| import os | |
| import pandas as pd | |
| import streamlit as st | |
| HG_DIR = '/nlp/scr/msuzgun/cache_extra/huggingface' | |
| # Specify HG cache dirs -- currently use only for 2.7b model | |
| os.environ['TRANSFORMERS_CACHE'] = f'{HG_DIR}/transformers' | |
| os.environ['HF_HOME'] = HG_DIR | |
| ## Import relevant libraries and dependencies | |
| !pip install datasets | |
| !pip install Transformers | |
| !pip install streamlit | |
| # Pretty print | |
| from pprint import pprint | |
| # Datasets load_dataset function | |
| from datasets import load_dataset | |
| # Transformers Autokenizer | |
| from transformers import AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') | |
| # Standard PyTorch DataLoader | |
| from torch.utils.data import DataLoader | |
| dataset_dict = load_dataset('HUPD/hupd', | |
| name='sample', | |
| data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather", | |
| cache_dir ='/u/scr/nlp/data/HUPD', | |
| icpr_label=None, | |
| train_filing_start_date='2016-01-01', | |
| train_filing_end_date='2016-01-31', | |
| val_filing_start_date='2017-01-01', | |
| val_filing_end_date='2017-01-31', | |
| ) | |
| df = pd.DataFrame.from_dict(dataset_dict["train"]) | |
| # Create a DataFrame object from list | |
| df = pd.DataFrame(df,columns =['patent_number','decision', 'abstract', 'claims','filing_date']) | |
| st.dataframe(df) | |