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--- |
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license: apache-2.0 |
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datasets: |
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- dmitva/human_ai_generated_text |
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--- |
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## 0xnu/AGTD-v0.1 |
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The "0xnu/AGTD-v0.1" model represents a significant breakthrough in distinguishing between text written by humans and one generated by Artificial Intelligence (AI). It is rooted in sophisticated algorithms and offers exceptional accuracy and efficiency in text analysis and classification. Everything is detailed in the study and accessible [here](https://arxiv.org/abs/2311.15565). |
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### Training Details |
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```sh |
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Precision: 0.6269 |
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Recall: 1.0000 |
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F1-score: 0.7707 |
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Accuracy: 0.7028 |
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Confusion Matrix: |
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[[197 288] |
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[ 0 484]] |
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``` |
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### Run the model |
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```Python |
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import os |
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os.environ["KERAS_BACKEND"] = "tensorflow" |
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import keras |
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import tensorflow as tf |
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import pickle |
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import numpy as np |
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from huggingface_hub import hf_hub_download |
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# Hugging Face repository details |
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REPO_ID = "0xnu/AGTD-v0.1" |
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MODEL_FILENAME = "human_ai_text_classification_model.keras" |
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TOKENIZER_FILENAME = "tokenizer.pkl" |
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# Download the model and tokenizer |
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model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME) |
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tokenizer_path = hf_hub_download(repo_id=REPO_ID, filename=TOKENIZER_FILENAME) |
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# Load the model |
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model = keras.models.load_model(model_path) |
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# Load the tokenizer |
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with open(tokenizer_path, 'rb') as tokenizer_file: |
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tokenizer = pickle.load(tokenizer_file) |
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# Input text |
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text = "This model trains on a diverse dataset and serves functions in applications requiring a mechanism for distinguishing between human and AI-generated text." |
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# Parameters (these should match the training parameters) |
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MAX_LENGTH = 100000 |
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# Tokenization function |
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def tokenize_text(text, tokenizer, max_length): |
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sequences = tokenizer.texts_to_sequences([text]) |
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padded_sequence = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post') |
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return padded_sequence |
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# Prediction function |
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def predict_text(text, model, tokenizer, max_length): |
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processed_text = tokenize_text(text, tokenizer, max_length) |
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prediction = model.predict(processed_text)[0][0] |
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return prediction |
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# Make prediction |
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prediction = predict_text(text, model, tokenizer, MAX_LENGTH) |
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# Interpret results |
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if prediction >= 0.5: |
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print(f"The text is likely AI-generated (confidence: {prediction:.2f})") |
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else: |
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print(f"The text is likely human-written (confidence: {1-prediction:.2f})") |
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print(f"Raw prediction value: {prediction}") |
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``` |
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### Citation |
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```tex |
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@misc{agtd2024, |
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author = {Oketunji, A.F.}, |
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title = {Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text}, |
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year = 2023, |
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version = {v3}, |
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publisher = {arXiv}, |
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doi = {https://doi.org/10.48550/arXiv.2311.15565}, |
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url = {https://arxiv.org/abs/2311.15565} |
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} |
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``` |
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### Copyright |
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(c) 2024 [Finbarrs Oketunji](https://finbarrs.eu). All Rights Reserved. |
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