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@@ -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
+tok2vec/model filter=lfs diff=lfs merge=lfs -text

README.md

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+# Cybersecurity NER Model v8
+
+Named Entity Recognition model for cybersecurity domain text, trained on spaCy v3.8 with custom training data.
+
+## Model Description
+
+Fine-tuned NER model for extracting 13 cybersecurity entity types from technical documentation, CVs, job descriptions, threat reports, and compliance documents.
+
+## Performance
+
+**Test Results (v8):**
+- Pass Rate: 94% (62/66 tests)
+- Dev F1 Score: 98.58%
+- Precision: 98.71%
+- Recall: 98.46%
+- Training Steps: 11,500 (early stopping)
+- Training Data: 2,223 examples
+
+**Entity Type Performance:**
+| Entity Type | Test Pass Rate | Dev Set F1 |
+|-------------|----------------|------------|
+| CVE | 100% (3/3) | 100.00% |
+| AUDIT_TERM | 75% (3/4) | 100.00% |
+| SECURITY_TOOL | 100% (4/4) | 100.00% |
+| CERTIFICATION | 100% (4/4) | 98.73% |
+| SECURITY_ROLE | 100% (4/4) | 98.11% |
+| FRAMEWORK | 100% (4/4) | 93.88% |
+| TECHNICAL_SKILL | 100% (4/4) | 100.00% |
+| ACRONYM | 100% (4/4) | 100.00% |
+| SECURITY_DOMAIN | 100% (4/4) | 100.00% |
+| ATTACK_TECHNIQUE | 75% (3/4) | 98.70% |
+| THREAT_TYPE | 75% (3/4) | 95.24% |
+| REGULATION | 75% (3/4) | 96.55% |
+| CONTROL_ID | 100% (4/4) | - |
+
+## Entity Types
+
+1. **CVE** - CVE identifiers (e.g., CVE-2024-1234)
+2. **CERTIFICATION** - Security certifications (CISSP, OSCP, CEH, CISM, Security+)
+3. **FRAMEWORK** - Security frameworks (NIST CSF, ISO 27001, MITRE ATT&CK, CIS Controls)
+4. **ATTACK_TECHNIQUE** - Attack methods (SQL injection, XSS, CSRF, buffer overflow)
+5. **TECHNICAL_SKILL** - Technical skills (Incident Response, Forensics, Penetration Testing)
+6. **AUDIT_TERM** - Audit/compliance terms (Risk assessment, Compliance audit, Security review)
+7. **SECURITY_ROLE** - Job roles (CISO, SOC Analyst, Security Engineer, Pentester)
+8. **THREAT_TYPE** - Threat types (APT, ransomware, phishing, DDoS, malware)
+9. **ACRONYM** - Security acronyms (SIEM, EDR, SOAR, IDS/IPS, WAF, DLP)
+10. **SECURITY_DOMAIN** - Security domains (Cloud Security, Network Security, Application Security)
+11. **REGULATION** - Regulations (GDPR, HIPAA, PCI-DSS, SOX, CCPA)
+12. **SECURITY_TOOL** - Security tools (Splunk, Metasploit, Burp Suite, Nmap, Wireshark)
+13. **CONTROL_ID** - Control identifiers (ISO 27001 A.5.1, NIST CSF PR.AC-1, CIS Control 1.1)
+
+## Usage
+
+```python
+import spacy
+
+# Load model
+nlp = spacy.load("path/to/model")
+
+# Extract entities
+text = "CISSP certified professional with experience in Splunk and Metasploit"
+doc = nlp(text)
+
+for ent in doc.ents:
+    print(f"{ent.text} -> {ent.label_}")
+```
+
+**Output:**
+```
+CISSP -> CERTIFICATION
+Splunk -> SECURITY_TOOL
+Metasploit -> SECURITY_TOOL
+```
+
+## Training Data
+
+**Sources:**
+- v7 merged data: 1,448 examples
+- v8 generated: 1,347 examples with multi-entity patterns, case variants
+- Manual curated: 100 examples
+- Final dataset: 2,223 unique examples (after validation and deduplication)
+
+**v8 Improvements:**
+- Multi-entity "X and Y" patterns (50 examples per entity type)
+- Title case variants (CISSP, cissp, Cissp)
+- Comma-separated list patterns
+- AUDIT_TERM edge cases (Compliance audit)
+
+**Entity Distribution:**
+- AUDIT_TERM: 326 (12.4%)
+- CERTIFICATION: 295 (11.2%)
+- SECURITY_TOOL: 293 (11.1%)
+- ATTACK_TECHNIQUE: 282 (10.7%)
+- THREAT_TYPE: 263 (10.0%)
+- TECHNICAL_SKILL: 228 (8.6%)
+- REGULATION: 222 (8.4%)
+- CVE: 182 (6.9%)
+- FRAMEWORK: 165 (6.3%)
+- SECURITY_ROLE: 153 (5.8%)
+- ACRONYM: 142 (5.4%)
+- SECURITY_DOMAIN: 85 (3.2%)
+
+## Training Configuration
+
+- **Framework:** spaCy 3.8
+- **Architecture:** tok2vec + TransitionBasedParser
+- **GPU:** NVIDIA RTX 4090
+- **Training steps:** 11,500 (early stopping)
+- **Patience:** 5,000 steps
+- **Learning rate:** 3e-05
+- **Dropout:** 0.25
+- **Batch size:** 1,000
+- **Train/dev split:** 85/15
+
+## Version History
+
+**v8 (Current):**
+- 94% pass rate (62/66)
+- Multi-entity extraction improved
+- Title case support added
+- AUDIT_TERM edge cases fixed
+
+**v7:**
+- 86% pass rate (57/66)
+- CVE detection restored
+- SECURITY_ROLE improved to 100%
+- IDS/IPS and DDoS fixed
+
+**v6:**
+- 74% pass rate (49/66)
+- CVE regression (missing)
+- AUDIT_TERM and SECURITY_ROLE issues
+
+## Known Limitations
+
+v8 has 4 remaining test failures:
+1. Multi-entity extraction in specific contexts ("APT group using ransomware")
+2. Span boundary issues with conjunctions ("XSS and CSRF mitigated")
+3. Specific "X and Y" patterns ("HIPAA and PCI-DSS standards")
+4. "Gap analysis" edge case
+
+## Use Cases
+
+- CV/resume skill extraction
+- Job description analysis
+- Threat intelligence reports
+- Compliance documentation
+- Security audit reports
+- Technical documentation
+- Security training materials
+
+## License
+
+MIT
+
+## Citation
+
+```bibtex
+@misc{cybersecurity-ner-v8,
+  title={Cybersecurity NER Model v8},
+  author={PKI Research},
+  year={2026},
+  url={https://huggingface.co/your-username/cybersecurity-ner-v8}
+}
+```
+
+## Contact
+
+For issues or questions, please open an issue on GitHub.

