Instructions to use Aremaki/LongBEL_1B_MedMentions_ST21pv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aremaki/LongBEL_1B_MedMentions_ST21pv with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Aremaki/LongBEL_1B_MedMentions_ST21pv", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Aremaki/LongBEL_1B_MedMentions_ST21pv", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Aremaki/LongBEL_1B_MedMentions_ST21pv with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aremaki/LongBEL_1B_MedMentions_ST21pv"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aremaki/LongBEL_1B_MedMentions_ST21pv",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aremaki/LongBEL_1B_MedMentions_ST21pv

SGLang

How to use Aremaki/LongBEL_1B_MedMentions_ST21pv with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Aremaki/LongBEL_1B_MedMentions_ST21pv" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aremaki/LongBEL_1B_MedMentions_ST21pv",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Aremaki/LongBEL_1B_MedMentions_ST21pv" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aremaki/LongBEL_1B_MedMentions_ST21pv",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aremaki/LongBEL_1B_MedMentions_ST21pv with Docker Model Runner:
```
docker model run hf.co/Aremaki/LongBEL_1B_MedMentions_ST21pv
```

LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking

LongBEL

LongBEL is a novel document-level framework for biomedical entity linking (BEL). Instead of normalizing each mention independently, LongBEL conditions each prediction on the document context and on previous normalizations produced in the same document. This design enforces document-level consistency and is enhanced by our robust memory mechanism. The method is introduced in our paper, currently under review.

LongBEL (MedMentions Edition)

This is a finetuned version of LLaMA-3-1B trained on MedMentions, applying the LongBEL framework to enable long context and robust memory predictions.

Field	Value
Base model	`meta-llama/Llama-3.2-1B-Instruct`
Task	Biomedical Entity Linking
Dataset	MedMentions-ST21pv
Knowledge base	UMLS 2017AA, ST21pv subset
Input	BigBio-like documents with mention spans and semantic groups
Output	Ranked UMLS concept predictions
Decoding	Semantic-guided constrained decoding
Main metric	Recall@1

Intended Use

This model is intended for research on biomedical entity linking and document-level consistency.

It assumes that mention spans and semantic groups are already provided. It does not perform named entity recognition. In a full pipeline, a NER model should first detect mentions and assign semantic groups, then LongBEL can normalize these mentions to UMLS concepts.

Usage

Loading the model

import torch
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "AnonymousARR42/LongBEL_1B_MedMentions_st21pv",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

Inference example

The model expects BigBio-like documents. Each entity should include a mention text, character offsets, and a semantic group in the type field.

num_beams = 5

bigbio_pages = [
    {
        "id": "001",
        "document_id": "doc_001",
        "passages": [
            {
                "id": "0",
                "type": "paragraph",
                "text": [
                    "A 29-year-old pregnant woman presented with severe-range hypertension, "
                    "headache, and epigastric pain. Laboratory testing showed proteinuria "
                    "and mildly elevated liver enzymes. She was admitted overnight with "
                    "suspected PET and was started on urgent treatment."
                ],
                "offsets": [[0, 257]],
            }
        ],
        "entities": [
            {
                "id": "T1",
                "type": "Living Beings",
                "text": ["pregnant woman"],
                "offsets": [[14, 28]],
            },
            {
                "id": "T2",
                "type": "Disorders",
                "text": ["severe-range hypertension"],
                "offsets": [[44, 69]],
            },
            {
                "id": "T3",
                "type": "Disorders",
                "text": ["proteinuria"],
                "offsets": [[128, 139]],
            },
            {
                "id": "T4",
                "type": "Disorders",
                "text": ["PET"],
                "offsets": [[217, 220]],
            },
        ],
        "events": [],
        "coreferences": [],
        "relations": [],
    }
]

predictions = model.sample(
    bigbio_pages=bigbio_pages,
    num_beams=num_beams,
)

for i in range(0, len(predictions), num_beams):
    mention = predictions[i]["mention"]
    print(f"## Mention {(i // num_beams) + 1}: {mention}")

    for j in range(num_beams):
        pred = predictions[i + j]
        print(
            f"   - Beam {j + 1}:\n"
            f"     Predicted concept name: {pred['pred_concept_name']}\n"
            f"     Predicted code: {pred['pred_concept_code']}\n"
            f"     Beam score: {pred['beam_score']:.3f}\n"
        )

Example Output:

## Mention 1: pregnant woman
   - Beam 1:
     Predicted concept name: Pregnant Woman
     Predicted code: C0033011
     Beam score: 0.997

   - Beam 2:
     Predicted concept name: Pregnant Women
     Predicted code: C0033011
     Beam score: 0.024

   - Beam 3:
     Predicted concept name: Pregnancy Partner
     Predicted code: C3538996
     Beam score: 0.000

   - Beam 4:
     Predicted concept name: Pregnant Woman
     Predicted code: C0033011
     Beam score: 0.000

   - Beam 5:
     Predicted concept name: Pregnant Women
     Predicted code: C0033011
     Beam score: 0.000

## Mention 2: severe-range hypertension
   - Beam 1:
     Predicted concept name: Hypertensive crisis
     Predicted code: C0020546
     Beam score: 0.312

   - Beam 2:
     Predicted concept name: Hypertensive crisis (disorder)
     Predicted code: C0020546
     Beam score: 0.235

   - Beam 3:
     Predicted concept name: Hypertensive disease (disorder)
     Predicted code: C0020538
     Beam score: 0.203

   - Beam 4:
     Predicted concept name: Hypertensive disease
     Predicted code: C0020538
     Beam score: 0.168

