Beta
Explore
Marketplace
Neural Labs
Playground
Wallet
Docs
sib200_mbert_cda_gemma_crosslingual – AI Model by fledor | AlphaNeural AI
You can deploy this model and start earning money today!
fledor
/
sib200_mbert_cda_gemma_crosslingual
like
0
safetensors
bert
us
Views
No views yet
Model card
Files and Versions
Community
API
Deploy
SIB200 CDA Model with Gemma (Cross-Lingual)
This model was trained on the SIB200 dataset using Counterfactual Data Augmentation (CDA) with counterfactuals generated by Gemma.
Training Parameters
Dataset
: SIB200
Mode
: CDA
Selection Model
: Gemma
Selection Method
: Random
Cross Lingual
: true
Train Size
: 700 examples
Epochs
: 20
Batch Size
: 8
Effective Batch Size
: 32 (batch_size * gradient_accumulation_steps)
Learning Rate
: 8e-06
Patience
: 8
Max Length
: 192
Gradient Accumulation Steps
: 4
Warmup Ratio
: 0.1
Weight Decay
: 0.01
Optimizer
: AdamW
Scheduler
: cosine_with_warmup
Random Seed
: 42
Performance
Overall Accuracy
: 70.37%
Overall Loss
: 0.0269
Language-Specific Performance
English (EN)
: 85.86%
German (DE)
: 87.88%
Arabic (AR)
: 23.23%
Spanish (ES)
: 86.87%
Hindi (HI)
: 73.74%
Swahili (SW)
: 64.65%
Model Information
Base Model
: bert-base-multilingual-cased
Task
: Topic Classification
Languages
: 6 languages (EN, DE, AR, ES, HI, SW)
