kopia lustrzana https://github.com/thinkst/zippy
Added OpenAI's detector and all the test run reports along with a ROC diagram
Signed-off-by: Jacob Torrey <jacob@thinkst.com>pull/6/head
Jacob Torrey
rodzic
8af95c8296
commit
5fe84f9aed
Plik binarny nie jest wyświetlany.
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Po Szerokość: | Wysokość: | Rozmiar: 58 KiB |
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@ -1,6 +1,6 @@
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#!/usr/bin/env python3
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import os, requests
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import os, requests, re
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from typing import Optional, Dict, Tuple
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MODEL_NAME = 'model-detect-v2'
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@ -28,19 +28,50 @@ def make_req(text : str) -> Optional[Dict]:
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'logprobs': 5
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}
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res = requests.post(API_URL, headers=headers, json=data)
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return res.json().get('choices', [None])[0]
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#print(str(res.status_code) + ' ' + res.text)
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res = res.json().get('choices', [None])[0]
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if res is None:
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return None
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if res.get('text') == '"':
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return ('AI', abs(res.get('logprobs').get('token_logprobs')[0]))
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elif res.get('text') == '!':
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return ('Human', abs(res.get('logprobs').get('token_logprobs')[0]))
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return None #res.get('text')
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def run_on_file(fn : str) -> Optional[Tuple[str, float]]:
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def run_on_file_chunked(fn : str, chunk_size : int = 4096) -> Optional[Tuple[str, float]]:
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with open(fn, 'r') as fp:
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contents = fp.read()
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res = make_req(contents)
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if res is None:
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print("Unable to classify!")
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return None
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else:
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#print(res)
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if res.get('text') == '"':
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return ('AI', abs(res.get('logprobs').get('token_logprobs')[0]))
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elif res.get('text') == '!':
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return ('Human', abs(res.get('logprobs').get('token_logprobs')[0]))
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return None #res.get('text')
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return run_on_text_chunked(contents)
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def run_on_text_chunked(contents : str, chunk_size : int = 4096) -> Optional[Tuple[str, float]]:
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# Remove extra spaces and duplicate newlines.
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contents = re.sub(' +', ' ', contents)
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contents = re.sub('\t', '', contents)
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contents = re.sub('\n+', '\n', contents)
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contents = re.sub('\n ', '\n', contents)
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start = 0
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end = 0
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chunks = []
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while start + chunk_size < len(contents) and end != -1:
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end = contents.rfind(' ', start, start + chunk_size + 1)
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chunks.append(contents[start:end])
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start = end + 1
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chunks.append(contents[start:])
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scores = []
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for c in chunks:
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scores.append(make_req(c))
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ssum : float = 0.0
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for s in scores:
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if s is None:
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continue
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if s[0] == 'AI':
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ssum -= s[1]
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else:
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ssum += s[1]
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sa : float = ssum / len(scores)
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if sa < 0:
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return ('AI', abs(sa))
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else:
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return ('Human', abs(sa))
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@ -9,7 +9,7 @@ from itertools import chain
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from math import sqrt
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from junitparser import JUnitXml
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MODELS = ['lzma', 'roberta', 'gptzero']
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MODELS = ['lzma', 'roberta', 'gptzero', 'openai']
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plt.figure()
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@ -53,8 +53,9 @@ for model in MODELS:
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# Plot the ROC curve
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plt.plot(fpr, tpr, lw=2, label=model.capitalize() + ': ROC curve (AUC = %0.2f)' % roc_auc)
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plt.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best @ threshold = %0.2f' % thresholds[ix])
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plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--', label="Random classifier")
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plt.scatter(fpr[ix], tpr[ix], marker='o', color='black')#, label=model.capitalize() + ': Best @ threshold = %0.2f' % thresholds[ix])
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plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--', label="Random classifier")
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plt.xlim([0.0, 1.0])
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plt.ylim([0.0, 1.05])
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plt.xlabel('False Positive Rate')
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@ -1,26 +1,110 @@
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#!/usr/bin/env python3
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import pytest, os
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import pytest, os, jsonlines
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from warnings import warn
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from openai_detect import run_on_file
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from openai_detect import run_on_file_chunked, run_on_text_chunked
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MIN_LEN = 1000
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NUM_JSONL_SAMPLES = 50
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AI_SAMPLE_DIR = 'samples/llm-generated/'
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HUMAN_SAMPLE_DIR = 'samples/human-generated/'
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ai_files = os.listdir(AI_SAMPLE_DIR)
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ai_files = filter(lambda f: os.path.getsize(AI_SAMPLE_DIR + f) >= 1000, ai_files)
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human_files = os.listdir(HUMAN_SAMPLE_DIR)
