ContextAugmNER distemist-qwen3b-r0-seed1
This repository contains one LoRA adapter from the ContextAugmNER paper release,
a reproducibility study of inference-protocol sensitivity in compact
decoder-only Spanish clinical named entity recognition.
The adapter is released as a PEFT/LoRA artifact. It does not contain merged
base-model weights. Users must obtain the upstream base model and comply with
the base-model licence and access terms.
Adapter Summary
- Dataset:
distemist
- Base-model alias:
qwen3b
- Base model:
Qwen/Qwen2.5-3B-Instruct
- Training regime:
r0 (R0-normal)
- Seed:
1
- Paper run ID:
train_distemist_opt1_r0_qwen3b_seed1
- Local release ID:
distemist-qwen3b-r0-seed1
Repository Contents
adapter_model.safetensors: LoRA adapter weights.adapter_config.json: PEFT adapter configuration, including base model.- tokenizer files: copied from the training/inference artifact for exact
reproducibility of the paper runs.
adapter_manifest.csv: one-row manifest mapping this HF repository to the
paper run ID and release metadata.
training_args.bin is excluded from the default upload because it is not needed
for inference and can contain environment-specific training state.
Loading
1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3
4base_model = "Qwen/Qwen2.5-3B-Instruct"
5adapter_id = "edugredu/contextaugmner-distemist-qwen3b-r0-seed1"
6
7tokenizer = AutoTokenizer.from_pretrained(adapter_id)
8model = AutoModelForCausalLM.from_pretrained(
9 base_model,
10 torch_dtype="auto",
11 device_map="auto",
12)
13model = PeftModel.from_pretrained(model, adapter_id)
14model.eval()
The paper's scored runs used the project inference code, strict parser,
deterministic span alignment, official shared-task scorers, greedy decoding,
chunking at 1800 characters with 200-character overlap, and a generation budget
of 960 new tokens. For paper reproduction, use the linked code release and the
manifest rather than ad hoc prompting.
How This Adapter Is Used in the Paper
AuxStop, batch-one execution, matched stop/truncation, online-stop-only,
offline-truncate, and left-padding conditions are inference-time protocols.
They reuse trained adapters and are not separate trained checkpoints.
For the central corrected inference recommendation, trained R0 adapters are
reused with left-padding at batch size 64 or effective batch-one execution,
depending on the reported contrast. R1 adapters are retained for the historical
primary grid and transparency about the original rationale-training comparison.
Data and Ethics
The adapter was trained on public, de-identified Spanish clinical NER benchmark
data distributed by the original shared-task organizers. Dataset access and use
must follow the MedProcNER, SympTEMIST, and DisTEMIST task terms.
Intended Use
This artifact is intended for research reproducibility, audit of inference
protocols, and controlled comparison with the ContextAugmNER paper results. It
is not a clinical decision-support system and should not be used for patient
care without independent validation, governance review, and task-specific error
analysis.
Limitations
- The adapter is specialised to one dataset/task configuration and one random
seed.
- Reported paper metrics depend on the full inference protocol, parser,
aligner, scorer, and padding/batching settings.
- The release does not establish universal clinical NER performance outside the
evaluated Spanish benchmark setting.
- The adapter inherits limitations and licence obligations from the upstream
base model.
Citation
If you use this adapter, cite the ContextAugmNER paper and the corresponding
base model.
