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| import torch | |
| import datasets | |
| import gradio | |
| from transformers import GPT2LMHeadModel, GPT2TokenizerFast | |
| class CrowSPairsDataset(object): | |
| def __init__(self): | |
| super().__init__() | |
| self.df = (datasets | |
| .load_dataset("BigScienceBiasEval/crows_pairs_multilingual")["test"] | |
| .to_pandas() | |
| .query('stereo_antistereo == "stereo"') | |
| .drop(columns="stereo_antistereo") | |
| ) | |
| def sample(self, bias_type, n=10): | |
| return self.df[self.df["bias_type"] == bias_type].sample(n=n) | |
| def bias_types(self): | |
| return self.df.bias_type.unique().tolist() | |
| def run(bias_type): | |
| sample = dataset.sample(bias_type) | |
| result = "<table><tr style='color: white; background-color: #555'><th>index</th><th>more stereotypical</th><th>less stereotypical<th></tr>" | |
| for i, row in sample.iterrows(): | |
| result += f"<tr><td>{i}</td>" | |
| more = row["sent_more"] | |
| more = tokenizer(more, return_tensors="pt")["input_ids"].to(device) | |
| with torch.no_grad(): | |
| out_more = model(more, labels=more.clone()) | |
| score_more = out_more["loss"] | |
| perplexity_more = -torch.exp(score_more).item() | |
| less = row["sent_less"] | |
| less = tokenizer(less, return_tensors="pt")["input_ids"].to(device) | |
| with torch.no_grad(): | |
| out_less = model(less, labels=less.clone()) | |
| score_less = out_less["loss"] | |
| perplexity_less = -torch.exp(score_less).item() | |
| if perplexity_more > perplexity_less: | |
| shade = round( | |
| abs((perplexity_more - perplexity_less) / perplexity_more), 2 | |
| ) | |
| result += f"<td style='padding: 0 1em; background-color: rgba(0,255,255,{shade})'>{row['sent_more']}</td><td style='padding: 0 1em;'>{row['sent_less']}</td></tr>" | |
| else: | |
| shade = abs((perplexity_less - perplexity_more) / perplexity_less) | |
| result += f"<td style='padding: 0 1em; background-color: rgba(255,0,255,{shade})'>{row['sent_more']}</td><td style='padding: 0 1em; background-color: rgba(255,0,255,{shade})'>{row['sent_less']}</td></tr>" | |
| result += "</table>" | |
| return result | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| else: | |
| device = torch.device("cpu") | |
| model_id = "gpt2" | |
| model = GPT2LMHeadModel.from_pretrained(model_id).to(device) | |
| tokenizer = GPT2TokenizerFast.from_pretrained(model_id) | |
| dataset = CrowSPairsDataset() | |
| bias_type_sel = gradio.Dropdown(label="Bias Type", choices=dataset.bias_types()) | |
| iface = gradio.Interface( | |
| fn=run, | |
| inputs=bias_type_sel, | |
| outputs="html", | |
| title="Detecting stereotypes in the GPT-2 language model using CrowS-Pairs", | |
| description="GPT-2 is a language model which can score how likely it is that some text is a valid English sentence: not only grammaticality, but also the 'meaning' of the sentence is part of this score. CrowS-Pairs is a dataset with pairs of more and less stereotypical examples for different social groups (e.g., gender and nationality stereotypes). We sample 10 random pairs from CrowS-Pairs and show whether the stereotypical example gets a higher score ('is more likely'). If GPT-2 systematically prefers the stereotypical examples, it has probably learnt these stereotypes from the training data.", | |
| ) | |
| iface.launch() | |