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human_files = filter(lambda f: os.path.getsize(HUMAN_SAMPLE_DIR + f) >= 1000, human_files)
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ai_files = [f for f in os.listdir(AI_SAMPLE_DIR) if os.path.getsize(AI_SAMPLE_DIR + f) >= MIN_LEN]
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human_files = [f for f in os.listdir(HUMAN_SAMPLE_DIR) if os.path.getsize(HUMAN_SAMPLE_DIR + f) >= MIN_LEN]
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def test_training_file():
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assert run_on_file('ai-generated.txt')[0] == 'AI', 'The training corpus should always be detected as AI-generated... since it is'
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CONFIDENCE_THRESHOLD : float = 0.00 # What confidence to treat as error vs warning
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def test_training_file(record_property):
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(classification, score) = run_on_file_chunked('ai-generated.txt')
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record_property("score", str(score))
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assert classification == 'AI', 'The training corpus should always be detected as AI-generated... since it is (score: ' + str(round(score, 8)) + ')'
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@pytest.mark.parametrize('f', human_files)
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def test_human_samples(f):
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(classification, score) = run_on_file(HUMAN_SAMPLE_DIR + f)
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assert classification == 'Human', f + ' is a human-generated file, misclassified as AI-generated with confidence ' + str(round(score, 8))
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def test_human_samples(f, record_property):
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res = run_on_file_chunked(HUMAN_SAMPLE_DIR + f)
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if res is None:
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pytest.skip('Unable to classify')
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(classification, score) = res
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record_property("score", str(score))
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if score > CONFIDENCE_THRESHOLD:
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assert classification == 'Human', f + ' is a human-generated file, misclassified as AI-generated with confidence ' + str(round(score, 8))
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else:
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if classification != 'Human':
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warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
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else:
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warn("Unable to confidently classify: " + f)
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@pytest.mark.parametrize('f', ai_files)
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def test_llm_sample(f):
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(classification, score) = run_on_file(AI_SAMPLE_DIR + f)
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assert classification == 'AI', f + ' is an LLM-generated file, misclassified as human-generated with confidence ' + str(round(score, 8))
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def test_llm_sample(f, record_property):
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res = run_on_file_chunked(AI_SAMPLE_DIR + f)
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if res is None:
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pytest.skip('Unable to classify')
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(classification, score) = res
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record_property("score", str(score))
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if score > CONFIDENCE_THRESHOLD:
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assert classification == 'AI', f + ' is an LLM-generated file, misclassified as human-generated with confidence ' + str(round(score, 8))
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else:
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if classification != 'AI':
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warn("Misclassified " + f + " with score of: " + str(round(score, 8)))
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else:
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warn("Unable to confidently classify: " + f)
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HUMAN_JSONL_FILE = 'samples/webtext.test.jsonl'
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human_samples = []
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with jsonlines.open(HUMAN_JSONL_FILE) as reader:
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for obj in reader:
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if len(obj.get('text')) >= MIN_LEN:
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human_samples.append(obj)
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@pytest.mark.parametrize('i', human_samples[0:NUM_JSONL_SAMPLES])
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def test_human_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i.get('text', ''))
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record_property("score", str(score))
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assert classification == 'Human', HUMAN_JSONL_FILE + ':' + str(i.get('id')) + ' (len: ' + str(i.get('length', -1)) + ') is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
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AI_JSONL_FILE = 'samples/xl-1542M.test.jsonl'
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ai_samples = []
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with jsonlines.open(AI_JSONL_FILE) as reader:
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for obj in reader:
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if len(obj.get('text')) >= MIN_LEN:
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ai_samples.append(obj)
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@pytest.mark.parametrize('i', ai_samples[0:NUM_JSONL_SAMPLES])
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def test_llm_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i.get('text', ''))
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record_property("score", str(score))
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assert classification == 'AI', AI_JSONL_FILE + ':' + str(i.get('id')) + ' (text: ' + i.get('text', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
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GPT3_JSONL_FILE = 'samples/GPT-3-175b_samples.jsonl'
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gpt3_samples = []
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with jsonlines.open(GPT3_JSONL_FILE) as reader:
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for o in reader:
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for l in o.split('<|endoftext|>'):
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if len(l) >= MIN_LEN:
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gpt3_samples.append(l)
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@pytest.mark.parametrize('i', gpt3_samples[0:NUM_JSONL_SAMPLES])
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def test_gpt3_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i)
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record_property("score", str(score))
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assert classification == 'AI', GPT3_JSONL_FILE + ' is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
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NEWS_JSONL_FILE = 'samples/news.jsonl'
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news_samples = []
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with jsonlines.open(NEWS_JSONL_FILE) as reader:
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for obj in reader:
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if len(obj.get('human')) >= MIN_LEN:
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news_samples.append(obj)
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@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
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def test_humannews_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i.get('human', ''))
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record_property("score", str(score))
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assert classification == 'Human', NEWS_JSONL_FILE + ' is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
