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---
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 |