config.cfg

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+[paths]
+train = "/home/pki/code/llm-training-repo/ner-cybersecurity/data/train_v8.spacy"
+dev = "/home/pki/code/llm-training-repo/ner-cybersecurity/data/dev_v8.spacy"
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = "pytorch"
+seed = 42
+use_pytorch_for_gpu_memory = true
+
+[nlp]
+lang = "en"
+pipeline = ["tok2vec","ner"]
+batch_size = 1000
+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.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+upstream = "*"
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 96
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
+rows = [5000,2500,2500,2500]
+include_static_vectors = false
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[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 = 0.25
+accumulate_gradient = 3
+patience = 5000
+max_epochs = 0
+max_steps = 100000
+eval_frequency = 500
+frozen_components = []
+annotating_components = []
+before_to_disk = null
+before_update = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+discard_oversize = false
+tolerance = 0.2
+get_length = null
+
+[training.batcher.size]
+@schedules = "compounding.v1"
+start = 100
+stop = 1000
+compound = 1.001
+t = 0.0
+
+[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 = 0.02
+grad_clip = 1.0
+use_averages = false
+eps = 0.00000001
+learn_rate = 0.00003
+
+[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

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+{
+  "lang":"en",
+  "name":"pipeline",
+  "version":"0.0.0",
+  "spacy_version":">=3.8.11,<3.9.0",
+  "description":"",
+  "author":"",
+  "email":"",
+  "url":"",
+  "license":"",
+  "spacy_git_version":"e7a662a",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null,
+    "mode":"default"
+  },
+  "labels":{
+    "tok2vec":[
+
+    ],
+    "ner":[
+      "ACRONYM",
+      "ATTACK_TECHNIQUE",
+      "AUDIT_TERM",
+      "CERTIFICATION",
+      "CVE",
+      "FRAMEWORK",
+      "REGULATION",
+      "SECURITY_DOMAIN",
+      "SECURITY_ROLE",
+      "SECURITY_TOOL",
+      "TECHNICAL_SKILL",
+      "THREAT_TYPE"
+    ]
+  },
+  "pipeline":[
+    "tok2vec",
+    "ner"
+  ],
+  "components":[
+    "tok2vec",
+    "ner"
+  ],
+  "disabled":[
+
+  ],
+  "performance":{
+    "ents_f":0.9858429858,
+    "ents_p":0.9871134021,
+    "ents_r":0.9845758355,
+    "ents_per_type":{
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+        "f":0.9523809524
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+      "CVE":{
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+        "r":1.0,
+        "f":1.0
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+      "AUDIT_TERM":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
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+      "CERTIFICATION":{
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+        "r":0.975,
+        "f":0.9873417722
+      },
+      "SECURITY_ROLE":{
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+        "f":0.9811320755
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+      "ATTACK_TECHNIQUE":{
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+        "r":0.9743589744,
+        "f":0.987012987
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+      "SECURITY_DOMAIN":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
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+      "ACRONYM":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
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+      "TECHNICAL_SKILL":{
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+        "r":1.0,
+        "f":1.0
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+      "REGULATION":{
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+      "FRAMEWORK":{
+        "p":1.0,
+        "r":0.8846153846,
+        "f":0.9387755102
+      },
+      "SECURITY_TOOL":{
+        "p":1.0,
+        "r":1.0,
+        "f":1.0
+      }
+    },
+    "tok2vec_loss":765.8896043144,
+    "ner_loss":1302.3627929688
+  }
+}

