From 303a14a2c54c97441d7e74a5775572a535ff979b Mon Sep 17 00:00:00 2001 From: Benjamin Fineran Date: Fri, 22 Sep 2023 15:32:51 -0400 Subject: [PATCH] [README.md] - sst2 extracted location + benchmark model arg (#282) --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index e19112bb..849cab05 100644 --- a/README.md +++ b/README.md @@ -194,7 +194,7 @@ Or, to sparsify a BERT model on the SST2 dataset for sentiment analysis, run the ```bash wget https://public.neuralmagic.com/datasets/nlp/text_classification/sst2_calibration.tar.gz tar -xzf sst2_calibration.tar.gz -sparsify.run one-shot --use-case text_classification --model "zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/base-none" --data ./sst2_calibration --optim-level 0.5 +sparsify.run one-shot --use-case text_classification --model "zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/base-none" --data --data ./sst2_calibration/sst2.hf --optim-level 0.5 ``` To dive deeper into One-Shot Experiments, read through the [One-Shot Experiment Guide](https://github.com/neuralmagic/sparsify/blob/main/docs/one-shot-experiment-guide.md). @@ -293,17 +293,17 @@ This will run a number of inferences to simulate a real-world scenario and print It's as simple as running the following command: ```bash -deepsparse.benchmark --model MODEL --scenario SCENARIO +deepsparse.benchmark --model_path MODEL --scenario SCENARIO ``` For example, to benchmark a dense ResNet-50 model, run the following command: ```bash -deepsparse.benchmark --model "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/base-none" --scenario sync +deepsparse.benchmark --model_path "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/base-none" --scenario sync ``` This can then be compared to the sparsified ResNet-50 model with the following command: ```bash -deepsparse.benchmark --model "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none" --scenario sync +deepsparse.benchmark --model_path "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none" --scenario sync ``` The output will look similar to the following: