Update README

README.md

@@ -1,3 +1,41 @@
 ---
 license: apache-2.0
 ---
+ # Quantized BERT-base model with 90% of ustructured sparsity
+ The pruned and quantized model in the OpenVINO IR. The pruned model was taken from this source and quantized with the code below using HF Optimum for OpenVINO:
+ 
+ ```python
+from functools import partial
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from optimum.intel.openvino import OVConfig, OVQuantizer
+
+model_id = "neuralmagic/oBERT-12-downstream-pruned-unstructured-90-mnli" #"typeform/distilbert-base-uncased-mnli" 
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+save_dir = "./nm_mnli_90"
+
+def preprocess_function(examples, tokenizer):
+    return tokenizer(examples["premise"], examples["hypothesis"], padding="max_length", max_length=128, truncation=True)
+
+# Load the default quantization configuration detailing the quantization we wish to apply
+quantization_config = OVConfig()
+# Instantiate our OVQuantizer using the desired configuration
+quantizer = OVQuantizer.from_pretrained(model)
+# Create the calibration dataset used to perform static quantization
+
+calibration_dataset = quantizer.get_calibration_dataset(
+    "glue",
+    dataset_config_name="mnli",
+    preprocess_function=partial(preprocess_function, tokenizer=tokenizer),
+    num_samples=100,
+    dataset_split="train",
+)
+# Apply static quantization and export the resulting quantized model to OpenVINO IR format
+quantizer.quantize(
+    quantization_config=quantization_config,
+    calibration_dataset=calibration_dataset,
+    save_directory=save_dir,
+)
+# Save the tokenizer
+tokenizer.save_pretrained(save_dir)
+ ```