readme: ---
  language: en
  license: apache-2.0
  datasets:
  - sst2
  - glue
  metrics:
  - accuracy
  tags:
  - text-classification
  - neural-compressor
  - int8
  ---
  
  # Dynamically quantized and pruned DistilBERT base uncased finetuned SST-2
  
  ## Table of Contents
  - [Model Details](#model-details)
  - [How to Get Started With the Model](#how-to-get-started-with-the-model)
  
  ## Model Details
  **Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) fine-tuned on SST-2 dynamically quantized and pruned using a magnitude pruning strategy to obtain a sparsity of 10% with [optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
  - **Model Type:** Text Classification
  - **Language(s):** English
  - **License:** Apache-2.0
  - **Parent Model:** For more details on the original model, we encourage users to check out [this](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model card.
  
  ## How to Get Started With the Model
  
  To load the quantized model and run inference using the Transformers [pipelines](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines), you can do as follows:
  
  ```python
  from transformers import AutoTokenizer, pipeline
  from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassification
  
  model_id = "echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1"
  model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id)
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
  text = "He's a dreadful magician."
  outputs = cls_pipe(text)
  ```
  