PyTorch trainer and model for Sequence Classification

Last update: Dec 09, 2022

Overview

PyTorch-trainer-and-model-for-Sequence-Classification

After cloning the repository, modify your training data so that the training data is a .csv file and it has 2 columns: Text and Label

In the below example, we will assume that our training data has 3 labels, the name of our training data file is train_data.csv

Example Usage

Import dependencies

import pandas as pd
import numpy as np
from transformers import AutoModel, AutoTokenizer, AutoConfig

from EarlyStopping import *
from modelling import *
from utils import *

Specify arguments

args.pretrained_path will be the path of our pretrained language model

class args:
    fold = 0
    pretrained_path = 'bert-base-uncased'
    max_length = 400
    train_batch_size = 16
    val_batch_size = 64
    epochs = 5
    learning_rate = 1e-5
    accumulation_steps = 2
    num_splits = 5

Create train and validation data

In this example we will train the model using cross-validation. We will split our training data into args.num_splits folds.

df = pd.read_csv('./train_data.csv')
df = create_k_folds(df, args.num_splits)

df_train = df[df['kfold'] == args.fold].reset_index(drop = True)
df_valid = df[df['kfold'] == args.fold].reset_index(drop = True)

Load the language model and its tokenizer

config = AutoConfig.from_pretrained(args.path)
tokenizer = AutoTokenizer.from_pretrained(args.path)
model_transformer = AutoModel.from_pretrained(args.path)

Prepare train and validation dataloaders

features = []
for i in range(len(df_train)):
    features.append(prepare_features(tokenizer, df_train.iloc[i, :].to_dict(), args.max_length))
    
train_dataset = CreateDataset(features)
train_dataloader = create_dataloader(train_dataset, args.train_batch_size, 'train')

features = []
for i in range(len(df_valid)):
    features.append(prepare_features(tokenizer, df_valid.iloc[i, :].to_dict(), args.max_length))
    
val_dataset = CreateDataset(features)
val_dataloader = create_dataloader(val_dataset, args.val_batch_size, 'val')

Use EarlyStopping and customize the score function

NOTE: The customized score function should have 2 parameters: the logits, and the actual label

def accuracy(logits, labels):
    logits = logits.detach().cpu().numpy()
    labels = labels.detach().cpu().numpy()
    pred_classes = np.argmax(logits * (1 / np.sum(logits, axis = -1)).reshape(logits.shape[0], 1), axis = -1)
    pred_classes = pred_classes.reshape(labels.shape)
    
    return np.sum(pred_classes == labels) / labels.shape[0]

es = EarlyStopping(mode = 'max', patience = 3, monitor = 'val_acc', out_path = 'model.bin')
es.monitor_score_function = accuracy

Create and train the model

Calling the fit method, the training process will begin

model = Model(config, model_transformer, num_labels = 3)
model.to('cuda')
num_train_steps = int(len(train_dataset) / args.train_batch_size * args.epochs)
model.fit(args.epochs, args.learning_rate, num_train_steps, args.accumulation_steps, 
          train_dataloader, val_dataloader, es)

NOTE: To complete the cross-validation training process, run the code above again with args.fold equals 1, 2, ..., args.num_splits - 1

PyTorch trainer and model for Sequence Classification

Related tags

Overview

PyTorch-trainer-and-model-for-Sequence-Classification

Example Usage

Import dependencies

Specify arguments

Create train and validation data

Load the language model and its tokenizer

Prepare train and validation dataloaders

Use EarlyStopping and customize the score function

Create and train the model

Owner

NhanTieu

A Python library for common tasks on 3D point clouds

My personal Home Assistant configuration.

The code repository for EMNLP 2021 paper "Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization".

Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)

Official repo for BMVC2021 paper ASFormer: Transformer for Action Segmentation

The King is Naked: on the Notion of Robustness for Natural Language Processing

Implementation of TimeSformer, a pure attention-based solution for video classification

Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning

Official Pytorch implementation of Meta Internal Learning

Bottom-up Human Pose Estimation

Raindrop strategy for Irregular time series

Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Human Pose estimation with TensorFlow framework

Code for 2021 NeurIPS --- Towards Multi-Grained Explainability for Graph Neural Networks

This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark

I created My own Virtual Artificial Intelligence named genesis, He can assist with my Tasks and also perform some analysis,,

CVAT is free, online, interactive video and image annotation tool for computer vision

Some experiments with tennis player aging curves using Hilbert space GPs in PyMC. Only experimental for now.

Automatic differentiation with weighted finite-state transducers.

[SDM 2022] Towards Similarity-Aware Time-Series Classification