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These checkpoints were trained on a qualitative-coding corpus whose redistribution licence and participant-consent basis are not documented in the retained research artifact (see "Data provenance" below). Access is granted for non-commercial research use and reproduction of the accompanying paper only. You are responsible for confirming that your own use is lawful in your jurisdiction. Do not redistribute the weights or attempt to reconstruct the underlying source corpus.
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CPU Open-Coding Models
This repository archives five CPU-capable English open-coding checkpoints from the matched qualitative-coding study.
| Subfolder | Architecture | Intended status |
|---|---|---|
tiny-coder-v1 |
T5-Efficient-Tiny, 15.6M | early clean coder |
tiny-hybrid-v2 |
T5-Efficient-Tiny, 15.6M | hybrid tiny candidate |
mini-coder-v1 |
T5-Efficient-Mini | intermediate-size baseline |
flan-small-coder |
FLAN-T5-Small | clean FLAN baseline |
flan-small-hybrid-v2 |
FLAN-T5-Small | interactive website candidate |
Load a checkpoint with a subfolder:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
repo = "Likich/cpu-open-coding-models"
subfolder = "tiny-hybrid-v2"
tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=subfolder)
model = AutoModelForSeq2SeqLM.from_pretrained(repo, subfolder=subfolder)
These models generate short first-pass code suggestions. Outputs must remain editable and reviewable; the models do not perform complete qualitative analysis and should not be treated as substitutes for researchers.
The expert evaluation found a human-retention benefit but also a substantial quality gap from task-trained Qwen2.5-7B. Source-data permissions are still under review. Keeping this repository private does not authorize onward distribution of its weights or training examples.
Which checkpoint to use
tiny-hybrid-v2 (T5-Efficient-Tiny, 15.6M) is the CPU model reported in the
paper: 61.7 MiB, a median 0.021 s per passage and 444 MiB peak process RSS on a
2021 M1 Max. flan-small-hybrid-v2 is the larger FLAN candidate.
Data provenance and release status
These weights derive from a 999-pair English open-coding benchmark: 600 pairs from social-science work across three university faculties (interviews and reviews, consensus-coded by three to five coders) and 399 SemEval-2014 Task 4 review excerpts, plus 1,990 machine-coded ICLR peer-review excerpts.
The supplied artifact records only the passage and its label. It does not record the original language, coder identities, adjudication trace, consent basis, or redistribution licence. Public availability of source text does not by itself establish permission to redistribute a compiled corpus or weights trained on it. Access is therefore gated, and the source passages are not released.
Memorisation
These are small sequence-to-sequence models fitted to a small label set, and they reproduce training annotations verbatim at a substantial rate. At the matched epoch-8 checkpoint, 28.9% of human-code (HR) outputs across 1,000 evaluated items are exact strings from the human training labels, drawing on 28 distinct labels. Treat generated codes as potentially reproducing the original coders' annotations rather than as novel interpretations. Long source passages are not recoverable from a model of this size; the annotation set is partially recoverable, which is why access is gated.
Intended use and limits
First-pass, editable code suggestions for one pre-segmented English passage, returning one code of at most six words. These models do not segment transcripts, assign multiple codes, build codebooks, or construct themes, and are not a substitute for a researcher. In a blinded five-expert evaluation, 47.5% of Tiny-HR suggestions were rated usable or better, against 85.0% for a task-adapted Qwen2.5-7B.
Citation
Accompanying paper: AI-Assisted Qualitative Coding on a CPU (under review). Code, prompts, analysis scripts and hashes accompany the submission.