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| """ | |
| This code a slight modification of perplexity by hugging face | |
| https://huggingface.co/docs/transformers/perplexity | |
| Both this code and the orignal code are published under the MIT license. | |
| by Burhan Ul tayyab and Nicholas Chua | |
| """ | |
| import torch | |
| import re | |
| from transformers import GPT2LMHeadModel, GPT2TokenizerFast | |
| from collections import OrderedDict | |
| class GPT2PPL: | |
| def __init__(self, device="cpu", model_id="gpt2"): | |
| self.device = device | |
| self.model_id = model_id | |
| self.model = GPT2LMHeadModel.from_pretrained(model_id).to(device) | |
| self.tokenizer = GPT2TokenizerFast.from_pretrained(model_id) | |
| self.max_length = self.model.config.n_positions | |
| self.stride = 512 | |
| def getResults(self, threshold): | |
| # if threshold < 60: | |
| # label = 0 | |
| # return "The Text is generated by AI.", label | |
| # elif threshold < 80: | |
| # label = 0 | |
| # return "The Text is most probably contain parts which are generated by AI. (require more text for better Judgement)", label | |
| # else: | |
| # label = 1 | |
| # return "The Text is written by Human.", label | |
| normalized_score = (threshold - 55) / 30 | |
| # Cap the values at 0 and 100 | |
| human_score = max(0, min(1, normalized_score)) | |
| return {"HUMAN": human_score, "AI": 1 - human_score} | |
| def __call__(self, sentence): | |
| """ | |
| Takes in a sentence split by full stop | |
| and print the perplexity of the total sentence | |
| split the lines based on full stop and find the perplexity of each sentence and print | |
| average perplexity | |
| Burstiness is the max perplexity of each sentence | |
| """ | |
| results = OrderedDict() | |
| total_valid_char = re.findall("[a-zA-Z0-9]+", sentence) | |
| total_valid_char = sum([len(x) for x in total_valid_char]) # finds len of all the valid characters a sentence | |
| if total_valid_char < 100: | |
| return {"status": "Please input more text (min 100 characters)"}, "Please input more text (min 100 characters)" | |
| lines = re.split(r'(?<=[.?!][ \[\(])|(?<=\n)\s*',sentence) | |
| lines = list(filter(lambda x: (x is not None) and (len(x) > 0), lines)) | |
| ppl = self.getPPL(sentence) | |
| print(f"Perplexity {ppl}") | |
| results["Perplexity"] = ppl | |
| offset = "" | |
| Perplexity_per_line = [] | |
| for i, line in enumerate(lines): | |
| if re.search("[a-zA-Z0-9]+", line) == None: | |
| continue | |
| if len(offset) > 0: | |
| line = offset + line | |
| offset = "" | |
| # remove the new line pr space in the first sentence if exists | |
| if line[0] == "\n" or line[0] == " ": | |
| line = line[1:] | |
| if line[-1] == "\n" or line[-1] == " ": | |
| line = line[:-1] | |
| elif line[-1] == "[" or line[-1] == "(": | |
| offset = line[-1] | |
| line = line[:-1] | |
| ppl = self.getPPL(line) | |
| Perplexity_per_line.append(ppl) | |
| print(f"Perplexity per line {sum(Perplexity_per_line)/len(Perplexity_per_line)}") | |
| results["Perplexity per line"] = sum(Perplexity_per_line)/len(Perplexity_per_line) | |
| print(f"Burstiness {max(Perplexity_per_line)}") | |
| results["Burstiness"] = max(Perplexity_per_line) | |
| # out, label = self.getResults(results["Perplexity per line"]) | |
| # results["label"] = label | |
| # return results, out | |
| return self.getResults(results["Perplexity per line"]) | |
| def getPPL(self,sentence): | |
| encodings = self.tokenizer(sentence, return_tensors="pt") | |
| seq_len = encodings.input_ids.size(1) | |
| nlls = [] | |
| likelihoods = [] | |
| prev_end_loc = 0 | |
| for begin_loc in range(0, seq_len, self.stride): | |
| end_loc = min(begin_loc + self.max_length, seq_len) | |
| trg_len = end_loc - prev_end_loc | |
| input_ids = encodings.input_ids[:, begin_loc:end_loc].to(self.device) | |
| target_ids = input_ids.clone() | |
| target_ids[:, :-trg_len] = -100 | |
| with torch.no_grad(): | |
| outputs = self.model(input_ids, labels=target_ids) | |
| neg_log_likelihood = outputs.loss * trg_len | |
| likelihoods.append(neg_log_likelihood) | |
| nlls.append(neg_log_likelihood) | |
| prev_end_loc = end_loc | |
| if end_loc == seq_len: | |
| break | |
| ppl = int(torch.exp(torch.stack(nlls).sum() / end_loc)) | |
| return ppl |