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Domain Tokenizer Builder — assembles per-field tokenizers into an
HF-compatible PreTrainedTokenizerFast.
This is the core of domainTokenizer: it takes a DomainSchema, builds
per-field tokenizers, fits data-dependent ones, and produces a single
HuggingFace tokenizer that can encode domain events as token ID sequences.
The output tokenizer is fully compatible with HF Trainer, push_to_hub,
from_pretrained, etc.
References:
- Nubank nuFormer: V = V_special(97) U V_BPE -- ~14 tokens/transaction
- ActionPiece: items as unordered feature sets -> tokenized sequences
"""
import json
import os
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
import numpy as np
from tokenizers import Tokenizer, decoders, models, pre_tokenizers, trainers
from transformers import PreTrainedTokenizerFast
from ..schema import DomainSchema, FieldSpec, FieldType
from .field_tokenizers import (
BaseFieldTokenizer,
CalendarTokenizer,
CategoricalTokenizer,
DiscreteNumericalTokenizer,
MagnitudeBucketTokenizer,
SignTokenizer,
create_field_tokenizer,
)
# Control tokens -- always present
CONTROL_TOKENS = ["[PAD]", "[UNK]", "[BOS]", "[EOS]", "[MASK]", "[CLS]", "[SEP]"]
class DomainTokenizerBuilder:
"""Builds an HF-compatible tokenizer from a DomainSchema.
Workflow:
1. builder = DomainTokenizerBuilder(schema)
2. builder.fit(events) # fit magnitude bins etc.
3. hf_tok = builder.build(text_corpus) # build HF tokenizer
4. tokens = builder.tokenize_event(event) # tokenize a single event
5. ids = hf_tok(tokens_str) # convert to IDs
Or use the convenience method:
6. ids = builder.encode_event(event, hf_tok) # event -> IDs in one call
7. ids = builder.encode_sequence(events, hf_tok) # full sequence -> IDs
Example (finance):
>>> from domain_tokenizer.schemas.predefined import FINANCE_SCHEMA
>>> builder = DomainTokenizerBuilder(FINANCE_SCHEMA)
>>> builder.fit(training_events)
>>> hf_tokenizer = builder.build(text_corpus=descriptions)
>>> token_ids = builder.encode_sequence(user_transactions, hf_tokenizer, max_length=2048)
"""
def __init__(self, schema: DomainSchema):
self.schema = schema
self.field_tokenizers: Dict[str, Optional[BaseFieldTokenizer]] = {}
self._is_fitted = False
self._build_field_tokenizers()
def _build_field_tokenizers(self):
"""Instantiate a field tokenizer for each field in the schema."""
for spec in self.schema.fields:
self.field_tokenizers[spec.name] = create_field_tokenizer(spec)
def fit(self, events: Sequence[Dict[str, Any]]) -> "DomainTokenizerBuilder":
"""Fit data-dependent tokenizers on training events.
Currently fits: NUMERICAL_CONTINUOUS fields (magnitude bucket bins).
Args:
events: Iterable of event dicts, e.g. [{"amount": 79.99, ...}, ...]
Returns:
self (for chaining)
"""
for spec in self.schema.fields:
if spec.field_type == FieldType.NUMERICAL_CONTINUOUS:
tok = self.field_tokenizers[spec.name]
values = []
for event in events:
v = event.get(spec.name) if isinstance(event, dict) else getattr(event, spec.name, None)
if v is not None:
values.append(float(v))
if values:
tok.fit(np.array(values))
else:
raise ValueError(f"No values found for field '{spec.name}' during fitting")
self._is_fitted = True
return self
@property
def is_fitted(self) -> bool:
"""Whether all data-dependent tokenizers have been fitted."""
if not self.schema.fittable_field_names:
return True
return self._is_fitted
def _collect_special_tokens(self) -> List[str]:
"""Collect all special tokens: control + event separator + per-field domain tokens."""
tokens = list(CONTROL_TOKENS)
tokens.append(self.schema.event_separator)
for spec in self.schema.fields:
tok = self.field_tokenizers.get(spec.name)
if tok is not None and hasattr(tok, "vocab"):
tokens.extend(tok.vocab)
seen = set()
unique = []
for t in tokens:
if t not in seen:
seen.add(t)
unique.append(t)
return unique
def build(
self,
text_corpus: Optional[Iterator[str]] = None,
bpe_vocab_size: int = 8000,
min_frequency: int = 2,
) -> PreTrainedTokenizerFast:
"""Build a complete HuggingFace-compatible tokenizer.
1. Collects all domain special tokens from field tokenizers
2. Trains BPE on text corpus (if schema has text fields)
3. Merges into a single PreTrainedTokenizerFast
Args:
text_corpus: Iterator of text strings for BPE training.
bpe_vocab_size: Target BPE vocabulary size (including special tokens).
min_frequency: Minimum frequency for BPE merges.
