From 027a0a3bc7f6b5ee1936a60d9fb6067159ce33fe Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Mon, 25 Apr 2022 10:44:47 -0400 Subject: [PATCH] Update huggingface-transformers/README.md with new models --- examples/huggingface-transformers/README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/huggingface-transformers/README.md b/examples/huggingface-transformers/README.md index b8871e3f8d..4a54903cdc 100644 --- a/examples/huggingface-transformers/README.md +++ b/examples/huggingface-transformers/README.md @@ -28,7 +28,7 @@ The DeepSparse-Hugging Face pipeline integration provides a simple API dedicated ```python from deepsparse.transformers import pipeline -model_path = "zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none" +model_path = "zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/12layer_pruned80_quant-none-vnni" token_classification = pipeline( task="token-classification", @@ -43,7 +43,7 @@ inference = token_classification("I saw Snorlax in Texas!") ```python from deepsparse.transformers import pipeline -model_path = "zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none" +model_path = "zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned80_quant-none-vnni" text_classification = pipeline( task="text-classification", @@ -58,7 +58,7 @@ inference = text_classification("Snorlax loves my Tesla!") ```python from deepsparse.transformers import pipeline -model_path="zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned-aggressive_98" +model_path="zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned80_quant-none-vnni" qa_pipeline = pipeline( task="question-answering", @@ -86,7 +86,7 @@ inference = qa_pipeline(question="What's my name?", context="My name is Snorlax" Want to find out how fast our sparse Hugging Face ONNX models perform inference? You can quickly do benchmarking tests with CLI commands; you only need to provide the model path of a SparseZoo ONNX model or your own local ONNX model to get started: ```bash -deepsparse.benchmark zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned-aggressive_98 +deepsparse.benchmark zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned80_quant-none-vnni ``` For a more in-depth discussion on benchmarking, check out the [Benchmarking tutorial](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/benchmark_model)! @@ -106,5 +106,5 @@ Example CLI command: ```bash deepsparse.server \ --task question_answering \ - --model_path "zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned-aggressive_98" -``` \ No newline at end of file + --model_path "zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned80_quant-none-vnni" +```