ota-roberta-base-ner (Ottoman Turkish NER)

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

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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-roberta-base

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model = AutoModelForTokenClassification.from_pretrained("enesyila/ota-roberta-base-ner")
tokenizer = AutoTokenizer.from_pretrained("enesyila/ota-roberta-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: 0.01
• Epochs: 10 (early stopping enabled)
• Gradient checkpointing: Enabled
• Mixed precision: Enabled (fp16)

Training Data

The model was fine-tuned on a manually annotated corpus of 5 classical Ottoman Turkish in both prose and verse with IJMES transliteration alphabet, consisting of 6,992 NER spans with labels PER, LOC, ORG, MISC. Folowing works were used as training data:

• 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	1313	609	813	2835	5570
Dev	147	68	133	365	713
Test	162	61	124	362	709

Evaluation Results

Span-level results on test set:

Label	Precision	Recall	F1-score	Support
LOC	0.8971	0.9385	0.9173	195
MISC	0.7317	0.7895	0.7595	76
ORG	0.9195	0.9195	0.9195	149
PER	0.9066	0.9278	0.9171	471

Span-level (micro avg):

• Precision: 0.8909
• Recall: 0.9169
• F1: 0.9038

Span-level (macro avg):

• Precision: 0.8637
• Recall: 0.8938
• F1: 0.8783

Token-level results (excluding “O” label)

Label	Precision	Recall	F1-score	Support
B-PER	0.9470	0.9490	0.9480	471
I-PER	0.9574	0.9662	0.9618	2956
B-LOC	0.9254	0.9538	0.9394	195
I-LOC	0.9176	0.9125	0.9150	537
B-ORG	0.9589	0.9396	0.9492	149
I-ORG	0.9701	0.9602	0.9651	979
B-MISC	0.8800	0.8684	0.8742	76
I-MISC	0.9128	0.8738	0.8929	515

Token-level (macro avg, excl. “O”):

• Precision: 0.9336
• Recall: 0.9279
• F1: 0.9307

Token-level (weighted avg, excl. “O”):

• Precision: 0.9491
• Recall: 0.9495
• F1: 0.9493

Model Card Author

Enes Yılandiloğlu

Model Card Contact

[email protected]