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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))
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