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U4RASD/
TypePredictor

Name: TypePredictor
Rating: 2.5 (34 reviews)

pytorch

type_predictor

arabic

named-entity-recognition

entity-typing

wojood

neoarabert

mention-classification

text-classification

U4RASD/TypePrediction

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TypePredictor

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 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

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

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

GPE, ORG, DATE, PERS, NORP, ORDINAL, OCC, EVENT, CARDINAL, LOC, WEBSITE, FAC, LAW, TIME, MONEY, CURR, LANGUAGE, PERCENT, PRODUCT, QUANTITY, UNIT

Dataset

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. A sentence can appear in more than one split with a different target mention. For that reason, the model card reports both:

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.

Preprocessing and encoding

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

Hardware/runtime:

GPU: NVIDIA A40
GPU VRAM: 44.43 GiB
PyTorch: 2.5.1+cu124
Transformers: 4.49.0
Python: 3.12.3

Validation curve

Validation ran every 500 optimizer steps. The curve below was generated from the Trainer eval log and is included in this repository as figures/eval_metrics.svg; the raw curve data is included as figures/eval_metrics.csv and figures/eval_metrics.json.

Checkpoints

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 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.

Validation results

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

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

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
import importlib.util

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()

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]))

The repository also includes inference.py, which handles marker-preserving entity-centered truncation with the same context-candidate policy used during training.

Limitations

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 2026-07-12T19:07:42+00:00.