   - Beam 5:
     Predicted concept name: Hypertension arterial
     Predicted code: C0020538
     Beam score: 0.114

## Mention 3: proteinuria
   - Beam 1:
     Predicted concept name: Proteinurias
     Predicted code: C0033687
     Beam score: 0.999

   - Beam 2:
     Predicted concept name: Proteinuric diabetic nephropathy (disorder)
     Predicted code: C0403519
     Beam score: 0.030

   - Beam 3:
     Predicted concept name: Proteinuria
     Predicted code: C0033687
     Beam score: 0.012

   - Beam 4:
     Predicted concept name: Proteinuria (disorder)
     Predicted code: C0033687
     Beam score: 0.006

   - Beam 5:
     Predicted concept name: Proteinuric diabetic nephropathy
     Predicted code: C0403519
     Beam score: 0.006

## Mention 4: PET
   - Beam 1:
     Predicted concept name: Investigation Finding
     Predicted code: C0243095
     Beam score: 0.081

   - Beam 2:
     Predicted concept name: PET - Pre-eclamptic toxemia
     Predicted code: C0032914
     Beam score: 0.017

   - Beam 3:
     Predicted concept name: PET - Pre-eclamptic toxaemia
     Predicted code: C0032914
     Beam score: 0.010

   - Beam 4:
     Predicted concept name: PET - Severe pre-eclamptic toxemia
     Predicted code: C0341950
     Beam score: 0.005

   - Beam 5:
     Predicted concept name: PET - Severe pre-eclamptic toxaemia
     Predicted code: C0341950
     Beam score: 0.004

Saliency map example

The model can also return token-level saliency maps during inference.

predictions, saliency_maps = model.sample(
    bigbio_pages=bigbio_pages,
    num_beams=num_beams,
    with_saliency_maps=True,
)

model.display_saliency_map(saliency_maps[3])

Example saliency map for the mention PET:

Saliency map for PET prediction

Evaluation

Entity linking performance is reported using Recall@1 with bootstrap confidence intervals. The best result is shown in bold, and the second-best result is underlined and ⭐ marks the main LongBEL-1B model.

Model	MM-ST21PV (English)	QUAERO-EMEA (French)	SympTEMIST (Spanish)	DisTEMIST (Spanish)	MedProcNER (Spanish)
Context-Free BEL
SciSpacy	53.8 ± 1.0	37.1 ± 4.3	9.8 ± 1.3	21.1 ± 1.9	10.3 ± 1.2
SapBERT	65.6 ± 1.0	59.7 ± 3.8	34.2 ± 2.0	38.6 ± 2.6	30.4 ± 2.1
CODER-all	62.9 ± 1.1	66.9 ± 4.0	42.2 ± 2.2	47.0 ± 2.6	42.7 ± 2.1
SapBERT-all	64.6 ± 1.1	67.9 ± 3.9	49.8 ± 2.4	49.6 ± 2.6	45.1 ± 2.2
BERGAMOT	60.9 ± 1.1	63.8 ± 4.9	48.0 ± 2.7	48.9 ± 2.4	42.3 ± 2.2
Local-Context BEL
ArboEL	76.9 ± 0.9	63.0 ± 3.9	55.4 ± 2.5	54.7 ± 2.6	59.7 ± 2.6
GENRE / mBART-large	69.6 ± 1.0	69.3 ± 5.4	59.8 ± 2.7	58.7 ± 2.7	66.0 ± 2.3
GENRE / Llama-1B	73.1 ± 1.0	75.1 ± 3.6	60.5 ± 2.4	62.5 ± 2.3	67.4 ± 2.1
GENRE / Llama-8B	75.0 ± 0.9	73.8 ± 4.0	61.7 ± 2.5	63.2 ± 2.5	68.3 ± 2.2
Global-Context BEL: LongBEL
⭐ LongBEL-1B	77.6 ± 0.9	74.5 ± 3.7	59.8 ± 2.5	61.9 ± 2.4	66.6 ± 2.1
LongBEL-1B + Ensemble	78.6 ± 0.8	77.2 ± 3.0	61.8 ± 2.5	64.3 ± 2.2	69.0 ± 2.0
LongBEL-8B	79.3 ± 0.8	75.4 ± 3.4	62.0 ± 2.6	63.6 ± 2.1	69.0 ± 2.1
LongBEL-8B + Ensemble	80.0 ± 0.8	77.6 ± 3.0	63.3 ± 2.5	65.8 ± 2.2	71.0 ± 2.0

The score reported for this checkpoint is the single LongBEL-1B model. The ensemble result requires fusing several LongBEL input configurations and is not produced by this checkpoint alone.

Speed and Memory

Measured on a single NVIDIA H100 80GB GPU.

Model	Model memory	Candidate memory	Speed
GENRE-Llama-1B baseline	2.4 GB	5.4 GB	69.6 mentions/s
LongBEL-1B	2.4 GB	5.4 GB	48.5 mentions/s

LongBEL has the same model memory footprint as the sentence-level Llama-1B baseline, but it is slower because it processes longer contexts and updates document-level memory during inference.

Limitations

This model assumes that mention spans and semantic groups are given. It does not perform mention detection.

LongBEL is most useful when concepts recur within a document. When most concepts appear only once, the memory mechanism has less information to exploit.

Because LongBEL uses previous predictions as memory, early mistakes can still influence later predictions. Robust memory training reduces this risk but does not remove it completely.

This model is intended for research use. It should not be used for clinical decision-making without additional validation and human oversight.