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xnli_mbert_cda_gemma_crosslingual

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XNLI CDA Model with Gemma (Cross-Lingual)

This model was trained on the XNLI dataset using Counterfactual Data Augmentation (CDA) with counterfactuals generated by Gemma.

Training Parameters

  • Dataset: XNLI
  • Mode: CDA
  • Selection Model: Gemma
  • Selection Method: Random
  • Cross Lingual: true
  • Train Size: 2400 examples
  • Epochs: 8
  • Batch Size: 16
  • Effective Batch Size: 64 (batch_size * gradient_accumulation_steps)
  • Learning Rate: 1e-05
  • Patience: 6
  • 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: 58.96%
  • Overall Loss: 0.0161

Language-Specific Performance

  • English (EN): 71.66%
  • German (DE): 62.53%
  • Arabic (AR): 56.01%
  • Spanish (ES): 64.35%
  • Hindi (HI): 52.38%
  • Swahili (SW): 46.81%

Model Information

  • Base Model: bert-base-multilingual-cased
  • Task: Natural Language Inference
  • Languages: 6 languages (EN, DE, AR, ES, HI, SW)
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