---
library_name: peft
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- base_model:adapter:microsoft/mdeberta-v3-base
- lora
- transformers
metrics:
- name: accuracy
  type: accuracy
  value: 0.9541
- name: f1
  type: f1
model-index:
- name: Subodh_MFND_mdeberta_v3
  results:
    - task:
        type: text-classification
        name: Multilingual Fake News Detection
      dataset:
        name: Custom Multilingual Fake News
        type: text
      metrics:
        - name: accuracy
          type: accuracy
          value: 0.9541
        - name: f1
          type: f1
          value: 0.95
---

# Subodh_MFND_mdeberta_v3

This model is a LoRA fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) for multilingual fake news detection (Bangla, English, Hindi, Spanish).  
**Final evaluation set results:**  
- **Accuracy**: 95.41%
- **F1**: 0.95
- (Precision/Recall can be filled in if you have them.)

## Model description

- Privacy-preserved, multi-lingual fake news detection.
- Fine-tuned with LoRA adapters (r=8, α=16, dropout=0.1).
- Batch size: 8, Epochs: 3, Learning rate: 2e-4.

## Intended uses & limitations

- Intended for research and production on multilingual fake news detection tasks.
- Works on Bangla, English, Hindi, and Spanish news content.
- Not intended for languages outside the fine-tuning set.

## Training and evaluation data

- Dataset: Custom multilingual fake news corpus (Bangla, English, Hindi, Spanish)
- Supervised classification (fake/real)

## Training procedure

### Training hyperparameters

- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: AdamW
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1    |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----:|
| 0.4942        | 1.0   | 9375  | 0.4617          | 0.7785   | 0.7776|
| 0.4948        | 2.0   | 18750 | 0.4684          | 0.7591   | 0.7424|
| 0.4892        | 3.0   | 28125 | 0.4376          | 0.7702   | 0.7569|
| **Final Test**|   -   |   -   |      -          | **0.9541** | **0.95** |

### Framework versions

- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0