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--- |
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language: en |
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license: apache-2.0 |
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datasets: |
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- sst2 |
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- glue |
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metrics: |
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- accuracy |
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tags: |
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- text-classification |
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- neural-compressor |
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- int8 |
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--- |
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# Dynamically quantized and pruned DistilBERT base uncased finetuned SST-2 |
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## Table of Contents |
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- [Model Details](#model-details) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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## Model Details |
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**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). |
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- **Model Type:** Text Classification |
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- **Language(s):** English |
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- **License:** Apache-2.0 |
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- **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. |
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## How to Get Started With the Model |
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This requires to install Optimum : |
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`pip install optimum[neural-compressor]` |
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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: |
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```python |
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from transformers import AutoTokenizer, pipeline |
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from optimum.intel import INCModelForSequenceClassification |
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model_id = "echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1" |
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model = INCModelForSequenceClassification.from_pretrained(model_id) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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text = "He's a dreadful magician." |
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outputs = cls_pipe(text) |
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``` |
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