Add FLERT model files and update README with FLERT inference code

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,296 @@
 ---
 license: mit
+language:
+- ar
+- it
+- de
+- en
+- hi
+- fi
+- fr
+- tr
+- es
+- pt
+- pl
+base_model:
+- jhu-clsp/mmBERT-base
+pipeline_tag: token-classification
+tags:
+- ner
+- pii
+- de-identification
+- multilingual
+- modernbert
+- crf
+- flert
 ---
+
+# Multilingual DialogPII NER
+
+A fine-tuned [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) model with a CRF layer for **Personally Identifiable Information (PII) detection** in multilingual dialogues across 11 languages.
+
+## Model Description
+
+This model performs token-level Named Entity Recognition (NER) to identify and classify PII entities in dialogue text. It was trained on synthetic multilingual de-identification of conversational data.
+
+- **Architecture:** mmBERT-base (ModernBERT) + CRF head with FLERT context windowing
+- **Training:** Fine-tuned on all 11 languages jointly (multilingual training) using FLERT-style document context
+- **Loss:** Cross-Entropy
+- **Hyperparameters:** lr=2e-05, batch_size=32, max_length=2048, dropout=0.1, epochs=10
+- **Context window:** 2 sentences left + 2 sentences right, separated by `[SEP]` markers
+- **Decoding:** Viterbi decoding via CRF layer
+
+## Supported Languages
+
+| Code | Language |
+|------|----------|
+| AR | Arabic |
+| DE | German |
+| EN | English |
+| FI | Finnish |
+| FR | French |
+| HI | Hindi |
+| IT | Italian |
+| PL | Polish |
+| PT | Portuguese |
+| SP | Spanish |
+| TR | Turkish |
+
+## Entity Types
+
+The model recognizes 19 PII entity types using BIO tagging:
+
+| Entity | Description |
+|--------|-------------|
+| `PERSON` | Person names |
+| `PERSON_EMAIL` | Email addresses |
+| `PERSON_SOCIAL_RELATION` | Social relations (e.g., "my wife") |
+| `ORG` | Organizations |
+| `LOC_CITY` | Cities |
+| `LOC_COUNTRY` | Countries |
+| `LOC_STREET` | Street names |
+| `LOC_ZIP` | ZIP/postal codes |
+| `LOC_HOUSENUMBER` | House numbers |
+| `LOC_OTHER` | Other locations |
+| `DATETIME` | Dates and times |
+| `DATETIME_AGE` | Ages |
+| `CODE` | ID numbers, reference codes |
+| `CODE_PHONE` | Phone numbers |
+| `CODE_URL` | URLs |
+| `PROFESSION` | Professions |
+| `PRODUCT` | Product names |
+| `QUANTITY` | Quantities |
+| `MISC` | Miscellaneous PII |
+
+## Performance
+
+Evaluated on held-out test sets per language (type-aware micro scores):
+
+| Language | Lenient F1 | Lenient F2 | Exact F1 | Exact F2 |
+|----------|-----------|-----------|---------|---------|
+| AR | 80.76 | 76.66 | 76.99 | 73.08 |
+| DE | 91.66 | 90.71 | 90.54 | 89.60 |
+| EN | 93.68 | 92.70 | 91.66 | 90.70 |
+| FI | 87.70 | 86.77 | 85.65 | 84.73 |
+| FR | 87.26 | 85.89 | 83.68 | 82.36 |
+| HI | 84.94 | 82.91 | 81.26 | 79.31 |
+| IT | 90.03 | 88.19 | 87.14 | 85.35 |
+| PL | 89.33 | 89.45 | 86.17 | 86.29 |
+| PT | 90.30 | 89.15 | 88.81 | 87.68 |
+| SP | 91.39 | 90.76 | 89.62 | 89.00 |
+| TR | 85.53 | 84.72 | 82.06 | 81.27 |
+| **AVG** | **88.42** | **87.08** | **85.78** | **84.49** |
+
+## Usage
+
+This model uses a custom CRF architecture with FLERT-style context windowing and cannot be loaded directly with `AutoModelForTokenClassification`. You need to use the custom `ModernBertCRF` class.
+
+### Setup
+
+```python
+import torch
+import json
+import re
+import torch.nn as nn
+import spacy
+from transformers import AutoModel, AutoTokenizer
+from torchcrf import CRF
+from huggingface_hub import snapshot_download
+
+class ModernBertCRF(nn.Module):
+    def __init__(self, base_model_name, num_labels, id2label, label2id):
+        super().__init__()
+        self.num_labels = num_labels
+        self.id2label = id2label
+        self.label2id = label2id
+        self.transformer = AutoModel.from_pretrained(base_model_name)
+        hidden_size = self.transformer.config.hidden_size
+        self.classifier = nn.Linear(hidden_size, num_labels)
+        self.dropout = nn.Dropout(0.1)
+        self.crf = CRF(num_labels, batch_first=True)
+
+    def forward(self, input_ids, attention_mask, labels=None, **kwargs):
+        kwargs.pop("token_type_ids", None)
+        outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)
+        sequence_output = self.dropout(outputs.last_hidden_state)
+        emissions = self.classifier(sequence_output)
+        return {"logits": emissions}
+
+    def decode(self, emissions, mask):
+        return self.crf.decode(emissions, mask=mask)
+
+# Load model
+model_dir = snapshot_download("DFKI-SLT/multilingual_DialogPII_NER")
+
+with open(f"{model_dir}/crf_config.json") as f:
+    config = json.load(f)
+
+with open(f"{model_dir}/flert_config.json") as f:
+    flert_config = json.load(f)
+
+model = ModernBertCRF(
+    base_model_name=config["base_model_name"],
+    num_labels=config["num_labels"],
+    id2label=config["id2label"],
+    label2id=config["label2id"],
+)
+model.load_state_dict(torch.load(f"{model_dir}/pytorch_model.bin", map_location="cpu"))
