Papers › Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs

Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs

18 Sep 2023arXiv:2309.09582archive 2025-07-28

Jonas Golde, Patrick Haller, Felix Hamborg, Julian Risch, Alan Akbik

Most NLP tasks are modeled as supervised learning and thus require labeled training data to train effective models. However, manually producing such data at sufficient quality and quantity is known to be costly and time-intensive. Current research addresses this bottleneck by exploring a novel paradigm called zero-shot learning via dataset generation. Here, a powerful LLM is prompted with a task description to generate labeled data that can be used to train a downstream NLP model. For instance, an LLM might be prompted to "generate 500 movie reviews with positive overall sentiment, and another 500 with negative sentiment." The generated data could then be used to train a binary sentiment classifier, effectively leveraging an LLM as a teacher to a smaller student model. With this demo, we introduce Fabricator, an open-source Python toolkit for dataset generation. Fabricator implements common dataset generation workflows, supports a wide range of downstream NLP tasks (such as text classification, question answering, and entity recognition), and is integrated with well-known libraries to facilitate quick experimentation. With Fabricator, we aim to support researchers in conducting reproducible dataset generation experiments using LLMs and help practitioners apply this approach to train models for downstream tasks.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2309.09582")

Code

Syntology Ran 17 of 19 code samples harvested from 2 repositories linked to this paper; 2 have no recorded run. Of those that ran: 17 ran with no contract checked.

By repository: official repository: 13 samples from 1 repository, 11 ran; found in paper text by Syntology: 6 samples from 1 repository, 6 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

flairnlp/fabricator officialmentioned in papermentioned on GitHubApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

19 samples harvested; 17 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

17ran
2unverified

Licence: 0 of the 19 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

calculate_answer_start flairNLP/fabricator/src/fabricator/dataset_transformations/question_answering.py official repository ran Apache-2.0 (permissive) · 81ab569cd8346156 · report
convert_spans_to_token_labels flairNLP/fabricator/src/fabricator/dataset_transformations/token_classification.py official repository ran Apache-2.0 (permissive) · a8da3033f9af176d · report
create_timestamp_path flairNLP/fabricator/src/fabricator/utils.py official repository ran fingerprinted Apache-2.0 (permissive) · c1f2f10c10a88040 · report
get_labels_from_dataset flairNLP/fabricator/src/fabricator/dataset_transformations/text_classification.py official repository ran Apache-2.0 (permissive) · cddc89c4c8339cd7 · report
postprocess_squad_format flairNLP/fabricator/src/fabricator/dataset_transformations/question_answering.py official repository ran Apache-2.0 (permissive) · 85f9d2e4993481da · report
preprocess_squad_format flairNLP/fabricator/src/fabricator/dataset_transformations/question_answering.py official repository ran Apache-2.0 (permissive) · e322caa231cc0084 · report
random_sampler flairNLP/fabricator/src/fabricator/samplers/samplers.py official repository ran Apache-2.0 (permissive) · 1266726c77cca097 · report
replace_class_labels flairNLP/fabricator/src/fabricator/dataset_transformations/text_classification.py official repository ran Apache-2.0 (permissive) · 7fba20d4a7dfc7a3 · report
replace_token_labels flairNLP/fabricator/src/fabricator/dataset_transformations/token_classification.py official repository ran Apache-2.0 (permissive) · d5e8675475cdb42a · report
single_label_stratified_sample flairNLP/fabricator/src/fabricator/samplers/samplers.py official repository ran Apache-2.0 (permissive) · bcd2a038c58aa493 · report
single_label_task_sampler flairNLP/fabricator/src/fabricator/samplers/samplers.py official repository ran Apache-2.0 (permissive) · e560cf2ebbd575b2 · report
convert_label_ids_to_texts flairNLP/fabricator/src/fabricator/dataset_transformations/text_classification.py official repository unverified Apache-2.0 (permissive) · a5bb802ee94c5178 · report
convert_token_labels_to_spans flairNLP/fabricator/src/fabricator/dataset_transformations/token_classification.py official repository unverified Apache-2.0 (permissive) · b08e9d3cfc7a721d · report
generate_qualified_class_name deepset-ai/Haystack/haystack/core/serialization.py found in paper text by Syntology ran Apache-2.0 (permissive) · 164df86632d7c91b · report
has_any_trigger deepset-ai/Haystack/haystack/core/pipeline/component_checks.py found in paper text by Syntology ran Apache-2.0 (permissive) · 6fefe2cdced3efd0 · report
mark_deserialization_internal deepset-ai/Haystack/haystack/core/serialization_security.py found in paper text by Syntology ran Apache-2.0 (permissive) · f000dffd4a55b693 · report
patch_log_method_to_kwargs_only deepset-ai/Haystack/haystack/logging.py found in paper text by Syntology ran Apache-2.0 (permissive) · 61479ed8bec405d6 · report
patch_log_with_level_method_to_kwargs_only deepset-ai/Haystack/haystack/logging.py found in paper text by Syntology ran Apache-2.0 (permissive) · 832a10875cdb79ca · report
patch_make_records_to_use_kwarg_string_interpolation deepset-ai/Haystack/haystack/logging.py found in paper text by Syntology ran Apache-2.0 (permissive) · c8cb9b84c1356fdd · report

Tasks

Dataset GenerationQuestion AnsweringText ClassificationZero-Shot Learningtext-classification

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections