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https://github.com/luxfhe/examples.git
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ML examples: - credit_scoring: Credit risk prediction with HMEQ data - disease_prediction: Healthcare ML on encrypted records - cifar: Encrypted image classification - federated_learning: Distributed FHE training - hybrid_model: Mixed FHE/plaintext inference Deployment: - breast_cancer: Cancer detection API - sentiment_analysis: Encrypted NLP - server: FHE inference server Blockchain: - hardhat-template: Solidity + FHE - react-template: Full dApp with FHE
30 lines
920 B
Python
30 lines
920 B
Python
import os
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import subprocess
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from pathlib import Path
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def main():
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path_of_script = Path(__file__).parent.resolve()
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os.environ["TRANSFORMERS_CACHE"] = str((path_of_script / "hf_cache").resolve())
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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# Load the tokenizer (converts text to tokens)
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tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
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# Load the pre-trained model
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transformer_model = AutoModelForSequenceClassification.from_pretrained(
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"cardiffnlp/twitter-roberta-base-sentiment-latest"
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)
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del tokenizer
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del transformer_model
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# Build image
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os.chdir(path_of_script)
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command = f'docker build --tag cml_client_sentiment_analysis --file "{path_of_script}/Dockerfile.client" .'
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print(command)
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subprocess.check_output(command, shell=True)
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if __name__ == "__main__":
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main()
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