Finalize TypePredictor model card and release artifacts

README.md

@@ -9,6 +9,7 @@ tags:
 - entity-typing
 - wojood
 - neoarabert
+- mention-classification
 datasets:
 - U4RASD/TypePrediction
 metrics:
@@ -18,31 +19,59 @@ metrics:
 
 # TypePredictor
 
-`TypePredictor` is a mention-level Arabic entity-type classifier trained on
-`U4RASD/TypePrediction`. Given an Arabic sentence and a known character span, the
-span is wrapped with generic `[ENT]` and `[/ENT]` markers and classified into one
-of 21 coarse Wojood entity types.
+`TypePredictor` is a mention-level Arabic entity type classifier. It assumes the
+entity span is already known, inserts `[ENT]` and `[/ENT]` around that span, and
+predicts one of 21 Wojood-style entity types.
 
-> This README is the automatically generated preliminary model card. The RunPod
-> package instructs Codex to wait for Ahmad's explicit confirmation that the full
-> training, validation, and test workflow is complete before writing and pushing
-> the final polished model card from all run artifacts.
+This model is intended as a type-normalization component for a later
+relation-extraction pipeline, where relation subjects and objects are already
+available as spans and need a consistent coarse entity type.
+
+## Key result
+
+The repository root contains the best checkpoint selected by overall validation
+macro F1. The best checkpoint was step `25,000` / epoch
+`3.968292`.
+
+| Split | Rows | Accuracy | Micro F1 | Macro F1 present types | Macro F1 all 21 | Weighted F1 |
+|---|---:|---:|---:|---:|---:|---:|
+| Validation overall | 12,600 | 0.977857 | 0.977857 | 0.958728 | 0.958728 | 0.977791 |
+| Test overall | 12,600 | 0.979365 | 0.979365 | 0.959920 | 0.959920 | 0.979297 |
+
+## What this model does and does not do
+
+- It classifies a supplied mention/span.
+- It does not detect entity boundaries.
+- It does not include a `NONE` class because the training, validation, and test
+  splits do not contain `NONE` examples.
+- It uses one shared classifier for all mentions; there is no separate subject
+  or object head.
 
 ## Architecture
 
 ```text
-sentence + known span
--> insert [ENT] ... [/ENT]
--> U4RASD/NeoAraBERT
--> CLS vector [768]
--> Dropout(0.1)
+Arabic sentence + known character span
+-> insert [ENT] and [/ENT] around the exact span
+-> tokenize with U4RASD/NeoAraBERT tokenizer
+-> U4RASD/NeoAraBERT encoder
+-> final hidden state at CLS position
+-> Dropout(0.10)
 -> Linear(768, 21)
 -> argmax entity type
 ```
 
-The baseline uses ordinary unweighted multiclass cross-entropy. There is no
-class weighting, weighted sampler, extra MLP, span pooling, threshold, or
-subject/object-specific model.
+Architecture details:
+
+- Base encoder: `U4RASD/NeoAraBERT`
+- Hidden size: `768`
+- Tokenizer size after markers: `65,002`
+- Classifier shape: `768 -> 21`
+- Total parameters: `248,162,325`
+- Encoder parameters: `248,146,176`
+- Classifier parameters: `16,149`
+- Loss: ordinary unweighted multiclass cross-entropy
+- No class weights, focal loss, oversampling, weighted sampler, span pooling,
+  threshold, extra MLP, or two-head design.
 
 ## Labels
 
@@ -50,64 +79,248 @@ GPE, ORG, DATE, PERS, NORP, ORDINAL, OCC, EVENT, CARDINAL, LOC, WEBSITE, FAC, LA
 
 ## Dataset
 
-- Train: 100,796
-- Validation: 12,600
-- Test: 12,600
-- Validation seen-sentence/new-entity: 11,661
-- Validation unseen-sentence: 939
-- Test seen-sentence/new-entity: 11,606
-- Test unseen-sentence: 994
+- Dataset repository: `U4RASD/TypePrediction`
+- Train file: `type_predictor_train.jsonl`
+- Validation file: `type_predictor_val.jsonl`
+- Test file: `type_predictor_test.jsonl`
+- Train rows: `100,796`
+- Validation rows: `12,600`
+- Test rows: `12,600`
+- Dataset validation timestamp: `2026-07-12T15:30:12+00:00`
+- IDs unique across splits: `True`
 
-The split is mention-level and intentionally allows the same sentence to appear
-across splits with different target mentions. Category-specific results should
-therefore be interpreted separately.
+The split is mention-level. A sentence can appear in more than one split with a
+different target mention. For that reason, the model card reports both:
 
-## Validation results
+- `seen_sentence_new_entity`: the sentence text was seen in training, but the
+  evaluated target mention is new.
+- `unseen_sentence`: the sentence text was not seen in training.
 
