README.md

---
library_name: transformers
base_model: HooshvareLab/bert-base-parsbert-uncased
tags:
- ner
- persian
- fine-tuned
- transformers
model-index:
- name: bert-finetuned-ner
  results: []
---

# bert-finetuned-ner

This model is a **fine-tuned Persian BERT model** based on [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/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)