Duplicate from svercoutere/longformer-classifier-refinement-abb

.gitattributes

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README.md

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+---
+library_name: transformers
+tags:
+  - transformers
+  - pytorch
+  - bert
+  - legal-domain
+  - entity-classification
+  - sequence-classification
+  - NER
+  - longformer
+  - token-classification
+  - label-studio
+  - english
+  - fine-tuned
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+# Legal-BERT Base Entity Classifier
+
+## Overview
+A fine-tuned Longformer-based model for classifying legal entities (such as locations and dates) within the context of legal decision texts.
+The model is based on `allenai/longformer` and is trained to predict the type of a marked entity span, given its context, using special entity markers `[E] ... [/E]`.
+
+## Model Details
+- **Model Name:** longformer-classifier-refinement-abb
+- **Architecture:** Longformer (allenai/longformer)
+- **Task:** Entity Classification (NER-style, entity-in-context classification)
+- **Framework:** PyTorch, Hugging Face Transformers
+- **Author:** S. Vercoutere
+
+## Intended Use
+- **Purpose:** Automatic classification of legal entities (e.g., location, date) in municipal or governmental decision documents.
+- **Not Intended For:** General-purpose NER, non-legal domains, or tasks outside entity classification.
+
+## Training Data
+- **Source:** Annotated legal decision texts from Ghent/Freiburg/Bamberg.
+- **Entity Types:**
+  - Locations: `impact_location`, `context_location`
+  - Dates: `publication_date`, `session_date`, `entry_date`, `expiry_date`, `legal_date`, `context_date`, `validity_period`, `context_period`
+- **Preprocessing:**
+  - XML-like tags in text, with entities wrapped in `<entity_type>...</entity_type>`.
+  - For training, one entity per sample is marked with `[E] ... [/E]` in context.
+  - Dataset balanced to max 5000 samples per label.
+
+## Training Procedure
+- **Model:** `nlpaueb/legal-bert-base-uncased`
+- **Tokenization:** Hugging Face AutoTokenizer, with `[E]` and `[/E]` as additional special tokens.
+- **Max Sequence Length:** 2048 (trained)
+- **Batch Size:** 4
+- **Optimizer:** AdamW
+- **Learning Rate:** 2e-5
+- **Epochs:** 10
+- **Mixed Precision:** Yes (AMP)
+- **Validation Split:** 20%
+- **Evaluation Metrics:** Accuracy, F1, confusion matrix
+
+## Evaluation
+
+**Validation Accuracy:** 0.8454 (on held-out validation set)
+
+**Detailed Entity-Level Evaluation:**
+
+| Entity Label     | Precision | Recall | F1-score   | Support |
+| ---------------- | --------- | ------ | ---------- | ------- |
+| context_date     | 0.9272    | 0.9405 | 0.9338     | 975     |
+| context_location | 0.9671    | 0.9751 | 0.9711     | 843     |
+| context_period   | 0.9744    | 0.8321 | 0.8976     | 137     |
+| entry_date       | 0.9528    | 0.9587 | 0.9557     | 484     |
+| expiry_date      | 0.8980    | 0.9496 | 0.9231     | 139     |
+| impact_location  | 0.9501    | 0.9559 | 0.9530     | 997     |
+| legal_date       | 1.0000    | 0.9926 | 0.9963     | 943     |
+| publication_date | 0.9501    | 0.9870 | 0.9682     | 386     |
+| session_date     | 0.9597    | 0.9597 | 0.9597     | 347     |
+| validity_period  | 0.9932    | 0.9379 | 0.9648     | 467     |
+| **accuracy**     |           |        | **0.9601** | 5718    |
+| **macro avg**    | 0.9572    | 0.9489 | 0.9523     | 5718    |
+| **weighted avg** | 0.9606    | 0.9601 | 0.9601     | 5718    |
+
+
+## Usage Example
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("svercoutere/longformer-classifier-refinement-abb")
+model = AutoModelForSequenceClassification.from_pretrained("svercoutere/longformer-classifier-refinement-abb")
+
+def classify_entity(entity_text, context_text):
+    marked_text = context_text.replace(entity_text, f"[E] {entity_text} [/E]", 1)
+    inputs = tokenizer(marked_text, return_tensors="pt", truncation=True, max_length=2048, padding="max_length")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    pred = torch.argmax(outputs.logits, dim=-1).item()
+    return pred  # Map to label using label_encoder.classes_
+```
+
+## Limitations & Bias
+- The model is trained on legal texts from specific municipalities and may not generalize to other domains or languages.
+- Only entity types present in the training data are supported.
+- The model expects entities to be marked with `[E] ... [/E]` in the input.
+
+## Citation
+If you use this model, please cite:
+
+```
+@misc{longformer-classifier-refinement-abb,
+  author = {S. Vercoutere},
+  title = {Longformer Entity Refinement},
+  year = {2026},
+  howpublished = {\url{https://huggingface.co/svercoutere/longformer-classifier-refinement-abb}}
+}
+```
+

added_tokens.json

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+{
+  "[/E]": 50266,
+  "[E]": 50265
+}

config.json

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+{
+  "architectures": [
+    "LongformerForSequenceClassification"
+  ],
+  "attention_mode": "longformer",
+  "attention_probs_dropout_prob": 0.1,
+  "attention_window": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9"
+  },
+  "ignore_attention_mask": false,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 4098,
+  "model_type": "longformer",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "onnx_export": false,
+  "pad_token_id": 1,
+  "problem_type": "single_label_classification",
+  "sep_token_id": 2,
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 1,
+  "vocab_size": 50267
+}

model.safetensors

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+oid sha256:6cfe293229aee94941ece63f93e9b032d48504bc149cef2d0e66994e80afb260
+size 594708936

special_tokens_map.json

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+{
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+    {
+      "content": "[E]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "[/E]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "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_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "3": {
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "50266": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "[E]",
+    "[/E]"
+  ],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "LongformerTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}