  "beam_width":1,
+  "beam_density":0.0,
+  "beam_update_prob":0.0,
+  "incorrect_spans_key":null
+}

ner/model

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+oid sha256:16a9dd30ffebdfed3a56b5e90821c3192f3088d3cff27b84185a63e59ba503c6
+size 308071

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+  "\u0ca0_\u0ca0",
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+  "\u2018S",
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+  "\u253b",
+  "\u253b\u2501\u253b",
+  "\u256f",
+  "\u25a1",
+  "\ufe35",
+  "\uff09"
+]

vocab/vectors.cfg

@@ -0,0 +1,3 @@
+{
+  "mode":"default"
+}