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xnli_mbert_cda_qwen_multilingual – AI Model by fledor | AlphaNeural AI
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XNLI CDA Model with Qwen
This model was trained on the XNLI dataset using Counterfactual Data Augmentation (CDA) with counterfactuals generated by Qwen.
Training Parameters
Dataset
: XNLI
Mode
: CDA
Selection Model
: Qwen
Selection Method
: Random
Train Size
: 2400 examples
Epochs
: 8
Batch Size
: 24
Effective Batch Size
: 96 (batch_size * gradient_accumulation_steps)
Learning Rate
: 3e-05
Patience
: 4
Max Length
: 256
Gradient Accumulation Steps
: 4
Warmup Ratio
: 0.1
Weight Decay
: 0.01
Optimizer
: AdamW
Scheduler
: cosine_with_warmup
Random Seed
: 42
Performance
Overall Accuracy
: 66.13%
Overall Loss
: 0.0137
Language-Specific Performance
English (EN)
: 73.45%
German (DE)
: 68.42%
Arabic (AR)
: 64.89%
Spanish (ES)
: 69.94%
Hindi (HI)
: 62.32%
Swahili (SW)
: 57.74%
Model Information
Base Model
: bert-base-multilingual-cased
Task
: Natural Language Inference
Languages
: 6 languages (EN, DE, AR, ES, HI, SW)
