I want to build a Pre-trained BERT model using my own sentence dataset. Is there any example I can refer to?

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Hi. I just started studying LLM and I think I know the basic principle of transformer structure. However, still I got problem when I tried to build it myself. Now, I tried to build Pre-trained BERT Model based on company dataset. While there are many code examples on the internet that use BERT for classification or use pre-trained modes, it is very difficult to find examples that show how to create a pre-trained model using my own dataset and implement tasks like Next Token Prediction or MLM.

If you have any related code examples on blogs, YouTube, or GitHub, could you please share them?

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Training BERT on Your Own Dataset: A Step-by-Step Guide

Hi cylanokim, hereā€™s a step-by-step guide to train a BERT model on your own sentence dataset and implement tasks like Masked Language Modeling (MLM) and Next Token Prediction (NTP).

  1. Install the Required Libraries

First, make sure you have Transformers and Datasets installed:

!pip install transformers datasets

  1. Import the Necessary Libraries

from transformers import BertTokenizer, BertForMaskedLM, Trainer, TrainingArguments
from datasets import load_dataset

  1. Load Your Custom Dataset

Make sure your dataset is in a format that Hugging Faceā€™s datasets library can process (CSV, JSON, or direct text).

Hereā€™s how to load your dataset (replace ā€œyour_dataset_hereā€ with your actual dataset path):

dataset = load_dataset(ā€œyour_dataset_hereā€) # Replace with your dataset

  1. Initialize the BERT Tokenizer

Use the BERT tokenizer to convert your raw text into tokens:

tokenizer = BertTokenizer.from_pretrained(ā€˜bert-base-uncasedā€™)

  1. Tokenize the Dataset

Define a function to tokenize the text and apply it to your dataset:

def tokenize_function(examples):
return tokenizer(examples[ā€˜textā€™], padding=ā€œmax_lengthā€, truncation=True)

Apply the tokenizer to your dataset
tokenized_datasets = dataset.map(tokenize_function, batched=True)

  1. Load the Pre-trained BERT Model with MLM Head

Now, load the BERT model that will perform Masked Language Modeling (MLM):

model = BertForMaskedLM.from_pretrained(ā€œbert-base-uncasedā€)

  1. Define the Training Arguments

Set up the training parameters, like the number of epochs, batch size, and logging steps:

training_args = TrainingArguments(
output_dir=ā€˜./resultsā€™, # Output directory
num_train_epochs=3, # Number of epochs
per_device_train_batch_size=16, # Batch size for training
per_device_eval_batch_size=64, # Batch size for evaluation
evaluation_strategy=ā€œepochā€, # Evaluate once per epoch
logging_dir=ā€˜./logsā€™, # Directory for logs
save_steps=500, # Save model every 500 steps
logging_steps=250, # Log every 250 steps
load_best_model_at_end=True, # Save the best model at the end
metric_for_best_model=ā€œeval_lossā€, # Track eval loss for best model
greater_is_better=False, # Lower loss is better
seed=42, # Set random seed for reproducibility
)

  1. Set Up the Trainer

Now, define the Trainer to handle the actual training:

trainer = Trainer(
model=model, # The model to train
args=training_args, # Training arguments
train_dataset=tokenized_datasets[ā€œtrainā€], # Training dataset
eval_dataset=tokenized_datasets[ā€œtestā€], # Evaluation dataset
)

  1. Start the Training Process

Youā€™re ready to train the model:

trainer.train()

  1. Save the Trained Model and Tokenizer

Once training is complete, make sure to save your trained model and tokenizer:

model.save_pretrained(ā€œ./final_modelā€)
tokenizer.save_pretrained(ā€œ./final_modelā€)

print(ā€œTraining complete and model saved!ā€)

Additional Notes:

Dataset: Ensure that your dataset is formatted properly (i.e., each sentence or text should be in the 'text' column of your dataset).

Learning Rate: If you're facing any issues with training stability or convergence, adjusting the learning rate to 5e-5 can help.

Fine-Tuning: You can always fine-tune the model for specific tasks like classification, token classification, etc., by adjusting the modelā€™s head after pretraining.

Helpful Resources:

Hugging Face BERT Training Documentation

BERT Fine-tuning Guide on Hugging Face

I hope this helps you get started on training your BERT model with your own dataset. Let me know if you need more assistance!

Solution generated by Triskel Data Deterministic Ai

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