This model is the state-of-the-art Named Entity Recognition (NER) model for Ottoman Turkish, fine-tuned from enesyila/ota-mdeberta-v3-base.
It recognizes PERSON, LOCATION, ORGANIZATION, and MISC entities in Ottoman Turkish texts.

Model Details

Developed by: Enes Yılandiloğlu
Model type: Token classification (NER)
Language(s): Ottoman Turkish (ota)
License: cc-by-nc-4.0
Finetuned from: enesyila/ota-mdeberta-v3-base-ner

How to Get Started with the Model

Use the code below to get started with the model.

python

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model = AutoModelForTokenClassification.from_pretrained("enesyila/ota-mdeberta-v3-base-ner")
tokenizer = AutoTokenizer.from_pretrained("enesyila/ota-mdeberta-v3-base-ner")

nlp = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="average")

text = "Aḥmed Paşanın yerine Edrinedeki Meḥmed Efendi Medresesinden Meḥmed Efendi mevsûl oldu."
print(nlp(text))

[{'entity_group': 'PER','score': 0.9800526,'word': 'Aḥmed Paşa','start': 0,'end': 10},
{'entity_group': 'LOC','score': 0.95372033,'word': 'Edrine','start': 21,'end': 27},
{'entity_group': 'ORG','score': 0.8995747,'word': 'Meḥmed Efendi Medresesinden','start': 32,'end': 59},
{'entity_group': 'PER','score': 0.9849827,'word': 'Meḥmed Efendi','start': 60,'end': 73}]

Training Procedure

Loss: Cross-entropy loss
Batch size: 16 (train), 16 (eval)
Optimizer: AdamW
Learning rate: 3e-5
Learning rate scheduler: Linear
Warmup ratio: 0.06
Epochs: 10
Gradient checkpointing: Enabled
Mixed precision: Enabled (fp16)

Training Data

The model was fine-tuned on a manually annotated corpus of 6 classical Ottoman Turkish in both prose and verse with IJMES transliteration alphabet, consisting of 9,960 NER spans with labels PER, LOC, ORG, MISC. Folowing works were used as training data:

Kitâb-ı Fâhir Kıssa-i Anter bin Şeddâd bin Kırâd el-Absî (15th century)
Ḳıṣâṣ-i Enbiyâ (16th century)
Zeyl-i Şakâʾik (17th century)
Veḳâyiʿü'l-Fużala (1731)
Neticetü'l-Fikriyye (18th century)
Silkü'l-Leʾal-i ʿÂl-i Os̱mân (18th century)

Named entity distribution by dataset split (roughly 80/10/10):

Split	LOC	MISC	ORG	PER	TOTAL
Train	1651	777	1374	4152	7954
Dev	165	123	167	556	1011
Test	223	127	147	498	995
Total	2039	1027	1688	5206	9960

Evaluation Results

Span-level results on test set:

Label	Precision	Recall	F1-score	Support
LOC	0.8857	0.9073	0.8964	205
MISC	0.7027	0.7761	0.7376	134
ORG	0.8571	0.8344	0.8456	151
PER	0.8985	0.9415	0.9195	564

Span-level (micro avg):

Precision: 0.8641
Recall: 0.8985
F1: 0.8809

Span-level (macro avg):

Precision: 0.8360
Recall: 0.8648
F1: 0.8498

Token-level results (excluding “O” label)

Label	Precision	Recall	F1-score	Support
B-PER	0.9286	0.9669	0.9474	242
I-PER	0.9800	0.9770	0.9785	4254
B-LOC	0.9459	0.9211	0.9333	76
I-LOC	0.9073	0.9375	0.9221	720
B-ORG	0.9667	0.7632	0.8529	38
I-ORG	0.9690	0.9265	0.9473	1115
B-MISC	0.8571	0.8136	0.8348	59
I-MISC	0.8716	0.8768	0.8742	852

Model Card Author

Enes Yılandiloğlu

Model Card Contact

enes.yilandiloglu@helsinki.fi