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@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
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def test_chatgptnews_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i.get('chatgpt', ''))
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record_property("score", str(score))
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assert classification == 'AI', NEWS_JSONL_FILE + ' is a AI-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
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@ -8,19 +8,22 @@ AI_SAMPLE_DIR = 'samples/llm-generated/'
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HUMAN_SAMPLE_DIR = 'samples/human-generated/'
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MIN_LEN = 150
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NUM_JSONL_SAMPLES = 50
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ai_files = os.listdir(AI_SAMPLE_DIR)
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human_files = os.listdir(HUMAN_SAMPLE_DIR)
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CONFIDENCE_THRESHOLD : float = 0.00 # What confidence to treat as error vs warning
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def test_training_file():
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def test_training_file(record_property):
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(classification, score) = run_on_file_chunked('ai-generated.txt')
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record_property("score", str(score))
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assert classification == 'AI', 'The training corpus should always be detected as AI-generated... since it is (score: ' + str(round(score, 8)) + ')'
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@pytest.mark.parametrize('f', human_files)
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def test_human_samples(f):
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def test_human_samples(f, record_property):
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(classification, score) = run_on_file_chunked(HUMAN_SAMPLE_DIR + f)
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record_property("score", str(score))
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if score > CONFIDENCE_THRESHOLD:
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assert classification == 'Human', f + ' is a human-generated file, misclassified as AI-generated with confidence ' + str(round(score, 8))
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else:
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@ -30,8 +33,9 @@ def test_human_samples(f):
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warn("Unable to confidently classify: " + f)
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@pytest.mark.parametrize('f', ai_files)
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def test_llm_sample(f):
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def test_llm_sample(f, record_property):
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(classification, score) = run_on_file_chunked(AI_SAMPLE_DIR + f)
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record_property("score", str(score))
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if score > CONFIDENCE_THRESHOLD:
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assert classification == 'AI', f + ' is an LLM-generated file, misclassified as human-generated with confidence ' + str(round(score, 8))
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else:
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@ -46,9 +50,10 @@ with jsonlines.open(HUMAN_JSONL_FILE) as reader:
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for obj in reader:
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human_samples.append(obj)
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@pytest.mark.parametrize('i', human_samples[0:250])
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def test_human_jsonl(i):
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@pytest.mark.parametrize('i', human_samples[0:NUM_JSONL_SAMPLES])
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def test_human_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i.get('text', ''))
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record_property("score", str(score))
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assert classification == 'Human', HUMAN_JSONL_FILE + ':' + str(i.get('id')) + ' (len: ' + str(i.get('length', -1)) + ') is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
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AI_JSONL_FILE = 'samples/xl-1542M.test.jsonl'
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@ -57,9 +62,10 @@ with jsonlines.open(AI_JSONL_FILE) as reader:
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for obj in reader:
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ai_samples.append(obj)
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@pytest.mark.parametrize('i', ai_samples[0:250])
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def test_llm_jsonl(i):
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@pytest.mark.parametrize('i', ai_samples[0:NUM_JSONL_SAMPLES])
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def test_llm_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i.get('text', ''))
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record_property("score", str(score))
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assert classification == 'AI', AI_JSONL_FILE + ':' + str(i.get('id')) + ' (text: ' + i.get('text', "").replace('\n', ' ')[:50] + ') is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
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GPT3_JSONL_FILE = 'samples/GPT-3-175b_samples.jsonl'
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@ -70,9 +76,10 @@ with jsonlines.open(GPT3_JSONL_FILE) as reader:
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if len(l) >= MIN_LEN:
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gpt3_samples.append(l)
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@pytest.mark.parametrize('i', gpt3_samples)
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def test_gpt3_jsonl(i):
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@pytest.mark.parametrize('i', gpt3_samples[0:NUM_JSONL_SAMPLES])
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def test_gpt3_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i)
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record_property("score", str(score))
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assert classification == 'AI', GPT3_JSONL_FILE + ' is an LLM-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
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NEWS_JSONL_FILE = 'samples/news.jsonl'
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@ -81,12 +88,14 @@ with jsonlines.open(NEWS_JSONL_FILE) as reader:
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for obj in reader:
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news_samples.append(obj)
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@pytest.mark.parametrize('i', news_samples[0:250])
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def test_humannews_jsonl(i):
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@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
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def test_humannews_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i.get('human', ''))
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record_property("score", str(score))
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assert classification == 'Human', NEWS_JSONL_FILE + ' is a human-generated sample, misclassified as AI-generated with confidence ' + str(round(score, 8))
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@pytest.mark.parametrize('i', news_samples[0:250])
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def test_chatgptnews_jsonl(i):
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@pytest.mark.parametrize('i', news_samples[0:NUM_JSONL_SAMPLES])
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def test_chatgptnews_jsonl(i, record_property):
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(classification, score) = run_on_text_chunked(i.get('chatgpt', ''))
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record_property("score", str(score))
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assert classification == 'AI', NEWS_JSONL_FILE + ' is a AI-generated sample, misclassified as human-generated with confidence ' + str(round(score, 8))
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