| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3-1.7B-Base |
| language: |
| - en |
| pipeline_tag: text-classification |
| tags: |
| - lora |
| - peft |
| - natural-language-classification |
| - truestate |
| --- |
| # The Classifier v1.1 |
|
|
| The Classifier scores a user-supplied menu of natural-language choices. This |
| repository contains the general v1.1 LoRA adapter and seven specialists fine-tuned |
| from that checkpoint on 4 October 2026. The frozen backbone is |
| [Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base), downloaded separately. |
| Each version includes its own LoRA weights, trained scalar scoring head and tokenizer. |
|
|
| | Version | Intended task | |
| | --- | --- | |
| | general | General-purpose classification across the training tasks | |
| | intent | Customer intent, including banking, insurance and telecom requests | |
| | sentiment | Positive, neutral and negative sentiment | |
| | triage | Support ticket categories | |
| | cause | Reported complaint issues and reasons | |
| | codify | Mapping text into product and service taxonomies | |
| | complaints | Financial complaint products and issues | |
| | stars | One-to-five review ratings | |
|
|
| ## Use |
|
|
| Install the [theclassifier package from PyPI](https://pypi.org/project/theclassifier/): |
|
|
| ```sh |
| pip install theclassifier |
| ``` |
|
|
| These checkpoints are public; no Hugging Face login is required. |
|
|
| ```python |
| from theclassifier import Classifier |
| |
| model = Classifier(model_repo="TrueState/theclassifier", device="auto") |
| result = model.classify( |
| "Please close my account.", |
| choices=["cancellation", "billing", "none"], |
| question="What is the customer asking for?", |
| version="intent", |
| ) |
| print(result["results"][0]["selected"]) |
| ``` |
|
|
| Version changes reuse the loaded backbone and pair each adapter with its own head. |
| Cached checkpoints are reused; missing versions are downloaded on demand. |
| Checkpoint folders are not standalone Transformers sequence-classification models: |
| use the package to load the adapter and scalar head together. |
|
|
| ## Training and evaluation |
|
|
| LoRA rank 16, alpha 16, dropout 0.05, targeting q_proj and v_proj. The scoring head |
| and adapters are trained; backbone weights remain frozen. The objective is |
| cross-entropy across each example's choice menu. The general model was trained |
| for approximately 23h44m; the specialists added approximately 19h36m on a single |
| RTX PRO 4000 Blackwell GPU. |
|
|
| The project's internal 4,264-task evaluation reports 81.3% general top-1 accuracy. |
| Task groups overlap, and intent contributes 59.1% of the evaluation. This is not |
| an independent benchmark or evidence that every specialist improves its task. |
| Training sources span Bitext, CLINC, CFPB complaints, municipal complaints, |
| IT tickets, airline sentiment/reasons, TweetEval and Yelp, with separate |
| held-out and evaluation-only sources. Consult the project paper for dataset |
| links, preparation details and limitations; per-checkpoint source manifests |
| are not complete. |
|
|
| ## Limitations |
|
|
| Scores are relative to the supplied choices and are not established as calibrated |
| across menus or domains. Wording, criteria and fallback choices affect results. |
| The model can misclassify clear inputs, including simple cancellation examples |
| used in release smoke tests. Validate on representative data before deployment. |
| CPU/CUDA smoke tests verify loading and execution, not classification quality. |
|
|
| The portable package uses direct candidate scoring with standard attention, not |
| the optimized production shared-prefix cache. Its latency is not interchangeable |
| with published service-time references. The default package input limit is 2,048 |
| tokens per candidate; longer inputs retain the first token and tail. |
|
|
| ## Files and licensing |
|
|
| `checksums.json` records the staged files. Scoring heads are safetensors; no |
| training data or intermediate checkpoints are included. The Python package and the adapter weights and scoring heads in this repository |
| are released under Apache-2.0. The upstream backbone and source datasets retain |
| their own terms. |
|
|