Returns:
A PreTrainedTokenizerFast ready for use with HF Trainer.
"""
for name in self.schema.fittable_field_names:
tok = self.field_tokenizers[name]
if isinstance(tok, MagnitudeBucketTokenizer) and not tok._is_fitted:
raise RuntimeError(
f"Field '{name}' requires fitting. Call builder.fit(events) first."
)
all_special_tokens = self._collect_special_tokens()
if self.schema.has_text_fields and text_corpus is not None:
base_tokenizer = Tokenizer(models.BPE(unk_token="[UNK]"))
base_tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
base_tokenizer.decoder = decoders.ByteLevel()
trainer_obj = trainers.BpeTrainer(
vocab_size=bpe_vocab_size,
special_tokens=all_special_tokens,
min_frequency=min_frequency,
show_progress=True,
)
if isinstance(text_corpus, (list, tuple)):
base_tokenizer.train_from_iterator(iter(text_corpus), trainer=trainer_obj)
else:
base_tokenizer.train_from_iterator(text_corpus, trainer=trainer_obj)
else:
vocab = {token: i for i, token in enumerate(all_special_tokens)}
merges = []
base_tokenizer = Tokenizer(models.BPE(vocab=vocab, merges=merges, unk_token="[UNK]"))
base_tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
base_tokenizer.decoder = decoders.BPEDecoder()
hf_tokenizer = PreTrainedTokenizerFast(
tokenizer_object=base_tokenizer,
bos_token="[BOS]",
eos_token="[EOS]",
pad_token="[PAD]",
unk_token="[UNK]",
mask_token="[MASK]",
cls_token="[CLS]",
sep_token="[SEP]",
)
return hf_tokenizer
def tokenize_event(self, event: Union[Dict[str, Any], Any]) -> List[str]:
"""Convert a single domain event into a list of token strings."""
tokens = []
for spec in self.schema.fields:
if isinstance(event, dict):
value = event.get(spec.name)
else:
value = getattr(event, spec.name, None)
if spec.field_type == FieldType.TEXT:
if value is not None:
tokens.append(str(value))
continue
tok = self.field_tokenizers.get(spec.name)
if tok is None:
continue
if value is None:
tokens.append("[UNK]")
continue
result = tok(value)
if isinstance(result, list):
tokens.extend(result)
else:
tokens.append(result)
return tokens
def tokenize_sequence(
self,
events: Sequence[Union[Dict[str, Any], Any]],
add_bos: bool = True,
add_eos: bool = True,
) -> List[str]:
"""Tokenize a full sequence of events into token strings."""
all_tokens = []
if add_bos:
all_tokens.append("[BOS]")
for i, event in enumerate(events):
if i > 0:
all_tokens.append(self.schema.event_separator)
event_tokens = self.tokenize_event(event)
all_tokens.extend(event_tokens)
if add_eos:
all_tokens.append("[EOS]")
return all_tokens
def encode_sequence(
self,
events: Sequence[Union[Dict[str, Any], Any]],
hf_tokenizer: PreTrainedTokenizerFast,
max_length: int = 2048,
add_bos: bool = True,
add_eos: bool = True,
return_tensors: Optional[str] = None,
) -> Dict[str, Any]:
"""Full pipeline: events -> token strings -> token IDs."""
token_strings = self.tokenize_sequence(events, add_bos=add_bos, add_eos=add_eos)
token_text = " ".join(token_strings)
encoding = hf_tokenizer(
token_text,
max_length=max_length,
truncation=True,
padding="max_length",
return_tensors=return_tensors,
)
return encoding
def save(self, directory: str):
"""Save the builder state (fitted bins, schema, etc.) to a directory."""
os.makedirs(directory, exist_ok=True)
state = {
"schema_name": self.schema.name,
"is_fitted": self._is_fitted,
"field_tokenizers": {},
}
for name, tok in self.field_tokenizers.items():
if tok is not None:
state["field_tokenizers"][name] = tok.to_dict()
with open(os.path.join(directory, "domain_tokenizer_builder.json"), "w") as f:
json.dump(state, f, indent=2)
def get_stats(self) -> Dict[str, Any]:
"""Return statistics about the tokenizer configuration."""
return {
"schema_name": self.schema.name,
"total_fields": len(self.schema.fields),
"special_token_count": self.schema.special_token_count,
"fixed_tokens_per_event": self.schema.fixed_tokens_per_event,
"has_text_fields": self.schema.has_text_fields,
"is_fitted": self.is_fitted,
"field_details": {
spec.name: {
"type": spec.field_type.value,
"vocab_tokens": spec.token_count,
"tokens_per_event": spec.tokens_per_event,
}
for spec in self.schema.fields
},
}
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