I’m training a tokenizer and model from scratch — how do I prepare my dataset and set the correct tokenizer parameters?

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I want to build my own AI language model instead of using a ready-made one. How should I organize my text data, and how do I choose the right settings when creating a tokenizer?

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Hmm, I think that’s going to be quite a difficult challenge…

What you are really building

When you “train a tokenizer + model from scratch,” you are defining the model’s input alphabet and text interface.

  • Tokenizer: deterministic text → token IDs. It has a pipeline (normalization → pre-tokenization → model → post-processing). (Hugging Face)
  • Language model: learns patterns over those token IDs.

If your tokenizer choices are inconsistent (whitespace, special tokens, fast vs slow behavior), you do not just get “slightly worse results.” You can get a model that learns unstable conventions or fails at inference.

Step 1: Decide the training objective first (it drives tokenizer choices)

Most “build my own language model” projects mean decoder-only causal LM (GPT-style) for generation.

  • Causal LM: predict next token. HF has an explicit “train a causal language model from scratch” walkthrough that follows the tokenizer→dataset→training flow. (Hugging Face)
  • Masked LM (BERT-style): predict masked tokens. This changes your required special tokens and data collator.

If you want text generation, pick causal LM unless you have a specific reason not to.

Step 2: Prepare the dataset so the tokenizer learns the right statistics

2.1 Pick a “document unit” and preserve boundaries

A tokenizer is trained on counts and co-occurrences. If you smash everything into one giant string without clear boundaries, you distort statistics around:

  • sentence starts
  • paragraph breaks
  • document separators
  • boilerplate repetition

Practical recommendation:

  • Store one document per record (JSONL is easiest).
  • Keep a single text field plus optional metadata (id, source, lang, license, timestamps).

This makes it easy to:

  • filter later
  • deduplicate
  • split train/validation cleanly

2.2 Clean, but do not over-clean

Your goal is “representative, high-quality text,” not “sterile text.”

Do:

  • fix encoding to UTF-8
  • remove empty/too-short docs
  • remove obvious boilerplate / spam patterns

Avoid:

  • aggressive punctuation stripping
  • collapsing whitespace if formatting matters (code, Markdown, lists)
  • lowercasing everything unless you will always lowercase at inference

2.3 Deduplicate early (before you trust any metrics)

Near-duplicates are common in web corpora and can contaminate validation. The ACL paper “Deduplicating Training Data Makes Language Models Better” reports that LM datasets contain many near-duplicates and that dedup reduces memorization and train–test overlap. (ACL Anthology)

Practical tools and references:

  • DataTrove: library designed to process, filter, and deduplicate text at very large scale. (GitHub)
  • HF blog on MinHash near-dedup (BigCode): explains a real near-dedup workflow and scaling pitfalls. (Hugging Face)
  • FineWeb dataset card explicitly states the CommonCrawl data was processed, filtered, and deduplicated with DataTrove. (Hugging Face)

If you do one “data engineering” thing well, do dedup.

2.4 Split into train/validation in a leakage-resistant way

Basic rule:

  • Validation must not share duplicates with training.
  • If you dedup, do it before split or do it consistently across splits.

2.5 Shard and stream if your corpus is large

Hugging Face Datasets supports streaming, which creates an IterableDataset (lazy, scalable iteration). (Hugging Face)
This matters if you are at “hundreds of GB” scale. HF explicitly says IterableDataset is ideal for very large datasets due to lazy behavior and speed advantages. (Hugging Face)

Step 3: Choose the tokenizer family (the safe default vs special cases)

You typically choose among:

  • Byte-level BPE (GPT-2 / RoBERTa style families): robust to messy text because it can represent arbitrary bytes. HF’s ByteLevel pre-tokenizer is documented as operating on bytes and splitting into words. (Hugging Face)
  • WordPiece (BERT): good for masked LM; classic ## continuation behavior (works best with whitespace-delimited languages).
  • Unigram (SentencePiece-style): often strong for multilingual and “no-whitespace segmentation” languages.