-| Subset | Rows | Accuracy | Micro F1 | Macro F1 (present types) | Weighted F1 |
-|---|---:|---:|---:|---:|---:|
-| overall | 12600 | 0.977857 | 0.977857 | 0.958728 | 0.977791 |
-| unseen_sentence | 939 | 0.945687 | 0.945687 | 0.908841 | 0.945011 |
-| seen_sentence_new_entity | 11661 | 0.980448 | 0.980448 | 0.963262 | 0.980403 |
+## Preprocessing and encoding
 
-## Test results
+- Markers: `[ENT]` and `[/ENT]`
+- Marker insertion is based on exact character offsets, not string replacement.
+- Maximum sequence length: `512`
+- Default context window: `300` characters
+- Fallback context candidates: `None, 500, 300, 150, 80, 30, 0`
+- The encoder rejects examples where truncation fails to preserve exactly one
+  opening marker and one closing marker in the correct order.
+
+## Training configuration
+
+| Setting | Value |
+|---|---:|
+| Seed | `42` |
+| Epochs | `4.0` |
+| Train batch size/device | `4` |
+| Eval batch size/device | `8` |
+| Gradient accumulation | `4` |
+| Effective batch size | `16` |
+| Encoder learning rate | `1e-05` |
+| Classifier learning rate | `5e-05` |
+| Weight decay | `0.01` |
+| Warmup ratio | `0.1` |
+| Max grad norm | `1.0` |
+| Dropout | `0.1` |
+| Logging steps | `50` |
+| Eval steps | `500` |
+| Save steps | `500` |
+| FP16 | `True` |
+| Best model criterion | overall validation macro F1 |
+
+Training runtime:
+
+- Started: `2026-07-12T15:30:38+00:00`
+- Finished: `2026-07-12T18:55:01+00:00`
+- Wall time: `12262.47` seconds (`3.41` hours)
+- Train runtime reported by Trainer: `12261.72` seconds
+- Train samples/sec: `32.882`
+- Train steps/sec: `2.055`
+- Final train loss: `0.190693`
 
-| Subset | Rows | Accuracy | Micro F1 | Macro F1 (present types) | Weighted F1 |
-|---|---:|---:|---:|---:|---:|
-| overall | 12600 | 0.979365 | 0.979365 | 0.959920 | 0.979297 |
-| unseen_sentence | 994 | 0.962777 | 0.962777 | 0.893174 | 0.961780 |
-| seen_sentence_new_entity | 11606 | 0.980786 | 0.980786 | 0.964113 | 0.980712 |
+Hardware/runtime:
 
-Detailed per-class reports, confusion matrices, predictions, configuration, and
-checkpoint metadata are included in the repository artifacts.
+- GPU: `NVIDIA A40`
+- GPU VRAM: `44.43 GiB`
+- PyTorch: `2.5.1+cu124`
+- Transformers: `4.49.0`
+- Python: `3.12.3`
 
 ## Checkpoints
 
-- Best checkpoint: epoch `3.9682923925552602`, step `25000`
+- Best checkpoint source: `outputs/TypePredictor/checkpoints/checkpoint-25000`
+- Preserved best checkpoint: `outputs/TypePredictor/best_checkpoint`
+- Best step: `25,000`
+- Best epoch: `3.968292`
 - Best validation macro F1: `0.958728`
-- Latest checkpoint: epoch `3.9994047382832654`, step `25196`
-- Repository root: best model
-- `checkpoints/latest/`: latest completed checkpoint snapshot
+- Latest completed checkpoint source: `outputs/TypePredictor/checkpoints/checkpoint-25196`
+- Preserved latest checkpoint: `outputs/TypePredictor/latest_checkpoint`
+- Latest step: `25,196`
+- Latest epoch: `3.999405`
+- Repository root released model: `best checkpoint`
+- `checkpoints/latest/` contains the latest completed checkpoint snapshot.
 
