sraivante/Financial-Form-Normalization-Instructions

Financial Form Normalization Instructions

3,657 English instruction examples across 38 tasks, associated with sraivante/TinyLlama-1.1B-Financial-Form-Normalizer-LoRA.

The examples teach short user replies to map to predefined application field values: dates, amounts, ZIP codes, yes/no or boolean values, category labels, navigation intents and small stage/state JSON objects. The intended task is supplied by a system prompt.

Copyright (c) 2026 sraivante, for original dataset contributions and original release material, to the extent copyright subsists. Licensed under Apache License 2.0. See COPYRIGHT.md for scope and provenance limits.

Contents and split meaning

File / configuration	Rows	Role
data/train.jsonl / training	3,657	Entire associated training corpus
evaluation/project_diagnostic.jsonl / project_diagnostic	114	Existing project examples; all 114 also occur in training
evaluation/release_diagnostic.jsonl / release_diagnostic	12	New release-authored diagnostic inputs
evaluation/release_results.json	12 paired results	Unchanged base versus adapter, with raw outputs

There is no independent validation or test split from training. The 114 project examples are not held out. The 12 new inputs are absent verbatim from the training corpus, but form a small convenience sample for release checks, not a representative benchmark. Neither diagnostic collection was used to train or tune this published adapter during release preparation.

Training schema

Every training row has:

• messages: exactly three objects in system, user, assistant order, each with string fields role and content.
• task: the task identifier.

{
  "messages": [
    {"role": "system", "content": "Task-specific mapping and output rules"},
    {"role": "user", "content": "Short user reply"},
    {"role": "assistant", "content": "Expected field value"}
  ],
  "task": "task_identifier"
}

The diagnostic files use string fields task, system, user and expected. The new release diagnostic additionally has a source explanation.

The training content is preserved unchanged. There are no exact duplicate rows, but 12 identical system/user pairs have conflicting assistant labels. For example, some yes/no tasks assign different labels to the same short acknowledgement. Details are recorded in provenance/data_audit.json.

Tasks

Task	Training rows
investment_profile	153
years_to_save	122
magi_tier	161
part_a	96
part_b	90
part_d	89
dental	81
tobacco_usage	74
supplement	88
current_savings	69
pre_medicare_plan	112
healthcare_profile_state	73
ltc_profile_state	77
user_profile_intent	83
funding_modification_decision	79
healthcare_funding_decide	60
ltc_iul_decide	60
healthcare_state_extraction	79
level_of_protection	59
match_type	55
tranche_size	152
change_state_identifier	145
go_to_home_identifier	54
user_profile_field_detection	109
change_state_detection	44
quality_of_care	70
assisted_care_year	109
ltc_state_extraction	61
gender	82
birthdate	145
health_profile	103
life_expectancy	156
retirement_age	120
current_zipcode	163
retirement_zipcode	168
tax_filing_status	52
concierge_charge	54
illustration_choice	110

There are 38 tasks, with 44–168 examples each. Labels and meanings are specific to each system prompt. An integer may represent a field value in one task and a category label in another.

Load the data

from datasets import load_dataset

repo = "sraivante/Financial-Form-Normalization-Instructions"
train = load_dataset(repo, "training", split="train")
project_checks = load_dataset(repo, "project_diagnostic", split="diagnostic")
release_checks = load_dataset(repo, "release_diagnostic", split="diagnostic")

assert len(train) == 3657
assert len(project_checks) == 114
assert len(release_checks) == 12

Pin a repository commit through revision=... when reproducing an experiment. SHA256SUMS and the provenance files identify the included bytes.

The training notebook renders messages through TinyLlama's chat template and performs supervised fine-tuning. The task column is metadata, not an additional model input. Re-tokenization with the saved tokenizer produced 64–302 tokens per training conversation.

Provenance and historical association

The source is fp_finetune_jsonl/chat/all_combined.jsonl, a local project corpus dated May 20, 2026. The 38 individual task files reproduce the combined messages in the same order once their task identifiers are attached.

A second copy, LLM_FINE_TUNE/finetune/data/datasets/all_combined-uploaded-20260520-125234.jsonl, has identical parsed records and order, with different serialization bytes. The TinyLlama notebook loads all_combined.jsonl and records 3,657 examples, matching this corpus. Its training history matches the adapter's 1,374-step, three-epoch checkpoint.

No checksum of the original Colab input file was recorded. The available evidence strongly supports this corpus's association, but does not prove historical byte identity. Full source hashes and the precise association limits are in provenance/source.json.

The corpus consists of short, example-style field values, phrase variants and misspellings. No generator script or external dataset attribution accompanies this exact chat corpus; the proportion authored manually versus with model assistance is not established. A separate later Qwen generation pipeline is not treated as evidence of how this snapshot was created.

Publication preserved the original training and project diagnostic files without editing, label correction, filtering, shuffling or deduplication. The 12 release diagnostic cases and their results were added separately and clearly labeled.

Evaluation and limitations

Model	Exact requested values
TinyLlama chat base	0 / 12
Published LoRA adapter	11 / 12

The adapter returned RETRY for the jointly filing tax-status example, whose expected label was MARRIED_FILING_JOINTLY. The base generally returned explanations instead of the required bare values. Only surrounding whitespace was removed for scoring; this measures strict value-and-format matching on these twelve inputs.

Scoring strips surrounding whitespace and requires an exact target string. Explanations count as failures even if they contain the correct value. The comparison uses the same unquantized BF16 base and task prompts, with greedy generation capped at 32 new tokens. Full conditions and outputs are in evaluation/release_results.json.

The task rules include application-specific defaults and fixed mappings, such as a default retirement ZIP and categorical numeric values. They are historical demonstration rules, not validated current tax, Medicare, medical, investment-return or eligibility guidance. Applying those values in a real product requires independent validation of the product's rules and the user's intended value.

The dataset includes example dates, ZIP codes, health-related labels and other form fields. No external customer-record source is documented. Pattern checks found no email addresses, credential-like strings, phone-number patterns or SSN-format strings in the training corpus; these checks do not establish that every value is synthetic or comprehensively de-identified.

Known constraints include conflicting labels, narrow gender categories, fixed defaults, small task-specific sample counts, no original held-out split, and limited coverage of ambiguous or novel replies. English is the intended language.

Copyright and license

Original contributions are offered under Apache License 2.0 with Copyright (c) 2026 sraivante, subject to the rights the contributor holds. No ownership is claimed over third-party models or tokenizer assets. The dataset does not redistribute the TinyLlama base weights.

Related model: TinyLlama 1.1B Financial Form Normalizer LoRA. A matching dataset snapshot is also included under that model repository's training_data/ directory.

Release version: 2026-09-25.