metadata
library_name: transformers
tags:
- peft
- lora
- ottomanturkish
datasets:
- BUCOLIN/HisTR
language:
- tr
metrics:
- f1
- precision
- recall
base_model:
- cihanunlu/BerTurk_Ottoman_Full_DAPT
pipeline_tag: token-classification
Model Card for Model ID
Overview
| Base model | cihanunlu/BerTurk_Ottoman_Full_DAPT |
| Adapter type | LoRA (Low-Rank Adaptation) built with HF PEFT |
| Task | Named-Entity Recognition • BIO tags PER / LOC / O |
| Language | Ottoman / Late-Ottoman Turkish (Latin transliteration) |
| Repo contents | ≈ 2 MB LoRA weights (adapter_model.bin, adapter_config.json) + tokenizer files |
Attach it to the BerTurk_Ottoman_Full_DAPT checkpoint to obtain a lightweight NER model fine-tuned on the HiSTR corpus.
- • Suitable for historical/Ottoman Turkish NER focusing on PERSON and LOCATION.
- • Performance drops on modern Turkish or domain-specific jargon.
- • Adapter inherits ethical constraints and biases of the base BerTurk model.
3 Evaluation
Dev (HiSTR) — best checkpoint (epoch 4)
- Precision – 77.3 %
- Recall – 84.9 %
- F1 – 80.9 %
Test (Rûznâmçe)
- Precision – 54.4 %
- Recall – 52.8 %
- F1 – 53.6 %
4 Training hyper-parameters
- LoRA rank r: 16
- LoRA α: 16
- Dropout: 0.10
- Peak learning rate: 5 × 10⁻⁴
- Effective batch size: 16
- Epochs: 5
- Mixed precision: FP16