• A set of unstructured sparse bert-base-uncased models fine-tuned for SQuADv1.
  • Tensorflow models are created using TFAutoModelForQuestionAnswering.from_pretrained(..., from_pt=True) and model.save_pretrained(tf_pth).
  • Observed issue - loss in model translation, discrepancy observed in evaluation between pytorch and tensorflow models.
  • Table below is evaluated in HF's transformers v4.9.2. Sparsity is normalized to dense layers in attention heads and FFNN.
  • Evaluation cli:
    python run_qa.py \
        --model_name_or_path <model identifier> \
        --dataset_name squad \
        --do_eval \
        --per_device_eval_batch_size 384 \
        --max_seq_length 68 \
        --doc_stride 26 \
        --output_dir /tmp/eval-squad
    
HF Model Hub Identifier sparsity em (pytorch) em (tf) f1 (pytorch) f1 (tf)
0 vuiseng9/bert-base-uncased-squadv1-85.4-sparse 85.4 69.9338 14.2573 77.6861 23.4917
1 vuiseng9/bert-base-uncased-squadv1-72.9-sparse 72.9 74.6358 31.0596 82.2555 39.8446
2 vuiseng9/bert-base-uncased-squadv1-65.1-sparse 65.1 76.1306 43.0274 83.4117 51.4300
3 vuiseng9/bert-base-uncased-squadv1-59.6-sparse 59.6 76.8590 50.4920 84.1267 59.0881
4 vuiseng9/bert-base-uncased-squadv1-52.0-sparse 52.0 78.0038 54.2857 85.2000 62.2914
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