|
|
--- |
|
|
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 |
