---
license: apache-2.0
datasets:
- sumitaryal/nepali_grammatical_error_detection
language:
- ne
metrics:
- accuracy
base_model:
- google/muril-base-cased
pipeline_tag: text-classification
widget:
- src: रामले भात खायो ।
  example_title: Sample 1
new_version: sumitaryal/Nepali_Grammatical_Error_Detection_MuRIL
library_name: transformers
---

# Model Card for Nepali Grammatical Error Detection (MuRIL)

This model is designed for **Nepali Grammatical Error Detection (GED)** task. It utilizes the BERT-based MuRIL model to detect grammatical errors in Nepali text.

## Model Details

### Model Description

- **Developed by:** Sumit Aryal
- **Model type:** BERT (MuRIL-based)
- **Language(s):** Nepali
- **License:** Apache 2.0
- **Finetuned from model:** google/muril-base-cased

### Dataset

- **Dataset Name:** [Nepali Grammatical Error Detection Dataset](https://huggingface.co/datasets/sumitaryal/nepali_grammatical_error_detection)
- **Description:** The dataset comprises a total of **2,568,682** correctly constructed sentences alongside their erroneous counterparts, resulting in **7,514,122** samples for the training dataset. For the validation dataset, it contains **365,606** correct sentences and **405,905** incorrect sentences. This diverse collection encompasses various types of grammatical errors, including verb inflections, homophones, punctuation errors, and sentence structure issues, making it a comprehensive resource for training and evaluating grammatical error detection models.

### Model Sources

- **Repository:** [Nepali Grammatical Error Detection MuRIL](https://huggingface.co/sumitaryal/Nepali_Grammatical_Error_Detection_MuRIL)
- **Paper:** "BERT-Based Nepali Grammatical Error Detection and Correction Leveraging a New Corpus" (INSPECT-2024)

## Uses

### Direct Use

- Grammar checking for written Nepali text.

## Evaluation Metrics
- **Accuracy:** 91.1515%
- **Traning Loss:** 0.242700
- **Validation Loss:** 0.217756

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import torch
from transformers import BertForSequenceClassification, AutoTokenizer

model = BertForSequenceClassification.from_pretrained("sumitaryal/Nepali_Grammatical_Error_Detection_MuRIL")
tokenizer = AutoTokenizer.from_pretrained("sumitaryal/Nepali_Grammatical_Error_Detection_MuRIL", do_lower_case=False)

input_sentence = "रामले भात खायो ।"
inputs = tokenizer(input_sentence, return_tensors="pt")

with torch.no_grad():
  logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
predicted_class = model.config.id2label[predicted_class_id]
print(f'The sentence "{input_sentence}" is "{predicted_class}"')
```

## Training Details
- Framework: PyTorch
- Hyperparameters:
  - Epoch = 1
  - Train Batch Size = 256
  - Valid Batch Size = 256
  - Loss Function = Cross Entripy Loss
  - Optimizer = AdamW
  - Optimizer Parameters:
    - Learning Rate = 5e-5
    - β1 = 0.9
    - β2 = 0.999
    - ϵ = 1e−8
- GPU = NVIDIA® GeForce® RTXTM 4060 GPU, 8GB VRAM