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

An official implementation of the Anchor DETR.

(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

Code for paper Novel View Synthesis via Depth-guided Skip Connections

All of the figures and notebooks for my deep learning book, for free!

This repo in the implementation of EMNLP'21 paper "SPARQLing Database Queries from Intermediate Question Decompositions" by Irina Saparina, Anton Osokin

Official implementation of Rethinking Graph Neural Architecture Search from Message-passing (CVPR2021)

Jax/Flax implementation of Variational-DiffWave.

Code for "Long Range Probabilistic Forecasting in Time-Series using High Order Statistics"

C3d-pytorch - Pytorch porting of C3D network, with Sports1M weights

Super Resolution for images using deep learning.

[CVPR 2022] Unsupervised Image-to-Image Translation with Generative Prior

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

This repository contains python code necessary to replicated the experiments performed in our paper "Invariant Ancestry Search"

Efficient Training of Audio Transformers with Patchout

Motion and Shape Capture from Sparse Markers

A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

A library for optimization on Riemannian manifolds

CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors

A Closer Look at Reference Learning for Fourier Phase Retrieval

Implementation of "Efficient Regional Memory Network for Video Object Segmentation" (Xie et al., CVPR 2021).