| language: en | |
| license: mit | |
| datasets: | |
| - glue | |
| - mrpc | |
| metrics: | |
| - f1 | |
| tags: | |
| - text-classfication | |
| - nlp | |
| - neural-compressor | |
| - PostTrainingDynamic | |
| - int8 | |
| - Intel® Neural Compressor | |
| # Dynamically quantized DistilBERT base uncased finetuned MPRC | |
| ## 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/textattack/distilbert-base-uncased-MRPC) fine-tuned on MPRC dynamically quantized with [optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [huggingface/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/textattack/distilbert-base-uncased-MRPC) model card. | |
| ## How to Get Started With the Model | |
| ### PyTorch | |
| To load the quantized model, you can do as follows: | |
| ```python | |
| from optimum.intel import INCModelForSequenceClassification | |
| model_id = "Intel/distilbert-base-uncased-MRPC-int8-dynamic" | |
| model = INCModelForSequenceClassification.from_pretrained(model_id) | |
| ``` | |
| #### Test result | |
| | |INT8|FP32| | |
| |---|:---:|:---:| | |
| | **Accuracy (eval-f1)** |0.8983|0.9027| | |
| | **Model size (MB)** |75|268| | |