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+id2label = {int(k): v for k, v in config["id2label"].items()}
+
+context_window = flert_config["context_window"]     # 2
+use_sep_marker = flert_config["context_sep_marker"]  # True
+```
+
+### Preprocessing: Sentence Splitting
+
+The model was trained using FLERT-style context windowing over **sentence-level** input. Each sentence is predicted with surrounding context sentences. For best results, split your input into sentences using [spaCy](https://spacy.io/) before inference.
+
+```python
+nlp = spacy.blank("en")          # use "de" for German, "xx" for multilingual
+nlp.add_pipe("sentencizer")
+
+def split_dialogue(text, nlp):
+    sentences = []
+    for line in text.strip().splitlines():
+        m = re.match(r"^(SPEAKER_\d+)\s*:\s*(.*)", line.strip())
+        if m:
+            speaker, rest = m.group(1), m.group(2)
+            sentences.append([speaker, ":"])
+            line = rest
+        if not line:
+            continue
+        doc = nlp(line)
+        for sent in doc.sents:
+            tokens = [tok.text for tok in sent if not tok.is_space]
+            if tokens:
+                sentences.append(tokens)
+    return sentences
+
+# Example
+raw = """SPEAKER_00: Hello, my name is Peter.
+SPEAKER_01: Hello, my name is Peter as well. Okay, and where do you come from? I come from Chicago."""
+
+sentences = split_dialogue(raw, nlp)
+```
+
+### Inference with FLERT Context Windowing
+
+The key difference from standard token classification: each sentence is predicted within a window of surrounding context sentences, joined by `[SEP]` tokens. Only labels for the target sentence are extracted.
+
+```python
+def predict_dialogue(sentences, model, tokenizer, id2label,
+                     context_window=2, use_sep_marker=True, device="cpu"):
+    sep = tokenizer.sep_token
+    all_labels = []
+    for i, target_tokens in enumerate(sentences):
+        left  = sentences[max(0, i - context_window):i]
+        right = sentences[i + 1:i + 1 + context_window]
+
+        flat_tokens = []
+        for s in left:
+            flat_tokens.extend(s)
+        if use_sep_marker and left:
+            flat_tokens.append(sep)
+
+        tgt_start = len(flat_tokens)
+        flat_tokens.extend(target_tokens)
+        tgt_end = len(flat_tokens)
+
+        if use_sep_marker and right:
+            flat_tokens.append(sep)
+        for s in right:
+            flat_tokens.extend(s)
+
+        enc = tokenizer(flat_tokens, is_split_into_words=True,
+                        return_tensors="pt", truncation=False).to(device)
+        word_ids = enc.word_ids(batch_index=0)
+
+        with torch.no_grad():
+            emissions = model(**enc)["logits"]
+            mask = enc["attention_mask"].bool()
+            preds = model.decode(emissions, mask)[0]
+
+        word_labels = ["O"] * len(target_tokens)
+        seen = set()
+        for idx, wid in enumerate(word_ids):
+            if wid is None or wid in seen:
+                continue
+            seen.add(wid)
+            if tgt_start <= wid < tgt_end:
+                word_labels[wid - tgt_start] = id2label[preds[idx]]
+
+        all_labels.append(word_labels)
+    return all_labels
+
+
+# Run prediction
+results = predict_dialogue(sentences, model, tokenizer, id2label,
+                           context_window=context_window,
+                           use_sep_marker=use_sep_marker)
+
+for sent_tokens, sent_labels in zip(sentences, results):
+    for token, label in zip(sent_tokens, sent_labels):
+        if label != "O":
+            print(f"{token:20s} -> {label}")
+```
+
+### Single-sentence inference
+
+For isolated sentences without dialogue context, pass them with `context_window=0`:
+
+```python
+tokens = ["My", "name", "is", "John", "Smith", "and", "I", "live", "in", "Berlin", "."]
+
+results = predict_dialogue([tokens], model, tokenizer, id2label,
+                           context_window=0, use_sep_marker=False)
+
+for token, label in zip(tokens, results[0]):
+    if label != "O":
+        print(f"{token:20s} -> {label}")
+```
+
+## Training Data
+
+The model was trained on synthetic multilingual dialogue data covering various domains (medical anamnesis, customer support, police reports, therapy sessions, etc.). The data was generated and annotated as part of a thesis project on multilingual PII de-identification.
+
+## Limitations
+
+- Trained on synthetic dialogue data; performance on real-world data may vary
+- Optimized for dialogue/conversational text; may underperform on formal documents
+- Arabic and Hindi show lower performance compared to European languages
+- Requires `pytorch-crf` package for inference
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@mastersthesis{erman2026multilingual,
+  title={Multilingual De-Identification of Dialogue Data using Transformer-based NER},
+  author={Erman, Derya},
+  year={2026}
+}
+```

