This model is a fine-tuned Persian BERT model based on HooshvareLab/bert-base-parsbert-uncased for Named Entity Recognition (NER). It has been trained to identify entities such as persons, organizations, locations, and products in Persian text.

Model Description

bert-finetuned-ner is designed for token-level classification in Persian. The model uses ParsBERT, a BERT variant pretrained on a large Persian corpus, as the base model and is fine-tuned on a wnut2017-persian dataset. It can predict entity labels for each token in input text, supporting tasks such as text analysis, information extraction, and question answering pipelines.

Intended Uses & Limitations

Intended Uses

Named Entity Recognition (NER) in Persian text.
Information extraction for NLP pipelines in Persian language applications.
Academic research or industrial projects requiring entity tagging.

Limitations

Performance depends heavily on the coverage and quality of the training data. Entities not represented in the dataset may not be recognized.
The model may misclassify rare or out-of-vocabulary words.
For critical applications, manual verification of predictions is recommended.
Trained on formal text; performance on dialects or colloquial Persian may vary.

Training and Evaluation Data

Dataset: ('Amir13/wnut2017-persian')
Entities annotated include: persons, organizations, locations, creative works, and products.
Tokenization handled using ParsBERT tokenizer (HooshvareLab/bert-base-parsbert-uncased).

Training Procedure

Training Hyperparameters

Learning rate: 2e-5
Train batch size: 8
Evaluation batch size: 8
Optimizer: AdamW (betas=(0.9, 0.999), epsilon=1e-8)
Learning rate scheduler: linear
Number of epochs: 10
Seed: 42

Training Environment

Framework: Transformers 4.56.1
PyTorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

Training Results

The model achieved training loss of ~0.13 (averaged over all epochs).
Accuracy, F1, precision, and recall metrics are recommended to be computed on a held-out test set for full evaluation.

How to Use

python

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_path = "path_to_saved_model"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForTokenClassification.from_pretrained(model_path)

ner_pipeline = pipeline(
    "ner",
    model=model,
    tokenizer=tokenizer,
    aggregation_strategy="simple"
)

text = "سلام. در تهران زندگی میکنم."
results = ner_pipeline(text)
print(results)

MoAmini77/bert-finetuned-ner

bert-finetuned-ner