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@@ -99,7 +99,8 @@ These techniques and optimizations improve model performance and reduce training
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### Feature support matrix
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The following features are supported by this model.
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-| Feature | BERT |
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+
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+| **Feature** | **BERT** |
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|:-----------------------:|:--------------------------:|
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| Horovod Multi-GPU | Yes |
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@@ -640,12 +641,12 @@ Our results were obtained by running the `run_squad.py` training script in the T
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The following tables compare `F1` scores across 5 different training runs with different seeds, for both FP16 and FP32 respectively. The runs showcase consistent convergence on all 5 seeds with very little deviation.
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-| **FP16, 8x GPUs** | **seed #1** | **seed #2** | **seed #3** | **seed #4** | **seed #5** | **mean** | **std** |
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+| **FP16, 8x GPUs** | **seed 1** | **seed 2** | **seed 3** | **seed 4** | **seed 5** | **mean** | **std** |
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|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
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|F1 |90.75|90.82|90.89|91.05|90.79|90.86|0.12|
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|Exact match|83.85|83.93|83.95|84.25|83.59|83.91|0.24|
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-| **FP32, 8x GPUs** | **seed #1** | **seed #2** | **seed #3** | **seed #4** | **seed #5** | **mean** | **std** |
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+| **FP32, 8x GPUs** | **seed 1** | **seed 2** | **seed 3** | **seed 4** | **seed 5** | **mean** | **std** |
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|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
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|F1 |90.70|90.80|90.89|91.08|90.73|90.84|0.15 |
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|Exact match|83.82|83.77|84.23|84.19|83.63|83.93|0.27 |
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Przemek Strzelczyk