-Both the best and latest full checkpoints are also preserved in the local RunPod
-output directory.
+## Validation results
 
-## Training configuration
+| Subset | Rows | Accuracy | Micro F1 | Macro F1 present types | Macro F1 all 21 | Weighted F1 |
+|---|---:|---:|---:|---:|---:|---:|
+| overall | 12,600 | 0.977857 | 0.977857 | 0.958728 | 0.958728 | 0.977791 |
+| unseen_sentence | 939 | 0.945687 | 0.945687 | 0.908841 | 0.865563 | 0.945011 |
+| seen_sentence_new_entity | 11,661 | 0.980448 | 0.980448 | 0.963262 | 0.963262 | 0.980403 |
+
+## Test results
+
+| Subset | Rows | Accuracy | Micro F1 | Macro F1 present types | Macro F1 all 21 | Weighted F1 |
+|---|---:|---:|---:|---:|---:|---:|
+| overall | 12,600 | 0.979365 | 0.979365 | 0.959920 | 0.959920 | 0.979297 |
+| unseen_sentence | 994 | 0.962777 | 0.962777 | 0.893174 | 0.850642 | 0.961780 |
+| seen_sentence_new_entity | 11,606 | 0.980786 | 0.980786 | 0.964113 | 0.964113 | 0.980712 |
+
+## Per-class validation results
+
+### Validation overall
+
+| Label | Precision | Recall | F1 | Support |
+|---|---:|---:|---:|---:|
+| GPE | 0.977432 | 0.982156 | 0.979789 | 2,690 |
+| ORG | 0.972832 | 0.982707 | 0.977745 | 2,660 |
+| DATE | 0.993431 | 0.994939 | 0.994185 | 1,976 |
+| PERS | 0.973832 | 0.972015 | 0.972923 | 1,072 |
+| NORP | 0.975050 | 0.955969 | 0.965415 | 1,022 |
+| ORDINAL | 0.979513 | 0.980769 | 0.980141 | 780 |
+| OCC | 0.981889 | 0.986996 | 0.984436 | 769 |
+| EVENT | 0.967302 | 0.941645 | 0.954301 | 377 |
+| CARDINAL | 0.973333 | 0.978552 | 0.975936 | 373 |
+| LOC | 0.974576 | 0.962343 | 0.968421 | 239 |
+| WEBSITE | 0.986577 | 0.993243 | 0.989899 | 148 |
+| FAC | 0.916667 | 0.930769 | 0.923664 | 130 |
+| LAW | 1.000000 | 1.000000 | 1.000000 | 90 |
+| TIME | 0.974359 | 0.873563 | 0.921212 | 87 |
+| MONEY | 0.933333 | 1.000000 | 0.965517 | 42 |
+| CURR | 1.000000 | 0.951220 | 0.975000 | 41 |
+| LANGUAGE | 0.933333 | 0.848485 | 0.888889 | 33 |
+| PERCENT | 1.000000 | 0.903226 | 0.949153 | 31 |
+| PRODUCT | 1.000000 | 0.842105 | 0.914286 | 19 |
+| QUANTITY | 1.000000 | 0.818182 | 0.900000 | 11 |
+| UNIT | 0.909091 | 1.000000 | 0.952381 | 10 |
+
+## Per-class test results
+
+### Test overall
+
+| Label | Precision | Recall | F1 | Support |
+|---|---:|---:|---:|---:|
+| GPE | 0.973897 | 0.984392 | 0.979117 | 2,691 |
+| ORG | 0.980769 | 0.978187 | 0.979477 | 2,659 |
+| DATE | 0.990438 | 0.995448 | 0.992936 | 1,977 |
+| PERS | 0.979535 | 0.983193 | 0.981361 | 1,071 |
+| NORP | 0.976540 | 0.978452 | 0.977495 | 1,021 |
+| ORDINAL | 0.982097 | 0.983355 | 0.982726 | 781 |
+| OCC | 0.987047 | 0.989610 | 0.988327 | 770 |
+| EVENT | 0.986413 | 0.962865 | 0.974497 | 377 |
+| CARDINAL | 0.977901 | 0.951613 | 0.964578 | 372 |
+| LOC | 0.969565 | 0.933054 | 0.950959 | 239 |
+| WEBSITE | 0.966887 | 0.986486 | 0.976589 | 148 |
+| FAC | 0.928000 | 0.899225 | 0.913386 | 129 |
+| LAW | 1.000000 | 1.000000 | 1.000000 | 91 |
+| TIME | 0.915663 | 0.873563 | 0.894118 | 87 |
+| MONEY | 0.973684 | 0.880952 | 0.925000 | 42 |
+| CURR | 0.975610 | 0.975610 | 0.975610 | 41 |
+| LANGUAGE | 0.875000 | 0.848485 | 0.861538 | 33 |
+| PERCENT | 0.964286 | 0.870968 | 0.915254 | 31 |
+| PRODUCT | 1.000000 | 0.947368 | 0.972973 | 19 |
+| QUANTITY | 0.909091 | 1.000000 | 0.952381 | 10 |
+| UNIT | 1.000000 | 1.000000 | 1.000000 | 11 |
 
