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| from transformers import pipeline, DistilBertTokenizerFast, DistilBertForSequenceClassification | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.utils.data import DataLoader, Dataset | |
| from datasets import load_dataset | |
| import streamlit as st | |
| # load tokenizer and fine-tuned model | |
| save_diirectory = "saved" | |
| model = DistilBertForSequenceClassification.from_pretrained(save_diirectory) | |
| model.eval() | |
| tokenizer = DistilBertTokenizerFast.from_pretrained(save_diirectory) | |
| # method to transform dataset to (patent_numbers, abstracts, claims, texts, labels), labels are 1/0 from decision | |
| def dataset_to_lists(dataset): | |
| patent_numbers = [] | |
| abstracts = [] | |
| claims = [] | |
| texts = [] | |
| labels = [] | |
| for data in dataset: | |
| patent_number = data['patent_number'] | |
| abstract = data['abstract'] | |
| claim = data['claims'] | |
| text = data['abstract'] + data['claims'] | |
| label = 1 if data['decision'] == 'ACCEPTED' else 0 | |
| patent_numbers.append(patent_number) | |
| abstracts.append(abstract) | |
| claims.append(claim) | |
| texts.append(text) | |
| labels.append(label) | |
| return patent_numbers, abstracts, claims, texts, labels | |
| # dataset class to fit in dataloader | |
| class TextEncodeDataset(Dataset): | |
| def __init__(self, encodings, labels): | |
| self.encodings = encodings | |
| self.labels = labels | |
| def __len__(self): | |
| return len(self.labels) | |
| def __getitem__(self, idx): | |
| item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} | |
| item['labels'] = torch.tensor(self.labels[idx]) | |
| return item | |
| # load dataset, only focus on "patent_number", "abstract", "claims", "decision" at Jan | |
| dataset_dict = load_dataset('HUPD/hupd', | |
| name='sample', | |
| data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather", | |
| icpr_label=None, | |
| train_filing_start_date='2016-01-01', | |
| train_filing_end_date='2016-01-31', | |
| val_filing_start_date='2016-02-01', | |
| val_filing_end_date='2016-02-01', | |
| ) | |
| dataset = dataset_dict['train'] | |
| dataset.set_format(type="torch", columns=["patent_number", "abstract", "claims", "decision"]) | |
| # transform dataset to lists of data | |
| patent_numbers, abstracts, claims, texts, labels = dataset_to_lists(dataset) | |
| # select a patent_number and get relevant info | |
| patent_number = str(st.selectbox('select a patent', patent_numbers)) | |
| selected_idx = patent_numbers.index(patent_number) | |
| abstract = abstracts[selected_idx] | |
| claim = claims[selected_idx] | |
| text = texts[selected_idx] | |
| label = labels[selected_idx] | |
| # display abstract and claim | |
| st.write('abstract: ' + abstract) | |
| st.write('claim: ' + claim) | |
| # get encoding text | |
| encoding = tokenizer(text, truncation=True, padding=True) | |
| # click to make prediction | |
| if st.button('Run'): | |
| with torch.no_grad(): | |
| output = model(torch.tensor(encoding['input_ids']).unsqueeze(dim=0), attention_mask=torch.tensor(encoding['attention_mask']).unsqueeze(dim=0), labels=torch.tensor(label)) | |
| predictions = F.softmax(output.logits, dim=1) | |
| score = predictions[0][1].item() | |
| st.write('score: ' + str(score)) | |
| st.write('actual result: ' + str(label)) | |
| st.write('output: ' + str(output)) | |