My default recommendation

If you are training a decoder-only causal LM and your data is general web text or mixed content, start with byte-level BPE.

If your corpus is primarily Japanese and you want segmentation that is less whitespace-dependent, Unigram is worth testing, but then you must be extra careful about implementation consistency (see pitfalls below).

Step 4: Understand tokenizer parameters as a pipeline

HF Tokenizers is explicit: a tokenizer is built by combining components (normalizers, pre-tokenizers, trainers, post-processors). (Hugging Face)
This is the mental model you want.

4.1 Normalization (what text is “the same”?)

Normalization happens first. Examples include Unicode normalization (like NFKC), lowercasing, accent stripping.

Rule:

  • Only normalize what you will also normalize at inference.

If you normalize aggressively, you reduce the model’s ability to distinguish forms.

4.2 Pre-tokenization (where can tokens start/end?)

This decides the “candidate boundaries” before BPE/WordPiece merges.

For byte-level BPE, HF documents the ByteLevel pre-tokenizer with parameters including add_prefix_space. (Hugging Face)
add_prefix_space=True means “add a space to the first word if there isn’t already one,” so "hello" behaves more like " hello". (Hugging Face)

This is not a cosmetic option. It changes the learned statistics at sentence starts.

4.3 Trainer knobs (the ones that actually matter)

HF’s trainer docs list the key training arguments:

  • vocab_size
  • min_frequency
  • special_tokens
  • (sometimes) limit_alphabet (Hugging Face)

Interpretation:

  • vocab_size: bigger vocab → shorter sequences but larger embedding matrix.
  • min_frequency: drops rare merges; too high can remove domain terms.
  • special_tokens: must be reserved up front.

HF’s quicktour emphasizes that providing special_tokens is critical so they are inserted into the vocabulary. (Hugging Face)

4.4 Special tokens (your “control language”)

Tokenizers docs state special tokens:

  • will never be processed by the model (not split)
  • can be removed when decoding (Hugging Face)

In practice, special tokens are where many real-world bugs happen because they appear adjacent to text and newlines.

For a causal LM, you almost always want:

  • eos_token (end of document / separator)
  • pad_token for batching (often set to EOS in causal setups, but do this intentionally)

For a masked LM, you need:

  • mask_token plus CLS/SEP/PAD/UNK conventions.

4.5 Post-processing (adding tokens like CLS/SEP, trimming offsets)

If you use a RoBERTa-like scheme, HF’s RobertaProcessing post-processor:

  • adds SEP and CLS
  • can trim offsets because ByteLevel BPE may include whitespace in offsets (trim_offsets=True) (Hugging Face)

Even if you do not need offsets today, offset correctness matters for any future alignment tasks.

Step 5: “Correct” initial parameter settings you can start with

There is no universally correct setting. There are safe starting points.

5.1 If you train a decoder-only causal LM (recommended baseline)

Tokenizer: Byte-level BPE

  • Pre-tokenizer: ByteLevel

    • Start with add_prefix_space=True unless you have a strong compatibility reason not to. (Then test sentence-start behavior.)

  • Trainer

    • vocab_size: start at 32k or 50k
    • min_frequency: start at 2–5
    • special_tokens: include your EOS and any structural markers you will use

These are not magic numbers. They are “reasonable defaults” that you then validate by measuring:

  • average tokens per document
  • fraction of samples hitting max length
  • qualitative splits for domain words, URLs, code tokens, emoji

5.2 If you train an MLM (BERT-like)

Tokenizer: typically WordPiece

  • Ensure you reserve [MASK] [CLS] [SEP] [PAD] [UNK] (or your equivalents).
  • Expect the tokenizer to depend more on whitespace and punctuation boundaries.