-- Encoder learning rate: `1e-05`
-- Classifier learning rate: `5e-05`
-- Epochs: `4.0`
-- Train batch size: `4`
-- Gradient accumulation: `4`
-- Effective batch size: `16`
-- Maximum length: `512`
-- FP16: `True`
-- Best-checkpoint criterion: validation macro F1
+### Test unseen-sentence per-class results
+
+This subset is the strictest split because the full sentence is unseen during
+training. Some rare labels have very small support here, so their F1 scores are
+high variance.
+
+### Test unseen_sentence
+
+| Label | Precision | Recall | F1 | Support |
+|---|---:|---:|---:|---:|
+| GPE | 0.953333 | 0.934641 | 0.943894 | 153 |
+| ORG | 0.964029 | 0.964029 | 0.964029 | 139 |
+| DATE | 0.959677 | 0.991667 | 0.975410 | 120 |
+| PERS | 0.978873 | 0.972028 | 0.975439 | 143 |
+| NORP | 0.960317 | 0.968000 | 0.964143 | 125 |
+| ORDINAL | 0.976190 | 1.000000 | 0.987952 | 82 |
+| OCC | 1.000000 | 0.960000 | 0.979592 | 50 |
+| EVENT | 1.000000 | 0.928571 | 0.962963 | 14 |
+| CARDINAL | 0.979167 | 0.959184 | 0.969072 | 49 |
+| LOC | 0.933333 | 0.933333 | 0.933333 | 15 |
+| WEBSITE | 0.945455 | 1.000000 | 0.971963 | 52 |
+| FAC | 0.888889 | 1.000000 | 0.941176 | 8 |
+| LAW | 1.000000 | 1.000000 | 1.000000 | 8 |
+| TIME | 0.900000 | 0.947368 | 0.923077 | 19 |
+| MONEY | 0.000000 | 0.000000 | 0.000000 | 1 |
+| CURR | 1.000000 | 0.666667 | 0.800000 | 3 |
+| LANGUAGE | 0.666667 | 0.500000 | 0.571429 | 8 |
+| PERCENT | 1.000000 | 1.000000 | 1.000000 | 2 |
+| PRODUCT | 1.000000 | 1.000000 | 1.000000 | 2 |
+| QUANTITY | 1.000000 | 1.000000 | 1.000000 | 1 |
+| UNIT | 0.000000 | 0.000000 | 0.000000 | 0 |
+
+## Confusion-matrix observations
+
+The strongest remaining confusions are mostly between semantically adjacent
+coarse types or rare labels with limited support.
+
+### Top test-overall confusions
+
+| Gold label | Predicted label | Count |
+|---|---|---:|
+| ORG | GPE | 36 |
+| GPE | ORG | 21 |
+| LOC | GPE | 11 |
+| FAC | GPE | 9 |
+| EVENT | ORG | 8 |
+| CARDINAL | ORDINAL | 8 |
+| NORP | ORG | 7 |
+| GPE | NORP | 6 |
+| ORG | NORP | 6 |
+| ORG | PERS | 5 |
+| NORP | PERS | 5 |
+| ORDINAL | ORG | 5 |
+
+### Top test-unseen-sentence confusions
+
+| Gold label | Predicted label | Count |
+|---|---|---:|
+| GPE | ORG | 3 |
+| ORG | GPE | 3 |
+| GPE | WEBSITE | 2 |
+| PERS | GPE | 2 |
+| CARDINAL | ORDINAL | 2 |
+| LANGUAGE | NORP | 2 |
+| GPE | DATE | 1 |
+| GPE | PERS | 1 |
+| GPE | NORP | 1 |
+| GPE | CARDINAL | 1 |
+| GPE | LOC | 1 |
+| ORG | WEBSITE | 1 |
+
+## Artifacts included in this repository
+
+- `pytorch_model.bin`: best checkpoint model weights
+- `config.json`, `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`
+- `type_predictor_config.json`: task-specific architecture and label config
+- `modeling_type_predictor.py`: custom PyTorch model wrapper
+- `inference.py`: local inference example
+- `metrics/`: validation/test overall, category-specific, and per-class metrics
+- `confusion_matrices/`: validation/test confusion matrices
+- `predictions/`: row-level validation and test predictions
+- `configs/`: architecture, labels, run config, and training arguments
+- `checkpoint_summary.json`, `run_summary.json`, `evaluation_results.json`
+- `checkpoints/latest/`: latest completed checkpoint snapshot
 
