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--- |
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license: mit |
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datasets: |
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- dmitva/human_ai_generated_text |
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language: |
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- en |
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widget: |
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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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tags: |
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- nlp |
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- code |
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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. I detailed the findings in a study, and it is accessible [here](https://arxiv.org/abs/2311.15565). |
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## Instruction Format |
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``` |
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<BOS> [CLS] [INST] Instruction [/INST] Model answer [SEP] [INST] Follow-up instruction [/INST] [SEP] [EOS] |
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``` |
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Pseudo-code for tokenizing instructions with the new format: |
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```Python |
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def tokenize(text): |
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return tok.encode(text, add_special_tokens=False) |
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[BOS_ID] + |
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tokenize("[CLS]") + tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") + |
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tokenize(BOT_MESSAGE_1) + tokenize("[SEP]") + |
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… |
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tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") + |
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tokenize(BOT_MESSAGE_N) + tokenize("[SEP]") + [EOS_ID] |
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``` |
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Notes: |
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- `[CLS]`, `[SEP]`, `[PAD]`, `[UNK]`, and `[MASK]` tokens are integrated based on their definitions in the tokenizer configuration. |
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- `[INST]` and `[/INST]` are utilized to encapsulate instructions. |
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- The tokenize method should not automatically add BOS or EOS tokens but should add a prefix space. |
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- The `do_lower_case` parameter indicates that text should be in lowercase for consistent tokenization. |
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- `clean_up_tokenization_spaces` remove unnecessary spaces in the tokenization process. |
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- The `tokenize_chinese_chars` parameter indicates special handling for Chinese characters. |
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- The maximum model length is set to 512 tokens. |
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## Installing Libraries |
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```sh |
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pip install transformers |
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``` |
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## Run the model |
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```Python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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model_id = "0xnu/AGTD-v0.1" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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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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# Preprocess the text |
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inputs = tokenizer(text, return_tensors='pt') |
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# Run the model |
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outputs = model(**inputs) |
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# Interpret the output |
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logits = outputs.logits |
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# Apply softmax to convert logits to probabilities |
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probabilities = torch.softmax(logits, dim=1) |
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# Assuming the first class is 'human' and the second class is 'ai' |
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human_prob, ai_prob = probabilities.detach().numpy()[0] |
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# Print probabilities |
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print(f"Human Probability: {human_prob:.4f}") |
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print(f"AI Probability: {ai_prob:.4f}") |
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# Determine if the text is human or AI-generated |
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if human_prob > ai_prob: |
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print("The text is likely human-generated.") |
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else: |
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print("The text is likely AI-generated.") |
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``` |
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## Citation |
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Please cite the paper if you are using the resource for your work. |
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```bibtex |
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@misc{abiodunfinbarrsoketunji-agtd2023, |
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doi = {10.48550/arXiv.2311.15565}, |
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url = {https://arxiv.org/abs/2311.15565}, |
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author = {Abiodun Finbarrs Oketunji}, |
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title = {Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text}, |
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publisher = {arXiv}, |
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year = {2023}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
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