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SwanLab and Transformers: Power Up Your NLP Experiments

Community Article Published June 17, 2024
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Ankush Singal's avatar
Andyrasika Ankush Singal

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Introduction

The world of Natural Language Processing (NLP) is abuzz with innovation. New models and techniques emerge constantly, making experiment tracking and collaboration crucial for success. This is where SwanLab steps in, offering a powerful integration with the popular Transformers library to streamline your NLP workflow.

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Definitions

  1. SwanLab: An open-source gem for AI experiment tracking. Imagine a lightweight platform that tracks your experiments, compares their results, and fosters collaboration within your team. SwanLab boasts a user-friendly interface and API, making it a breeze to use.

  2. Transformers: This library from Hugging Face provides pre-trained models for various NLP tasks, like text classification, question answering, and sentiment analysis. With Transformers, you can hit the ground running with powerful NLP capabilities.

The Benefits of Integrating SwanLab with Transformers

Here's how this dynamic duo empowers your NLP endeavors:

  1. Effortless Experiment Tracking: SwanLab seamlessly integrates with your Transformers code. With minimal coding, you can track key training metrics, like accuracy or loss, alongside crucial hyperparameters (experiment settings). This meticulous tracking allows you to compare different model configurations and identify the best performers.
  2. Visualize Your Success: SwanLab goes beyond numbers. It offers various chart types, including line graphs, to visualize your experiment's progress. You can even embed images, audio, and text snippets, providing a rich context for your analysis. SwanLab also automatically logs information like GPU hardware and code directory, giving you a complete picture of your experiment setup.
  3. Framework Agnostic: SwanLab plays well with others. It integrates seamlessly with popular deep learning frameworks like PyTorch and TensorFlow, ensuring compatibility with your existing workflow. This extends to Transformers, which is built on top of these frameworks.
  4. Collaboration Made Easy: SwanLab fosters teamwork. Imagine your team working on the same project, all their experiments synchronized in real-time within a central dashboard. This allows for knowledge sharing and fosters iterative improvement based on each other's findings.
  5. Share Your Insights: SwanLab generates persistent URLs for each experiment. Share these links with colleagues or embed them in online notes, making it easy to discuss and showcase your NLP breakthroughs.

Code Implementation

Lets delve into Transformers with SwanLab for experiment tracking and visualization

Step I: Install Libraries 

pip install transformers swanlab datasets evaluate

Step II: Import libraries and create SwanLabCallback instance 

import evaluate
import numpy as np
import swanlab
from swanlab.integration.huggingface import SwanLabCallback
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments


def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)


def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)


dataset = load_dataset("yelp_review_full")

tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")

tokenized_datasets = dataset.map(tokenize_function, batched=True)

small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))

metric = evaluate.load("accuracy")

model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)

training_args = TrainingArguments(
    output_dir="test_trainer",
    report_to="none",
    num_train_epochs=3,
    logging_steps=50,
)

swanlab_callback = SwanLabCallback(experiment_name="TransformersTest", cloud=False)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=small_train_dataset,
    eval_dataset=small_eval_dataset,
    compute_metrics=compute_metrics,
    callbacks=[swanlab_callback],
)

trainer.train()

Conclusion

SwanLab and Transformers are a powerful combination for NLP enthusiasts and researchers. By streamlining experiment tracking, visualization, and collaboration, this duo empowers you to unlock the full potential of your NLP projects. So, dive in, experiment, and watch your NLP journey flourish!

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