Add NERGAL epoch-5 hybrid PII snapshot

.gitattributes

@@ -33,3 +33,6 @@ 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
+figures/primary-three-model-curves.png filter=lfs diff=lfs merge=lfs -text
+figures/xlmr-seven-epoch-curves.png filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+language:
+- pl
+base_model: FacebookAI/xlm-roberta-large
+library_name: transformers
+pipeline_tag: token-classification
+tags:
+- polish
+- pii
+- ner
+- xlm-roberta
+- hybrid
+---
+
+# NERGAL
+
+**Named Entity Recognition with Grounded Additive Labels**
+
+SlayerLab hybrid PII cleaner for Polish. Not a chat model. Not a drop-in `pipeline("token-classification")`.
+
+## TL;DR
+
+Python rules do the identifiers they can prove. A transformer NER head adds phone and other PII spans the regex misses. The cleaner **unions** the two on the original text, then replaces hits with `[Telefon]` or `[PII]`.
+
+- **Ground:** frozen `scrub_pii` regex
+- **Additive labels:** XLM-RoBERTa-large token classifier, BIO tags `phone` / `pii`, threshold 0.95
+- **This snapshot:** seed `202609160`, **epoch 5** of a seven-epoch schedule
+
+## 841-dev
+
+Tables use one development split: 841 passages, 215 with gold PII, **354 spans** (169 phone, 185 other PII). It is the `dev` side of a 4,500-passage labelled tranche (3,655 train / 841 dev). Sources match Dynaword (EUR-Lex, HPLT, Wikipedia, parliamentary and government text, plus smaller news/literary slices). Labels mix unchanged silver with human review.
+
+The files contain real identifiers, so they are not released with the weights.
+
+## Why XLM-R
+
+GLiNER, HerBERT-large, and XLM-R-large were trained on the same split and unioned with the same regex. Plot: diagnostic threshold 0.50; selection used the full threshold grid. GLiNER covers more at 0.50 and then dumps precision. XLM-R is the architecture we kept.
+
+![Primary three-model curves](figures/primary-three-model-curves.png)
+
+## Why epoch 5
+
+Fresh XLM-R, seven epochs. 133 epoch/threshold combinations. Epoch 5 at 0.95 was the only point that both beat the historical GLiNER∪regex incumbent on coverage and introduced zero new false-mask characters. Epoch 7 covers more PII (334/354) but adds 15 new false characters.
+
+![Seven-epoch XLM-R curves](figures/xlmr-seven-epoch-curves.png)
+
+| Epoch | Covered @ 0.95 /354 | Residual passages | False characters | New false vs incumbent |
+|---|---:|---:|---:|---:|
+| 1 | 272 | 59 | 175 | 42 |
+| 2 | 291 | 49 | 141 | 8 |
+| 3 | 317 | 30 | 140 | 7 |
+| 4 | 320 | 28 | 143 | 10 |
+| **5** | **323** | **25** | **133** | **0** |
+| 6 | 328 | 20 | 147 | 14 |
+| 7 | 334 | 16 | 148 | 15 |
+
+## 841-dev scores
+
+Union with unchanged rules. Character scores are on gold vs masked characters.
+
+| System | Whole spans /354 | Residual passages | False chars | Char precision | Char recall |
+|---|---:|---:|---:|---:|---:|
+| Regex only | 245 | 73 | 133 | 97.36% | 81.01% |
+| Historical GLiNER email12 ∪ regex | 289 | 51 | 143 | 97.54% | 93.48% |
+| **NERGAL (this snapshot ∪ regex)** | **323** | **25** | **133** | **97.76%** | **95.95%** |
+
+144/169 phone, 179/185 other PII. Exact-span precision 87.50%, recall 88.98%, F1 88.24%.
+
+## Extra seeds
+
+| Seed | Whole /354 | False chars | New false vs historical union |
+|---|---:|---:|---:|
+| 202609160 (this repo) | 323 | 133 | 0 |
+| 202609161 | 322 | 134 | 1 |
+| 202609162 | 316 | 151 | 18 |
+
+## Load
+
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("SlayerLab/NERGAL")
+model = AutoModelForTokenClassification.from_pretrained("SlayerLab/NERGAL")
+```
+
+Reproducing the table needs the windowed BIO decoder and regex union (`hybrid.json`: threshold 0.95, gap ids `250002`/`250003`). Vanilla token classification will not match.
+
+Base weights: [`FacebookAI/xlm-roberta-large`](https://huggingface.co/FacebookAI/xlm-roberta-large) revision `c23d21b0620b635a76227c604d44e43a9f0ee389` (MIT).

config.json

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+{
+  "architectures": [
+    "XLMRobertaForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "O",
+    "1": "B-phone",
+    "2": "I-phone",
+    "3": "B-pii",
+    "4": "I-pii"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "B-phone": 1,
+    "B-pii": 3,
+    "I-phone": 2,
+    "I-pii": 4,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.6",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250004
+}

hybrid.json

@@ -0,0 +1,24 @@
+{
+  "full_name": "Named Entity Recognition with Grounded Additive Labels",
+  "hub_id": "SlayerLab/NERGAL",
+  "mode": "rules_union",
+  "epoch": 5,
+  "seed": 202609160,
+  "threshold": 0.95,
+  "rules_sha256": "547c0428b0799bf051566d6ac489987eff27f09e1bda36a5665452fe155b3966",
+  "weights_sha256": "063ee5f9782c1b4d99e838be5a836328718e1372e213d5b87098b371b5c162af",
+  "gaps": [
+    "[PII_SPACE]",
+    "[PII_BREAK]"
+  ],
+  "gap_ids": [
+    250002,
+    250003
+  ],
+  "drop_in_token_classification_pipeline": false,
+  "promotion_authorized": false,
+  "backbone": {
+    "repo": "FacebookAI/xlm-roberta-large",
+    "revision": "c23d21b0620b635a76227c604d44e43a9f0ee389"
+  }
+}

model.safetensors

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sentencepiece.bpe.model

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special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.json

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tokenizer_config.json

@@ -0,0 +1,71 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250002": {
+      "content": "[PII_SPACE]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250003": {
+      "content": "[PII_BREAK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}