In this article, we will explore how to employ the BERT2BERT temporal tagger, an innovative **Seq2Seq model** designed for temporal tagging of plain text using the BERT language model. This model is outlined in the paper “BERT got a Date: Introducing Transformers to Temporal Tagging” and is available in the GitHub repository.

Understanding the Model

BERT, which stands for Bidirectional Encoder Representations from Transformers, is a cutting-edge model that has learned from a vast array of English text data through a method called self-supervised learning. The BERT2BERT model employs an encoder-decoder architecture to effectively generate text where raw input text is transformed into temporally annotated output. Here’s a brief overview:

• The model is pre-trained on weakly annotated data derived from a rule-based system named HeidelTime.
• Further fine-tuning is conducted on benchmark datasets including Wikiwars, Tweets, and Tempeval-3.

How to Use the Model

Let’s walk through using this impressive model step by step. Here’s how you can get started:

Loading the Model

First, you’ll need to set up the tokenizer and the model:

tokenizer = AutoTokenizer.from_pretrained("satya/almasian/temporal_tagger_BERT_tokenclassifier")
model = EncoderDecoderModel.from_pretrained("satya/almasian/temporal_tagger_BERT_tokenclassifier")

Inferences with the Model

To get predictions or generate annotated text, input your raw text as follows:

model_inputs = tokenizer(input_text, truncation=True, return_tensors="pt")
out = model.generate(**model_inputs)
decoded_preds = tokenizer.batch_decode(out, skip_special_tokens=True)

Fine-Tuning the Model

If you have your own temporally annotated data, you can fine-tune the model with the Seq2SeqTrainer from the Hugging Face library. Here’s how:

trainer = Seq2SeqTrainer(
        model=model2model,
        tokenizer=tokenizer,
        args=training_args,
        compute_metrics=metrics.compute_metrics,
        train_dataset=train_data,
        eval_dataset=val_data,
    )
train_result = trainer.train()

Analogy Time: Imagine Cooking a Gourmet Meal

Think of using the BERT2BERT model like preparing an intricate gourmet meal:

• Your ingredients (raw text) need initial preparation (tokenization), which is done by the tokenizer.
• Then the chef (your model) transforms these ingredients into a well-prepared dish (annotated text) through meticulous cooking (training and inference).
• If you’re crafting a special dish tailored to specific tastes (fine-tuning), you can adjust the recipe (parameters) based on feedback (your annotated dataset).

Training Data

The training data for the model consists of:

• Pretraining: 1 million weakly annotated samples from HeidelTime, which are extracted from news articles dated between January 1, 2019, and July 30, 2019.
• Fine-tuning: Datasets include Tempeval-3, Wikiwars, and Tweets.

Training Procedure

The model is trained expertly over several epochs:

• Pretraining is conducted for 3 epochs using a batch size of 12, with a learning rate of 5e-05.
• Fine-tuning is performed for 8 epochs with 5 different random seeds.
• Training utilizes 2 NVIDIA A100 GPUs boasting 40GB of memory.

Troubleshooting Tips

If you are facing issues with the model, keep these tips in mind:

• Ensure you have the correct versions of all required libraries installed.
• If experiencing noisy outputs, implementing the cleaning functions in the provided repository can help clarify results.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.