paranormal-or-skeptic-ISI-public/generate.py

import pandas as pd
from transformers import BertTokenizer, AdamW, AutoModelForSequenceClassification
import torch
import matplotlib.pyplot as plt
from torch.utils.data import TensorDataset, DataLoader, RandomSampler
import torch.nn as nn
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
from model import BERT_Arch
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from transformers import BertTokenizerFast, BertForSequenceClassification
from transformers import Trainer, TrainingArguments

class Dataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}
        item["labels"] = torch.tensor([self.labels[idx]])
        return item

    def __len__(self):
        return len(self.labels)

device = torch.device('cuda')

train_texts = \
    pd.read_csv('train/in.tsv.xz', compression='xz', sep='\t', header=None, error_bad_lines=False, quoting=3)[0].tolist()[:1000]
train_labels = pd.read_csv('train/expected.tsv', sep='\t', header=None, quoting=3)[0].tolist()[:1000]
dev_texts = pd.read_csv('dev-0/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)[0].tolist()[:1000]
dev_labels = pd.read_csv('dev-0/expected.tsv', sep='\t', header=None, quoting=3)[0].tolist()[:1000]
model_name = "bert-base-uncased"

model = BertForSequenceClassification.from_pretrained(
    model_name, num_labels=len(pd.unique(train_labels))).to(device)
max_length = 512
tokenizer = BertTokenizerFast.from_pretrained(model_name, do_lower_case=True)

# model.load_pretrained(model_path)
# tokenizer.load_pretrainded(model_path)

train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=max_length)
valid_encodings = tokenizer(dev_texts, truncation=True, padding=True, max_length=max_length)

input_ids = torch.tensor(valid_encodings.data['input_ids'])[:100]
attention_mask = torch.tensor(valid_encodings.data['attention_mask'])[:100]

with torch.no_grad():
  preds = model(input_ids.to(device), attention_mask.to(device))
  preds = preds.logits.detach().cpu().numpy()
  preds = np.argmax(preds, axis = 1)
    
print(preds)
print(classification_report(dev_labels, preds))
print(accuracy_score(dev_labels, preds))