Step 6: Train the tokenizer in a way that matches your dataset scale

HF Tokenizers supports training from:

  • files
  • or any Python iterator (train_from_iterator) (Hugging Face)

If you are using Datasets streaming (IterableDataset), iterator-based training is the natural fit.

Step 7: Wire the tokenizer to model training (HF “from scratch” workflow)

Two reliable references:

  • HF blog “How to train a new language model from scratch”: find dataset → train tokenizer → train LM → validate. (Hugging Face)

  • Transformers example scripts:

    • run_clm.py for causal LM (GitHub)
    • run_mlm.py for masked LM (GitHub)

Also, the HF course chapter on causal LM from scratch demonstrates applying a tokenizer to a corpus and training with Trainer/Accelerate. (Hugging Face)

Step 8: Common pitfalls you should actively test for

These are frequent “it trained but behaves weird” failures.

8.1 Whitespace surprises (especially sentence start)

  • Byte-level BPE can treat "hello" and " hello" differently. add_prefix_space changes this behavior. (Hugging Face)
  • add_prefix_space can also interact with added tokens and decoding, producing unexpected spaces (real issue report). (GitHub)

Test:

  • tokenization of first word in a document
  • encode→decode roundtrip on sentence starts

8.2 Special tokens splitting in edge cases

Even though special tokens are intended not to split, real edge cases exist, such as when no space precedes a special token and certain pipelines are used (documented issue). (GitHub)
There are also reports of whitespace behavior around special tokens in some “slow” paths. (Hugging Face Forums)

Test:

  • <SPECIAL>\ntext
  • text<SPECIAL>text
  • special token adjacency with CJK characters

8.3 Fast vs slow differences and byte fallback

Transformers documents that many tokenizers exist in slow (Python) and fast (Tokenizers-backed) forms. (Hugging Face)
There are known issues where SentencePiece tokenizers using byte fallback do not convert cleanly to fast tokenizers (byte fallback missing), affecting OOV handling. (GitHub)

If you use SentencePiece-style tooling, test unknown characters early.

8.4 Dataset duplication inflating eval and encouraging memorization

This is common enough that you should assume it is present unless proven otherwise. (ACL Anthology)

Step 9: The “version reality” in late 2025

Transformers v5 introduces a major tokenization redesign and explicitly states you can train model-specific tokenizers from scratch more directly than before. (Hugging Face)
At the same time, there are active tokenization bug reports against v5 RC builds. (GitHub)

Practical implication:

  • If you want maximum stability, pin to a stable v4 stack.
  • If you adopt v5 RC, add tokenizer regression tests (encode/decode, special tokens, whitespace) to CI.

A concrete “do this first” plan

  1. Pick causal LM + byte-level BPE as your baseline.

  2. Build dataset as JSONL with one doc per record, split train/validation, dedup early. (GitHub)

  3. Train tokenizer with:

    • ByteLevel pre-tokenizer (add_prefix_space decided by tests) (Hugging Face)
    • BPE trainer with vocab_size=32k–50k, min_frequency=2–5, and reserved special tokens (Hugging Face)

  4. Run tests:

    • whitespace at sentence start
    • special token adjacency
    • encode→decode roundtrip
    • OOV behavior if using SentencePiece-style conversion (GitHub)

  5. Only then start pretraining with run_clm.py. (GitHub)

High-signal guides and docs (keep these open)

Summary

  • Organize text as documents, not a soup. Split train/validation. Dedup early. (ACL Anthology)
  • Treat tokenizer as a pipeline (normalizer → pre-tokenizer → trainer → post-processing). (Hugging Face)
  • Start with byte-level BPE for a causal LM unless you have a strong language-specific reason not to. (Hugging Face)
  • The “big knobs” are vocab_size, min_frequency, special_tokens, and whitespace behavior (add_prefix_space). (Hugging Face)
  • Run explicit tests for whitespace, special tokens, and fast-vs-slow behavior before you spend compute. (GitHub)