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cihanunlu/BerTurk_Ottoman_Full_DAPT

sourceHugging Faceupdated 1y agoView on Hugging Face

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BerTurk Ottoman Full DAPT

A domain‐adaptive continuation of dbmdz/bert-base-turkish-128k-cased, pre‐trained on 800 K modern‐Latin Ottoman-Turkish sentences (≈ 14 M tokens) from the OTC Corpus (Özateş et al., 2025). This checkpoint is intended as a drop-in encoder for NER task.

Model Details

Property	Value
Base	`dbmdz/bert-base-turkish-128k-cased`
Domain data	`BUCOLIN/OTC-Corpus`
Pre‐training task	Masked Language Modeling (MLM)
Epochs	4
Sequence length	128 tokens (chunked)
Batch size	16 (per device)
Learning rate	3 × 10⁻⁵
Warmup steps	500
Weight decay	0.01
Mixed precision	fp16
Checkpoint size	≈ full weights, fp16
Vocabulary	same as base

Training Data

—Corpus: BUCOLIN/OTC-Corpus
—800 K modern‐Latin transliterations of Ottoman-Turkish text
—Pre‐split into train/validation (90 %/10 %) during fine‐tuning

Training

python


# Args
args = TrainingArguments(
    output_dir="BerTurk_Ottoman_Full_DAPT",
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=4,
    learning_rate=3e-5,
    eval_strategy="epoch",
    save_strategy="epoch",
    warmup_steps=500,
    weight_decay=0.01,
    fp16=True,
    logging_steps=100,
    save_steps=500,
    eval_steps=500,
    save_total_limit=2,
    load_best_model_at_end=True,
)

Hardware & Training

—Hardware: Google Colab Pro (T4 GPU, high VRAM).
—Batch size: 128
—Final Validation Loss | 2.2306
—Total DAPT time: ~ 3 hours for 4 epochs

Test Use

python


from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline

# Load model & tokenizer
tokenizer = AutoTokenizer.from_pretrained("cihanunlu/BerTurk_Ottoman_Full_DAPT")
model     = AutoModelForMaskedLM.from_pretrained("cihanunlu/BerTurk_Ottoman_Full_DAPT")


nlp = pipeline("fill-mask", model=model, tokenizer=tokenizer)
res = nlp("Devlet-i Aliyye-i Osmaniyye’nin [MASK] için tedâbîr-i mühimme ittikhāz olunmalıdır.")
print(res)