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ProtonX MLE

Project: Fine-tune & quantize Distilbert for IMDB reviews classification. Docker, Flask, ONNX, ONNXRuntime, Transformers, python 3.9

Authors:

Advisors:

I. Set up environment locally

  • Step 1: Python 3.9 & pipenv are required

  • Step 2: cd into sentiment-classification then use command line pipenv install -r requirements.txt to set up env pipenv check to check env status pipenv shell to active env

  • Step 3: cd src in command line

II. Set up docker

docker build -t sentiment-classification .
docker run -dp 5001:80 sentiment-classification

III. Set up your dataset

  • Datasets must be in format CSV. You can check dataset folder to more details.

IV. Training Process

Training script:

python3 trainer.py -ds ${dataset-path} -r ${ratio-split-dataset} -m ${base-model} -b ${batcg-size}
 -e ${epoches} -lr ${learning-rate} -wd ${weight-decay} -o ${output-path} -s ${save-model-dir}

Example: you can ignore all arguments then command will use the default setting

python3 trainer.py -s ../models/DistilBert

dataset-path can accept 1 or more files path, then split dataset with the ratio as you choose. If you provide 2 or 3 files path and do not provide split ratio, the files will be considered in order: train, test or train, valid, test. For 1 or more than 3 files path, split ration is required. Split ratio can accept 1 or 2 values. One value: test-dataset ratio. Two ratios in order: test-dataset ration, validation-dataset ratio. Example:

python3 trainer.py -ds ../dataset/train.csv -r 0.2 0.2 -s ../models/DistilBert

There are some important arguments for the script you should consider when running it:

  • -s: the directory to save model

V. Predict Process

Predict with torch model:

python3 predictor.py torch -m ${torch-model-path}

Example:

python3 predictor.py -m ../models/DistilBert

Predict with onnx model:

python3 predictor.py onnx -m ${onnx-model-path} -t ${tokenizer-path}

Example:

python3 predictor.py -m ../models/ONNX/distilbert_full_precision.onnx -t ../models/ONNX

VI. Evaluation

Run locally on 1000 examples of test dataset

Evaluation torch model

python3 evaluation.py torch

Result:

test Acc: 0.9355
PyTorch cpu Inference time = 323.8157 ms

Evaluation ONNX model

python3 evaluation.py onnx

Result:

test Acc: 0.9482
ONNX cpu Inference time = 286.9295 ms

VII. Notebook & models file

Training notebook with HF dataset: https://colab.research.google.com/drive/1Bxx0w8EHoVo84yJ6I3VqfdED9-YpRtde

Training notebook with TF dataset: https://colab.research.google.com/drive/1PjCCokQnnXdYVKoXD2qcSezBQQmAwqMW#scrollTo=KW8CH-HBCuzN

Quantization notebook: https://colab.research.google.com/drive/1wtAaF8oQhw5vuAjgFtpJjob-kX-STOif#scrollTo=8J6HAKSiE7IK

Models file to download: https://drive.google.com/drive/folders/1hlVSm434UgpwwzROfYKillIxwMc8lWYt?usp=sharing