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 sha256:28bfe70ec710ca15e40af7123c982e25c1989cac8e4fa00c68bd366c2a792953
+size 138948

ner/moves

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tok2vec/cfg

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+{
+
+}

tok2vec/model

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+oid sha256:813510ec24c5e8b1bf2f3cabc40c5628aaacecfd773462c155a2be63bb44d9cd
+size 6585091

tokenizer

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+  "would",
+  "w\u2019s",
+  "x",
+  "x'",
+  "x'x",
+  "x'xxxx",
+  "x.",
+  "x.X",
+  "x.d",
+  "x.x",
+  "x.x.",
+  "x/x",
+  "xD",
+  "xDD",
+  "xX",
+  "xXX",
+  "x_X",
+  "x_d",
+  "x_x",
+  "xam",
+  "xd",
+  "xdd",
+  "xdr",
+  "xed",
+  "xml",
+  "xss",
+  "xx",
+  "xx'",
+  "xx'x",
+  "xx'xx",
+  "xx.",
+  "xxx",
+  "xxx&xx",
+  "xxx'x",
+  "xxx-dddd",
+  "xxx/xx",
+  "xxxdd",
+  "xxxx",
+  "xxxx'",
+  "xxxx'x",
+  "xxxx+",
+  "xxxxd",
+  "xxxx\u2019",
+  "xxxx\u2019x",
+  "xxx\u2019x",
+  "xx\u2019",
+  "xx\u2019x",
+  "xx\u2019xx",
+  "x\u2019",
+  "x\u2019x",
+  "x\u2019xxxx",
+  "x\ufe35x",
+  "y",
+  "y'",
+  "y's",
+  "y.",
+  "year",
+  "years",
+  "yed",
+  "yesterday",
+  "you",
+  "ype",
+  "yst",
+  "yuk",
+  "y\u2019",
+  "y\u2019s",
+  "z",
+  "z.",
+  "zachman",
+  "zed",
+  "zero",
+  "zuh",
+  "|",
+  "}",
+  "\u00a0",
+  "\u00ac",
+  "\u00ac_\u00ac",
+  "\u00af",
+  "\u00af\\(x)/\u00af",
+  "\u00af\\(\u30c4)/\u00af",
+  "\u00b0",
+  "\u00b0C.",
+  "\u00b0F.",
+  "\u00b0K.",
+  "\u00b0X.",
+  "\u00b0c.",
+  "\u00b0f.",
+  "\u00b0k.",
+  "\u00b0x.",
+  "\u00e4",
+  "\u00e4.",
+  "\u00f6",
+  "\u00f6.",
+  "\u00fc",
+  "\u00fc.",
+  "\u0ca0",
+  "\u0ca0_\u0ca0",
+  "\u0ca0\ufe35\u0ca0",
+  "\u2014",
+  "\u2018",
+  "\u2018S",
+  "\u2018X",
+  "\u2018s",
+  "\u2018x",
+  "\u2019",
+  "\u2019-(",
+  "\u2019-)",
+  "\u2019Cause",
+  "\u2019Cos",
+  "\u2019Coz",
+  "\u2019Cuz",
+  "\u2019S",
+  "\u2019X",
+  "\u2019Xxx",
+  "\u2019Xxxxx",
+  "\u2019am",
+  "\u2019bout",
+  "\u2019cause",
+  "\u2019cos",
+  "\u2019coz",
+  "\u2019cuz",
+  "\u2019d",
+  "\u2019em",
+  "\u2019ll",
+  "\u2019m",
+  "\u2019nuff",
+  "\u2019re",
+  "\u2019s",
+  "\u2019ve",
+  "\u2019x",
+  "\u2019xx",
+  "\u2019xxx",
+  "\u2019xxxx",
+  "\u2019y",
+  "\u2019\u2019",
+  "\u2501",
+  "\u253b",
+  "\u253b\u2501\u253b",
+  "\u256f",
+  "\u25a1",
+  "\ufe35",
+  "\uff09"
+]

vocab/vectors.cfg

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