metadata

language: en
license: apache-2.0
tags:
  - text-classfication
  - int8
  - Intel® Neural Compressor
  - QuantizationAwareTraining
datasets:
  - mrpc
metrics:
  - f1

INT8 BERT base uncased finetuned MRPC

QuantizationAwareTraining

This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.

The original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.

Test result

	INT8	FP32
Accuracy (eval-f1)	0.9142	0.9042
Model size (MB)	107	418

Load with optimum:

from optimum.intel import INCModelForSequenceClassification

model_id = "Intel/bert-base-uncased-mrpc-int8-qat"
int8_model = INCModelForSequenceClassification.from_pretrained(model_id)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0
train_batch_size: 8
eval_batch_size: 8
eval_steps: 100
load_best_model_at_end: True
metric_for_best_model: f1
early_stopping_patience = 6
early_stopping_threshold = 0.001