from datasets import load_dataset import pandas as pd import numpy as np import os import json import torch import sys from torch.utils.data import Dataset, DataLoader from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification from transformers import Trainer, TrainingArguments, AdamW torch.backends.cuda.matmul.allow_tf32 = True model_name = "distilbert-base-uncased" upsto_abstracts_model_path = './models/uspto_abstracts' upsto_claims_model_path = './models/uspto_claims' class USPTODataset(Dataset): def __init__(self, encodings, labels): self.encodings = encodings self.labels = 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 def __len__(self): return len(self.labels) def LoadDataset(): print("=== LOADING THE DATASET ===") # Extracting the dataset, filtering only for Jan. 2016 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', ) print("Separating between training and validation data") df_train = pd.DataFrame(dataset_dict['train'] ) df_val = pd.DataFrame(dataset_dict['validation'] ) print("=== PRE-PROCESSING THE DATASET ===") #We are interested in the following columns: # - Abstract # - Claims # - Decision <- our `y` # Let's preprocess them both out of our training and validation data # Also, consider that the "Decision" column has three types of values: "Accepted", "Rejected", and "Pending". To remove unecessary baggage, we will be only looking for "Accepted" and "Rejected". necessary_columns = ["abstract","claims","decision"] output_values = ['ACCEPTED','REJECTED'] print("Dropping unused columns") trainFeaturesToDrop = [col for col in list(df_train.columns) if col not in necessary_columns] trainDF = df_train.dropna() trainDF.drop(columns=trainFeaturesToDrop, inplace=True) trainDF = trainDF[trainDF['decision'].isin(output_values)] valFeaturesToDrop = [col for col in list(df_val.columns) if col not in necessary_columns] valDF = df_val.dropna() valDF.drop(columns=valFeaturesToDrop, inplace=True) valDF = valDF[valDF['decision'].isin(output_values)] # We need to replace the values in the `decision` column to numerical representations. ] # We will set "ACCEPTED" as `1` and "REJECTED" as `0`. print("Replacing values in `decision` column") yKey = {"ACCEPTED":1,"REJECTED":0} trainDF2 = trainDF.replace({"decision": yKey}) valDF2 = valDF.replace({"decision": yKey}) # We re-label the `decision` column to `label`. print("Renaming `decision` to `label`") trainDF3 = trainDF2.rename(columns={'decision': 'label'}) valDF3 = valDF2.rename(columns={'decision': 'label'}) # We can grab the data for each column so that we have a list of values for training labels, # training texts, validation labels, and validation texts. print("Extracting label and text data from dataframes") trainData = { "labels":trainDF3["label"].tolist(), "abstracts":trainDF3["abstract"].tolist(), "claims":trainDF3["claims"].tolist(), } valData = { "labels":valDF3["label"].tolist(), "abstracts":valDF3["abstract"].tolist(), "claims":valDF3["claims"].tolist(), } #print(f'TRAINING:\t# labels: {len(trainData["labels"])}\t# texts: {len(trainData["text"])}') #print(f'VALID:\t# labels: {len(valData["labels"])}\t# texts: {len(valData["text"])}') if not os.path.exists("./data"): os.makedirs('./data') with open("./data/train.json", "w") as outfile: json.dump(trainData, outfile, indent=2) with open("./data/val.json", "w") as outfile: json.dump(valData, outfile, indent=2) return trainData, valData def TrainModel(trainData, valData): print("=== ENCODING DATA ===") #print(len(trainData["labels"]), len(trainData["text"]), len(valData["labels"]), len(valData["text"])) print("\t- initializing tokenizer") tokenizer = DistilBertTokenizerFast.from_pretrained(model_name) print("\t- encoding training data") train_abstracts_encodings = tokenizer(trainData["abstracts"], truncation=True, padding=True) train_claims_encodings = tokenizer(trainData["claims"], truncation=True, padding=True) #print("\t- encoding validation data") #val_abstracts_encodings = tokenizer(valData["abstracts"], truncation=True, padding=True) #val_claims_encodings = tokenizer(valData["claims"], truncation=True, padding=True) print(trainData["abstracts"][:10]) print(trainData["labels"][:10]) print("=== CREATING DATASETS ===") print("\t- initializing dataset for training data") train_abstracts_dataset = USPTODataset(train_abstracts_encodings, trainData["labels"]) train_claims_dataset = USPTODataset(train_claims_encodings, trainData["labels"]) #print("\t- initializing dataset for validation data") #val_abstracts_dataset = USPTODataset(val_abstracts_encodings, valData["labels"]) #val_claims_dataset = USPTODataset(val_claims_encodings, valData["labels"]) print("=== PREPARING MODEL ===") print("\t- setting up device") device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') print("\t- initializing model") model = DistilBertForSequenceClassification.from_pretrained(model_name) model.to(device) model.train() print("== PREPARING TRAINING ===") print("\t- initializing trainers") train_abstracts_loader = DataLoader(train_abstracts_dataset, batch_size=4, shuffle=True) train_claims_loader = DataLoader(train_claims_dataset, batch_size=4, shuffle=True) #train_claims_loader = DataLoader(train_claims_dataset, batch_size=4, shuffle=True) print("\t- initializing optim") optim = AdamW(model.parameters(), lr=5e-5) def Train(loader, save_path, num_train_epochs=2): batch_num = len(loader) for epoch in range(num_train_epochs): print(f'\t- Training epoch {epoch+1}/{num_train_epochs}') batch_count = 0 for batch in loader: print(f'{batch_count}|{batch_num} - {round((batch_count/batch_num)*100)}%', end="") #print('\t\t- optim zero grad') optim.zero_grad() #print('\t\t- input_ids') input_ids = batch['input_ids'].to(device) #print('\t\t- attention_mask') attention_mask = batch['attention_mask'].to(device) #print('\t\t- labels0') labels = batch['labels'].to(device) #print('\t\t- outputs') outputs = model(input_ids, attention_mask=attention_mask, labels=labels) #print('\t\t- loss') loss = outputs[0] #print('\t\t- backwards') loss.backward() #print('\t\t- step') optim.step() batch_count += 1 print("\r", end="") model.eval() model.save_pretrained(save_path, from_pt=True) print(f'Saved model in {save_path}!') print("=== TRAINING ABSTRACTS ===") Train(train_abstracts_loader,upsto_abstracts_model_path) print("=== TRAINING CLAIMS ===") Train(train_claims_loader,upsto_claims_model_path) def main(): trainDataPath = "./data/train.json" valDataPath = "./data/val.json" trainData = None valData = None if os.path.exists(trainDataPath) and os.path.exists(valDataPath): print("Loading from existing data files") ftrain = open(trainDataPath) trainData = json.load(ftrain) ftrain.close() fval = open(valDataPath) valData = json.load(fval) fval.close() else: trainData, valData = LoadDataset() #print(len(trainData["labels"]), len(trainData["text"]), len(valData["labels"]), len(valData["text"])) print("Data loaded successfully!") TrainModel(trainData, valData) """ train_args = TrainingArguments( output_dir="./results", num_train_epochs=2, per_device_train_batch_size=16, per_device_eval_batch_size=64, warmup_steps=500, learning_rate=5e-5, weight_decay=0.01, logging_dir="./logs", logging_steps=10 ) model = DistilBertForSequenceClassification.from_pretrained(model_name) trainer = Trainer( model=model, args=train_args, train_dataset=train_dataset, eval_dataset=val_dataset ) trainer.train() """ if __name__ == "__main__": main()