crf_config.json

@@ -0,0 +1,105 @@
+{
+  "base_model_name": "jhu-clsp/mmBERT-base",
+  "num_labels": 48,
+  "id2label": {
+    "0": "O",
+    "1": "B-CITY",
+    "2": "B-CODE",
+    "3": "B-CODE_PHONE",
+    "4": "B-CODE_URL",
+    "5": "B-DATETIME",
+    "6": "B-DATETIME_AGE",
+    "7": "B-DISEASE",
+    "8": "B-LOC_CITY",
+    "9": "B-LOC_COUNTRY",
+    "10": "B-LOC_DISTRICT",
+    "11": "B-LOC_HOUSENUMBER",
+    "12": "B-LOC_NAME",
+    "13": "B-LOC_OTHER",
+    "14": "B-LOC_STREET",
+    "15": "B-LOC_ZIP",
+    "16": "B-MISC",
+    "17": "B-NAME",
+    "18": "B-ORG",
+    "19": "B-PERSON",
+    "20": "B-PERSON_EMAIL",
+    "21": "B-PERSON_SOCIAL_RELATION",
+    "22": "B-PRODUCT",
+    "23": "B-PROFESSION",
+    "24": "B-QUANTITY",
+    "25": "I-CITY",
+    "26": "I-CODE",
+    "27": "I-CODE_PHONE",
+    "28": "I-DATETIME",
+    "29": "I-DATETIME_AGE",
+    "30": "I-DISEASE",
+    "31": "I-LOC_CITY",
+    "32": "I-LOC_COUNTRY",
+    "33": "I-LOC_DISTRICT",
+    "34": "I-LOC_HOUSENUMBER",
+    "35": "I-LOC_NAME",
+    "36": "I-LOC_OTHER",
+    "37": "I-LOC_STREET",
+    "38": "I-LOC_ZIP",
+    "39": "I-MISC",
+    "40": "I-NAME",
+    "41": "I-ORG",
+    "42": "I-PERSON",
+    "43": "I-PERSON_EMAIL",
+    "44": "I-PERSON_SOCIAL_RELATION",
+    "45": "I-PRODUCT",
+    "46": "I-PROFESSION",
+    "47": "I-QUANTITY"
+  },
+  "label2id": {
+    "O": 0,
+    "B-CITY": 1,
+    "B-CODE": 2,
+    "B-CODE_PHONE": 3,
+    "B-CODE_URL": 4,
+    "B-DATETIME": 5,
+    "B-DATETIME_AGE": 6,
+    "B-DISEASE": 7,
+    "B-LOC_CITY": 8,
+    "B-LOC_COUNTRY": 9,
+    "B-LOC_DISTRICT": 10,
+    "B-LOC_HOUSENUMBER": 11,
+    "B-LOC_NAME": 12,
+    "B-LOC_OTHER": 13,
+    "B-LOC_STREET": 14,
+    "B-LOC_ZIP": 15,
+    "B-MISC": 16,
+    "B-NAME": 17,
+    "B-ORG": 18,
+    "B-PERSON": 19,
+    "B-PERSON_EMAIL": 20,
+    "B-PERSON_SOCIAL_RELATION": 21,
+    "B-PRODUCT": 22,
+    "B-PROFESSION": 23,
+    "B-QUANTITY": 24,
+    "I-CITY": 25,
+    "I-CODE": 26,
+    "I-CODE_PHONE": 27,
+    "I-DATETIME": 28,
+    "I-DATETIME_AGE": 29,
+    "I-DISEASE": 30,
+    "I-LOC_CITY": 31,
+    "I-LOC_COUNTRY": 32,
+    "I-LOC_DISTRICT": 33,
+    "I-LOC_HOUSENUMBER": 34,
+    "I-LOC_NAME": 35,
+    "I-LOC_OTHER": 36,
+    "I-LOC_STREET": 37,
+    "I-LOC_ZIP": 38,
+    "I-MISC": 39,
+    "I-NAME": 40,
+    "I-ORG": 41,
+    "I-PERSON": 42,
+    "I-PERSON_EMAIL": 43,
+    "I-PERSON_SOCIAL_RELATION": 44,
+    "I-PRODUCT": 45,
+    "I-PROFESSION": 46,
+    "I-QUANTITY": 47
+  },
+  "use_crf": true
+}

flert_config.json

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+  "use_flert": true,
+  "context_window": 2,
+  "context_sep_marker": true,
+  "context_left_only": false
+}

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