| --- |
| 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 |
