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pminervini
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Merge branch 'main' of https://huggingface.co/spaces/pminervini/hallucinations-leaderboard into main
Browse files
src/backend/tasks/selfcheckgpt/task.py
CHANGED
@@ -21,9 +21,9 @@ class SelfCheckGpt(Task):
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def __init__(self, data_dir=None, cache_dir=None, download_mode=None, config=None):
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super().__init__(data_dir=data_dir, cache_dir=cache_dir, download_mode=download_mode, config=config)
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-
self.generation_kwargs = {"
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self.generation_kwargs_sampling_number = 5 # the number of sampling for self-consistence
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-
self.generation_kwargs_sampling = {"temperature":
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self.selfcheckgpt_type = os.environ.get('SELFCHECKGPTTYPE', 'SelfCheckNLI')
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self.selfcheckgpt_device = os.environ.get('SELFCHECKGPTDEVICE', DEVICE)
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@@ -38,7 +38,7 @@ class SelfCheckGpt(Task):
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elif self.selfcheckgpt_type == 'SelfCheckNLI':
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self.selfcheckgpt = SelfCheckNLI(device=self.selfcheckgpt_device)
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self.SelfCheckNLI_error_cnt = 0
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-
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def has_training_docs(self):
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return False
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@@ -102,21 +102,21 @@ class SelfCheckGpt(Task):
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beta1=0.8, beta2=0.8) # additional params depending on scoring_method
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elif self.selfcheckgpt_type == 'SelfCheckNLI':
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selfcheckgpt_scores = self.selfcheckgpt.predict(sentences=sentences, sampled_passages=other_responses)
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-
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if len(selfcheckgpt_scores) == 0:
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self.SelfCheckNLI_error_cnt += 1
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print(f"SelfCheckNLI Warning.SelfCheckNLI_error_cnt:{self.SelfCheckNLI_error_cnt}. This instance is marked as hallucinated with
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result = {
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'avg-selfcheckgpt':
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'max-selfcheckgpt':
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}
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else:
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threshold = 0.
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# passage is hallucianted if one sentence is hallucinated. It's very strict.
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selfcheckgpt_scores_max =
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# passage is hallucianted if average score of all sentences is hallucinated.
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selfcheckgpt_scores_avg =
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result = {'avg-selfcheckgpt': selfcheckgpt_scores_avg, 'max-selfcheckgpt': selfcheckgpt_scores_max}
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return result
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@@ -139,4 +139,4 @@ class SelfCheckGpt(Task):
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A dictionary where keys are the names of submetrics and values are
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whether a higher value of the submetric is better
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"""
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-
return {k:
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def __init__(self, data_dir=None, cache_dir=None, download_mode=None, config=None):
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super().__init__(data_dir=data_dir, cache_dir=cache_dir, download_mode=download_mode, config=config)
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self.generation_kwargs = {"until": ["\n\n", "<unk>", "<|im_end|>", "</s>"], "max_length": 512} # these end tokens are hard coded because of the current limitaion of the llm-eval.
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self.generation_kwargs_sampling_number = 5 # the number of sampling for self-consistence
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self.generation_kwargs_sampling = {"temperature": 0.99, "do_sample": True, "until": ["\n\n", "<unk>", "<|im_end|>", "</s>"], "max_length": 512}
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self.selfcheckgpt_type = os.environ.get('SELFCHECKGPTTYPE', 'SelfCheckNLI')
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self.selfcheckgpt_device = os.environ.get('SELFCHECKGPTDEVICE', DEVICE)
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elif self.selfcheckgpt_type == 'SelfCheckNLI':
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self.selfcheckgpt = SelfCheckNLI(device=self.selfcheckgpt_device)
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self.SelfCheckNLI_error_cnt = 0
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def has_training_docs(self):
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return False
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beta1=0.8, beta2=0.8) # additional params depending on scoring_method
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elif self.selfcheckgpt_type == 'SelfCheckNLI':
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selfcheckgpt_scores = self.selfcheckgpt.predict(sentences=sentences, sampled_passages=other_responses)
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if len(selfcheckgpt_scores) == 0:
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self.SelfCheckNLI_error_cnt += 1
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print(f"SelfCheckNLI Warning.SelfCheckNLI_error_cnt:{self.SelfCheckNLI_error_cnt}. This instance is marked as hallucinated with 0.0.")
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result = {
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'avg-selfcheckgpt': 0.0,
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'max-selfcheckgpt': 0.0
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}
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else:
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threshold = 0.6 # https://huggingface.co/blog/dhuynh95/automatic-hallucination-detection
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# passage is hallucianted if one sentence is hallucinated. It's very strict.
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selfcheckgpt_scores_max = 0.0 if max(selfcheckgpt_scores) > threshold else 1.0
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# passage is hallucianted if average score of all sentences is hallucinated.
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selfcheckgpt_scores_avg = 0.0 if sum(selfcheckgpt_scores) / len(selfcheckgpt_scores) > threshold else 1.0
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result = {'avg-selfcheckgpt': selfcheckgpt_scores_avg, 'max-selfcheckgpt': selfcheckgpt_scores_max}
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return result
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A dictionary where keys are the names of submetrics and values are
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whether a higher value of the submetric is better
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"""
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return {k: True for k in ["avg-selfcheckgpt", "max-selfcheckgpt"]}
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