Xingjian Shi sxjscience

California House Prices Prediction

Install AutoGluon (I used version==0.1 in the submission and you may try the latest version, which may give you a better performance).

pip install autogluon

	from autogluon.multimodal import MultiModalPredictor
	from datasets import load_dataset
	import json
	import os
	import time
	import argparse

	PAWS_X_LANGUAGE_L = ['en', 'fr', 'es', 'de', 'zh', 'ja', 'ko']

	os.makedirs("data_cache", exist_ok=True)

	from autogluon.multimodal import MultiModalPredictor
	from datasets import load_dataset
	import json
	import os
	import time
	import argparse

	train_data = load_dataset("glue", 'mrpc')['train'].to_pandas().drop('idx', axis=1)
	test_data = load_dataset("glue", 'mrpc')['validation'].to_pandas().drop('idx', axis=1)
	label = 'label'

	import argparse
	import os
	from auto_mm_bench.datasets_with_image import dataset_with_image_registry, create_dataset
	from autogluon.core.features.feature_metadata import FeatureMetadata
	from autogluon.tabular import TabularPredictor

	BASELINE_HPARAMS = {
	'FASTAI': {},
	}

	import time
	import torch
	from transformers import GPTNeoForCausalLM, AutoConfig, GPT2Tokenizer
	import torch
	import hashlib
	import transformers
	import argparse
	import collections
	import os
	import logging

	import os
	from PIL import Image

	base_dir = 'image manually'
	with open('label_file.txt', 'w') as of:
	for filename in os.listdir(base_dir):
	print('Labeling filename', filename)
	img = Image.open(os.path.join(base_dir, filename))
	img.show()
	label = input('Flood = 0, Non-flood=1: ')

	import pandas as pd
	import numpy as np
	import argparse
	import os
	import json
	import random
	from autogluon.tabular import TabularPredictor
	from autogluon.text import TextPredictor
	from autogluon.text.text_prediction.infer_types import infer_column_problem_types
	from autogluon.text.text_prediction import constants as _C

	export SQUAD_DIR=/home/ubuntu/squad
	python3 -m torch.distributed.launch --nproc_per_node=4 ./examples/question-answering/run_squad.py \
	--model_type albert \
	--model_name_or_path albert-base-v2 \
	--do_train \
	--do_eval \
	--version_2_with_negative \
	--train_file $SQUAD_DIR/train-v2.0.json \
	--predict_file $SQUAD_DIR/dev-v2.0.json \
	--learning_rate 3e-5 \

	FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04

	LABEL maintainer="GluonNLP Team"

	ARG DEBIAN_FRONTEND=noninteractive

	ENV PYTHONDONTWRITEBYTECODE=1 \
	PYTHONUNBUFFERED=1 \
	LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib" \
	PYTHONIOENCODING=UTF-8 \