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| import torch | |
| import streamlit as st | |
| from datasets import load_dataset | |
| from transformers import DistilBertForSequenceClassification, DistilBertTokenizer | |
| decision_to_str = {'REJECTED': 0, 'ACCEPTED': 1, 'PENDING': 2, 'CONT-REJECTED': 3, 'CONT-ACCEPTED': 4, 'CONT-PENDING': 5} | |
| 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='2016-01-21', | |
| val_filing_start_date='2016-01-22', | |
| val_filing_end_date='2016-01-31', | |
| ) | |
| dataset = dataset_dict['validation'] | |
| model_abstract = DistilBertForSequenceClassification.from_pretrained('theresatvan/hupd-distilbert-abstract') | |
| tokenizer_abstract = DistilBertTokenizer.from_pretrained('theresatvan/hupd-distilbert-abstract') | |
| model_claims = DistilBertForSequenceClassification.from_pretrained('theresatvan/hupd-distilbert-claims') | |
| tokenizer_claims = DistilBertTokenizer.from_pretrained('theresatvan/hupd-distilbert-claims') | |
| def predict(model_abstract, model_claims, tokenizer_abstract, tokenizer_claims, input): | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| model_abstract.to(device) | |
| model_claims.to(device) | |
| model_abstract.eval() | |
| model_claims.eval() | |
| abstract, claims = input['abstract'], input['claims'] | |
| encoding_abstract = tokenizer_abstract(abstract, return_tensors='pt', truncation=True, padding='max_length') | |
| encoding_claims = tokenizer_claims(claims, return_tensors='pt', truncation=True, padding='max_length') | |
| input_abstract = encoding_abstract['input_ids'].to(device) | |
| attention_mask_abstract = encoding_abstract['attention_mask'].to(device) | |
| input_claims = encoding_claims['input_ids'].to(device) | |
| attention_mask_claims = encoding_claims['attention_mask'].to(device) | |
| with torch.no_grad(): | |
| outputs_abstract = model_abstract(input_ids=input_abstract) | |
| outputs_claims = model_claims(input_ids=input_claims) | |
| print(outputs_abstract.logits) | |
| print(outputs_claims.logits) | |
| combined_prob = (outputs_abstract.logits.softmax(dim=1) + outputs_claims.logits.softmax(dim=1)) / 2 | |
| label = torch.argmax(combined_prob, dim=1) | |
| return label, combined_prob.tolist()[0] | |
| if __name__ == '__main__': | |
| st.title = "Can I Patent This?" | |
| form = st.form('patent-prediction-form') | |
| dropdown = [example['patent_number'] for example in dataset] | |
| input_application = form.selectbox('Select a patent\'s application number', dropdown) | |
| submit = form.form_submit_button("Submit") | |
| if submit: | |
| input = dataset.filter(lambda e: e['patent_number'] == input_application) | |
| label, prob = predict(model_abstract, model_claims, tokenizer_abstract, tokenizer_claims, input) | |
| st.write(label) | |
| st.write(prob) | |
| st.write(input['decision']) | |