Update model card

README.md

@@ -1,3 +1,42 @@
 ---
+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)
+```