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[BERT/PyT] fix typos

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* fix typos

* fix typo
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Sharath TS 2021-04-06 02:46:25 -07:00 committed by GitHub
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2
PyTorch/LanguageModeling/BERT/README.md
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@ -983,7 +983,7 @@ To achieve these same results, follow the steps in the [Quick Start Guide](#quic
##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)
Our results were obtained by running `scripts/run_squad.sh` in the pytorch:20.06-py3 NGC container on NVIDIA DGX-1 with (1x V100 16G) GPUs.
Our results were obtained by running `scripts/run_squad.sh` in the pytorch:20.06-py3 NGC container on NVIDIA DGX A100 with (1x A100 40G) GPUs.
###### Fine-tuning inference on NVIDIA DGX A100 (1x A100 40GB)

2
PyTorch/Segmentation/MaskRCNN/README.md
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@ -83,7 +83,7 @@ The default configuration of this model can be found at `pytorch/maskrcnn_benchm
- Feature extractor:
- Backend network set to Resnet50_conv4
- Backbone network weights are frozen after second epoch
- First two blocks of backbone network weights are frozen
- Region Proposal Network (RPN):
- Anchor stride set to 16

2
TensorFlow/LanguageModeling/BERT/README.md
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@ -1090,7 +1090,7 @@ Our results were obtained by running the `scripts/finetune_inference_benchmark.s
###### Fine-tuning inference performance for SQuAD v1.1 on DGX A100 40GB
Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` training script in the TensorFlow 20.06-py3 NGC container on NVIDIA DGX A100 with 1x V100 40GB GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining.
Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` training script in the TensorFlow 20.06-py3 NGC container on NVIDIA DGX A100 with 1x A100 40GB GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining.
| Model | Sequence Length | Batch Size | Precision | Throughput-Average(sent/sec) | Latency-Average(ms) | Latency-90%(ms) | Latency-95%(ms) | Latency-99%(ms) |
|-------|-----------------|------------|-----------|------------------------------|---------------------|-----------------|-----------------|-----------------|

2
TensorFlow2/LanguageModeling/ELECTRA/README.md
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@ -970,7 +970,7 @@ TF32
##### Inference performance: NVIDIA T4
Our results were obtained by running the `scripts/benchmark_squad.sh` script in the tensorflow:20.07-tf2-py3 NGC container on NVIDIA DGX-1 with (1x V100 16G) GPUs.
Our results were obtained by running the `scripts/benchmark_squad.sh` script in the tensorflow:20.07-tf2-py3 NGC container on NVIDIA Tesla T4 (1x T4 16GB) GPU.
###### Fine-tuning inference on NVIDIA T4