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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
35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
import numpy
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import tqdm
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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# Function that transforms a list of texts to their representation
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# learned by the transformer.
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def text_to_tensor(
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list_text_X_train: list,
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transformer_model: AutoModelForSequenceClassification,
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tokenizer: AutoTokenizer,
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device: str,
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) -> numpy.ndarray:
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# Tokenize each text in the list one by one
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tokenized_text_X_train_split = []
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for text_x_train in list_text_X_train:
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tokenized_text_X_train_split.append(tokenizer.encode(text_x_train, return_tensors="pt"))
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# Send the model to the device
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transformer_model = transformer_model.to(device)
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output_hidden_states_list = []
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for tokenized_x in tqdm.tqdm(tokenized_text_X_train_split):
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# Pass the tokens through the transformer model and get the hidden states
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# Only keep the last hidden layer state for now
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output_hidden_states = transformer_model(tokenized_x.to(device), output_hidden_states=True)[
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1
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][-1]
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# Average over the tokens axis to get a representation at the text level.
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output_hidden_states = output_hidden_states.mean(dim=1)
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output_hidden_states = output_hidden_states.detach().cpu().numpy()
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output_hidden_states_list.append(output_hidden_states)
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return numpy.concatenate(output_hidden_states_list, axis=0)
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