| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("jaimin/parrot_adequacy_model") | |
| model = AutoModelForSequenceClassification.from_pretrained("jaimin/parrot_adequacy_model") | |
| class Adequacy(): | |
| def __init__(self, model_tag='jaimin/parrot_adequacy_model', use_auth_token="access"): | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| self.adequacy_model = AutoModelForSequenceClassification.from_pretrained(model_tag,use_auth_token="access") | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_tag,use_auth_token="access") | |
| def filter(self, input_phrase, para_phrases, adequacy_threshold, device="cpu"): | |
| top_adequacy_phrases = [] | |
| for para_phrase in para_phrases: | |
| x = self.tokenizer(input_phrase, para_phrase, return_tensors='pt', max_length=128, truncation=True) | |
| self.adequacy_model = self.adequacy_model.to(device) | |
| logits = self.adequacy_model(**x).logits | |
| probs = logits.softmax(dim=1) | |
| prob_label_is_true = probs[:, 1] | |
| adequacy_score = prob_label_is_true.item() | |
| if adequacy_score >= adequacy_threshold: | |
| top_adequacy_phrases.append(para_phrase) | |
| return top_adequacy_phrases | |
| def score(self, input_phrase, para_phrases, adequacy_threshold, device="cpu"): | |
| adequacy_scores = {} | |
| for para_phrase in para_phrases: | |
| x = self.tokenizer(input_phrase, para_phrase, return_tensors='pt', max_length=128, truncation=True) | |
| x = x.to(device) | |
| self.adequacy_model = self.adequacy_model.to(device) | |
| logits = self.adequacy_model(**x).logits | |
| probs = logits.softmax(dim=1) | |
| prob_label_is_true = probs[:, 1] | |
| adequacy_score = prob_label_is_true.item() | |
| if adequacy_score >= adequacy_threshold: | |
| adequacy_scores[para_phrase] = adequacy_score | |
| return adequacy_scores |