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| import torch | |
| from onnxruntime.quantization import quantize_dynamic,QuantType | |
| import os | |
| import subprocess | |
| import numpy as np | |
| import pandas as pd | |
| def zero_shot_classification(premise: str, labels: str, model, tokenizer): | |
| try: | |
| labels=labels.split(',') | |
| labels=[l.lower() for l in labels] | |
| except: | |
| raise Exception("please pass atleast 2 labels to classify") | |
| premise=premise.lower() | |
| labels_prob=[] | |
| for l in labels: | |
| hypothesis= f'this is an example of {l}' | |
| input = tokenizer.encode(premise,hypothesis, | |
| return_tensors='pt', | |
| truncation_strategy='only_first') | |
| output = model(input) | |
| entail_contra_prob = output['logits'][:,[0,2]].softmax(dim=1)[:,1].item() #only normalizing entail & contradict probabilties | |
| labels_prob.append(entail_contra_prob) | |
| labels_prob_norm=[np.round(100*c/np.sum(labels_prob),1) for c in labels_prob] | |
| df=pd.DataFrame({'labels':labels, | |
| 'Probability':labels_prob_norm}) | |
| return df | |
| ##example | |
| # zero_shot_classification(premise='Tiny worms and breath analyzers could screen for \disease while it’s early and treatable', | |
| # labels='science, sports, museum') | |
| def create_onnx_model_zs(art_path='zeroshot_onnx_dir'): | |
| # create onnx model using | |
| if not os.path.exists(art_path): | |
| try: | |
| subprocess.run(['python3', '-m', 'transformers.onnx', | |
| '--model=facebook/bart-large-mnli', | |
| '--feature=sequence-classification', | |
| art_path]) | |
| except: | |
| pass | |
| #create quanitzed model from vanila onnx | |
| quantize_dynamic(f"{art_path}/model.onnx",f"{art_path}/model_quant.onnx",weight_type=QuantType.QUInt8) | |
| else: | |
| pass | |
| def zero_shot_classification_onnx(premise,labels,_session,_tokenizer): | |
| try: | |
| labels=labels.split(',') | |
| labels=[l.lower() for l in labels] | |
| except: | |
| raise Exception("please pass atleast 2 labels to classify") | |
| premise=premise.lower() | |
| labels_prob=[] | |
| for l in labels: | |
| hypothesis= f'this is an example of {l}' | |
| inputs = _tokenizer(premise,hypothesis, | |
| return_tensors='pt', | |
| truncation_strategy='only_first') | |
| input_feed = { | |
| "input_ids": np.array(inputs['input_ids']), | |
| "attention_mask": np.array((inputs['attention_mask'])) | |
| } | |
| output = _session.run(output_names=["logits"],input_feed=dict(input_feed))[0] #returns logits as array | |
| output=torch.from_numpy(output) | |
| entail_contra_prob = output[:,[0,2]].softmax(dim=1)[:,1].item() #only normalizing entail & contradict probabilties | |
| labels_prob.append(entail_contra_prob) | |
| labels_prob_norm=[np.round(100*c/np.sum(labels_prob),1) for c in labels_prob] | |
| df=pd.DataFrame({'labels':labels, | |
| 'Probability':labels_prob_norm}) | |
| return df | |