Principle:Neuml Txtai Training Data Preparation

Knowledge Sources	txtai txtai Documentation BERT
Domains	Deep_Learning, Training, NLP
Last Updated	2026-02-09 00:00 GMT

Overview

Training data preparation is the process of converting raw text datasets into tokenized, model-ready tensors that can be consumed by a transformer training loop. Different NLP tasks -- classification, question answering, sequence-to-sequence generation, and language modeling -- each demand a distinct tokenization strategy, column mapping, and output schema. Getting this step right is a prerequisite for successful fine-tuning.

Description

Modern transformer models do not operate on raw strings. Before any gradient update can occur, every training example must be:

1. Mapped -- the correct columns in the source dataset must be identified (e.g., which column holds the question, which holds the context, which holds the label).
2. Tokenized -- strings must be converted to integer token IDs via a task-appropriate tokenizer, respecting maximum sequence lengths, padding, and truncation rules.
3. Formatted -- the resulting tensors must include the fields the model's forward pass expects (e.g., input_ids, attention_mask, labels, start_positions / end_positions).

The specifics vary by task:

• Text classification -- a single text (or text pair) is tokenized and a numeric label is attached.
• Question answering -- a question and context are tokenized jointly with stride-based chunking; character-level answer spans are converted to token-level start/end positions.
• Sequence-to-sequence -- source and target texts are tokenized independently; the target token IDs become the labels field.
• Language modeling -- text is tokenized and concatenated into fixed-length chunks; no explicit labels are needed because the model predicts the next (or masked) token from the input itself.

Usage

Training data preparation should be applied whenever a practitioner needs to fine-tune a transformer model on a custom dataset. Typical scenarios include:

• Fine-tuning a BERT-style model for sentiment analysis or topic classification.
• Training an extractive QA model on a domain-specific question-answer corpus.
• Adapting a T5 or BART model for summarization or translation via sequence-to-sequence training.
• Continuing pretraining of a causal or masked language model on domain text.

In all cases the raw dataset (a HuggingFace Dataset, a pandas/Polars DataFrame, or an iterable of dicts) must be transformed into tokenized tensors before being handed to the trainer.

Theoretical Basis

The theoretical foundation rests on the subword tokenization algorithms used by modern transformers (BPE, WordPiece, SentencePiece). These algorithms decompose words into smaller units that balance vocabulary size against sequence length, enabling the model to handle out-of-vocabulary words gracefully.

Pseudocode for the general data preparation pipeline:

FUNCTION prepare_training_data(dataset, tokenizer, task, columns, maxlength):
    FOR EACH batch IN dataset:
        IF task == "classification":
            tokens = tokenizer(batch[text_col], max_length=maxlength, truncation=True, padding=True)
            tokens["label"] = batch[label_col]
        ELIF task == "question-answering":
            tokens = tokenizer(batch[question_col], batch[context_col],
                               max_length=maxlength, stride=stride,
                               return_overflowing_tokens=True,
                               return_offsets_mapping=True)
            tokens["start_positions"], tokens["end_positions"] = map_char_spans_to_token_spans(batch, tokens)
        ELIF task == "seq2seq":
            tokens = tokenizer(batch[source_col], max_length=maxlength, truncation=True)
            tokens["labels"] = tokenizer(batch[target_col])["input_ids"]
        ELIF task == "language-modeling":
            tokens = tokenizer(batch[text_col])
            tokens = concatenate_and_chunk(tokens, maxlength)
        RETURN tokens

Key theoretical considerations:

• Padding vs. truncation -- sequences shorter than maxlength are padded; those longer are truncated. The choice of padding strategy (static vs. dynamic) affects both memory efficiency and training speed.
• Stride for QA -- long contexts are split into overlapping windows (controlled by stride) so that the answer span is never lost at a chunk boundary.
• Prefix injection for seq2seq -- models like T5 expect a task-specific prefix (e.g., "translate English to French: ") prepended to every source string.

Related Pages

Implemented By

• Implementation:Neuml_Txtai_Data_Tokenizers