 ## Loading
 
-The repository contains `modeling_type_predictor.py` because this classifier is
-a small custom PyTorch wrapper around NeoAraBERT.
+Because this is a small custom wrapper around NeoAraBERT, load the model through
+the included `modeling_type_predictor.py`.
 
 ```python
 from huggingface_hub import hf_hub_download
@@ -117,20 +330,61 @@ source = hf_hub_download("U4RASD/TypePredictor", "modeling_type_predictor.py")
 spec = importlib.util.spec_from_file_location("modeling_type_predictor", source)
 module = importlib.util.module_from_spec(spec)
 spec.loader.exec_module(module)
+
 model, tokenizer, config = module.NeoAraBERTTypePredictor.from_pretrained(
     "U4RASD/TypePredictor"
 )
+model.eval()
 ```
 
-## Intended use
+## Inference example
+
+```python
+import torch
+
+sentence = "زار أحمد القاهرة أمس."
+entity = "القاهرة"
+start = sentence.index(entity)
+end = start + len(entity)
+
+marked = sentence[:start] + " [ENT] " + sentence[start:end] + " [/ENT] " + sentence[end:]
+batch = tokenizer(
+    marked,
+    return_tensors="pt",
+    truncation=True,
+    max_length=config["max_length"],
+)
+
+with torch.no_grad():
+    logits = model(**batch)["logits"]
+    probs = torch.softmax(logits, dim=-1)[0]
+
+label_id = int(probs.argmax())
+print(config["labels"][label_id], float(probs[label_id]))
+```
 
-Use the model when the target entity mention and its character span are already
-known. The same model can classify a relation subject or object mention.
+The repository also includes `inference.py`, which handles marker-preserving
+entity-centered truncation with the same context-candidate policy used during
+training.
 
 ## Limitations
 
-- The model does not detect spans; it classifies a supplied span.
-- The split contains sentence overlap by design, so overall metrics are not a
-  pure unseen-sentence estimate.
-- Rare classes such as `UNIT` and `QUANTITY` have limited support.
-- This first experiment intentionally uses no imbalance correction.
+- This is not a full NER model; it requires a known span.
+- There is no `NONE`/non-entity class in this training run.
+- Overall metrics are not a pure unseen-sentence estimate because the split is
+  mention-level and intentionally contains sentence overlap. Use
+  `unseen_sentence` metrics for the stricter generalization view.
+- Rare labels such as `UNIT`, `QUANTITY`, `PRODUCT`, `PERCENT`, and `LANGUAGE`
+  have much lower support than GPE/ORG/DATE/PERS/NORP.
+- The first locked experiment intentionally avoided imbalance correction, extra
+  classifier layers, span pooling, or threshold tuning.
+
+## Reproducibility notes
+
+The generated artifacts contain the run configuration, training arguments,
+Trainer log history, dataset validation report, encoding validation report,
+metrics, predictions, and confusion matrices. The test set was evaluated only
+after training and checkpoint selection; it was not used for checkpoint or
+hyperparameter selection.
+
+Final model card generated from local RunPod artifacts at `2026-07-12T19:05:52+00:00`.

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