Dynamically quantized and pruned DistilBERT base uncased finetuned SST-2
Table of Contents
Model Details
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized and pruned using a magnitude pruning strategy to obtain a sparsity of 10% with optimum-intel through the usage of 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 model card.
How to Get Started With the Model
This requires to install Optimum :
pip install optimum[neural-compressor]
To load the quantized model and run inference using the Transformers pipelines, you can do as follows:
from transformers import AutoTokenizer, pipeline
from optimum.intel import INCModelForSequenceClassification
model_id = "echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1"
model = INCModelForSequenceClassification.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)