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| from deepsparse import Pipeline | |
| import time | |
| import gradio as gr | |
| markdownn = ''' | |
| # Text Classification Pipeline with DeepSparse | |
| Text Classification involves assigning a label to a given text. For example, sentiment analysis is an example of a text classification use case. | |
|  | |
| ## What is DeepSparse | |
| DeepSparse is sparsity-aware inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application. DeepSparse provides sparsified pipelines for computer vision and NLP. | |
| The text classification Pipeline, for example, wraps an NLP model with the proper preprocessing and postprocessing pipelines, such as tokenization. | |
| ### Inference | |
| Here is sample code for a text classification pipeline: | |
| ``` | |
| from deepsparse import Pipeline | |
| pipeline = Pipeline.create(task="zero_shot_text_classification", model_path="zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/pruned80_quant-none-vnni",model_scheme="mnli",model_config={"hypothesis_template": "This text is related to {}"},) | |
| inference = pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],) | |
| print(inference) | |
| ``` | |
| ## Resources | |
| [Classify Even Longer Customer Reviews Using Sparsity with DeepSparse](https://neuralmagic.com/blog/accelerate-customer-review-classification-with-sparse-transformers/) | |
| ''' | |
| task = "zero_shot_text_classification" | |
| dense_classification_pipeline = Pipeline.create( | |
| task=task, | |
| model_path="zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none", | |
| model_scheme="mnli", | |
| model_config={"hypothesis_template": "This text is related to {}"}, | |
| ) | |
| sparse_classification_pipeline = Pipeline.create( | |
| task=task, | |
| model_path="zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/pruned80_quant-none-vnni", | |
| model_scheme="mnli", | |
| model_config={"hypothesis_template": "This text is related to {}"}, | |
| ) | |
| def run_pipeline(text): | |
| dense_start = time.perf_counter() | |
| dense_output = dense_classification_pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],) | |
| dense_result = dict(dense_output) | |
| dense_end = time.perf_counter() | |
| dense_duration = (dense_end - dense_start) * 1000.0 | |
| sparse_start = time.perf_counter() | |
| sparse_output = sparse_classification_pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],) | |
| sparse_result = dict(sparse_output) | |
| sparse_end = time.perf_counter() | |
| sparse_duration = (sparse_end - sparse_start) * 1000.0 | |
| return sparse_result, sparse_duration, dense_result, dense_duration | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(markdownn) | |
| with gr.Column(): | |
| gr.Markdown(""" | |
| ### Text classification demo | |
| """) | |
| text = gr.Text(label="Text") | |
| btn = gr.Button("Submit") | |
| dense_answers = gr.Textbox(label="Dense model answer") | |
| dense_duration = gr.Number(label="Dense Latency (ms):") | |
| sparse_answers = gr.Textbox(label="Sparse model answers") | |
| sparse_duration = gr.Number(label="Sparse Latency (ms):") | |
| gr.Examples([["Who are you voting for in 2020?"],["Public health is very important"]],inputs=[text],) | |
| btn.click( | |
| run_pipeline, | |
| inputs=[text], | |
| outputs=[sparse_answers,sparse_duration,dense_answers,dense_duration], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |