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sib200_mbert_cda_llama_crosslingual – AI Model by fledor | AlphaNeural AI
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SIB200 CDA Model with Llama (Cross-Lingual)
This model was trained on the SIB200 dataset using Counterfactual Data Augmentation (CDA) with counterfactuals generated by Llama.
Training Parameters
Dataset
: SIB200
Mode
: CDA
Selection Model
: Llama
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
: 68.52%
Overall Loss
: 0.0210
Language-Specific Performance
English (EN)
: 83.84%
German (DE)
: 83.84%
Arabic (AR)
: 25.25%
Spanish (ES)
: 84.85%
Hindi (HI)
: 73.74%
Swahili (SW)
: 59.60%
Model Information
Base Model
: bert-base-multilingual-cased
Task
: Topic Classification
Languages
: 6 languages (EN, DE, AR, ES, HI, SW)
