Instructions to use AnonymousARR42/LongBEL_1B_SPACCC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnonymousARR42/LongBEL_1B_SPACCC with Transformers:

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

pipe = pipeline("text-generation", model="AnonymousARR42/LongBEL_1B_SPACCC", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AnonymousARR42/LongBEL_1B_SPACCC", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AnonymousARR42/LongBEL_1B_SPACCC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnonymousARR42/LongBEL_1B_SPACCC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnonymousARR42/LongBEL_1B_SPACCC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnonymousARR42/LongBEL_1B_SPACCC

SGLang

How to use AnonymousARR42/LongBEL_1B_SPACCC 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 "AnonymousARR42/LongBEL_1B_SPACCC" \
    --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": "AnonymousARR42/LongBEL_1B_SPACCC",
		"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 "AnonymousARR42/LongBEL_1B_SPACCC" \
        --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": "AnonymousARR42/LongBEL_1B_SPACCC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AnonymousARR42/LongBEL_1B_SPACCC with Docker Model Runner:
```
docker model run hf.co/AnonymousARR42/LongBEL_1B_SPACCC
```

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 (SPACCC Edition)

This is a finetuned version of LLaMA-3-1B trained on SPACCC, 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	SPACCC
Knowledge base	SNOMED CT Spanish Version (July 31, 2021 release)
Input	BigBio-like documents with mention spans and semantic groups
Output	Ranked SNOMED 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 SNOMED concepts.

Usage

Loading the model

import torch
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "AnonymousARR42/LongBEL_1B_SPACCC",
    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": [
                    "Una mujer embarazada de 29 años consultó por hipertensión grave, "
                    "cefalea y dolor epigástrico. Las pruebas de laboratorio mostraron proteinuria "
                    "y una ligera elevación de las enzimas hepáticas. Fue ingresada durante la noche "
                    "por sospecha de PET y se inició tratamiento urgente."
                ],
                "offsets": [[0, 275]],
            }
        ],
        "entities": [
            {
                "id": "T1",
                "type": "Living Beings",
                "text": ["mujer embarazada"],
                "offsets": [[4, 20]],
            },
            {
                "id": "T2",
                "type": "Disorders",
                "text": ["hipertensión grave"],
                "offsets": [[45, 63]],
            },
            {
                "id": "T3",
                "type": "Disorders",
                "text": ["proteinuria"],
                "offsets": [[131, 142]],
            },
            {
                "id": "T4",
                "type": "Disorders",
                "text": ["PET"],
                "offsets": [[239, 242]],
            },
        ],
        "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: 1.000

   - Beam 2:
   - Predicted concept name:Pregnant woman
   - Predicted code: C0033011
   - Beam score: 0.003

   - Beam 3:
   - Predicted concept name:Pregnant woman (person)
   - Predicted code: C0033011
   - Beam score: 0.001

   - Beam 4:
   - Predicted concept name:Pregnancy Partner
   - Predicted code: C3538996
   - Beam score: 0.000

   - Beam 5:
   - Predicted concept name:Pregnant woman (person)
   - Predicted code: C0033011
   - Beam score: 0.000

## Mention 2: severe-range hypertension
   - Beam 1:
   - Predicted concept name:Hypertensive disease
   - Predicted code: C0020538
   - Beam score: 0.078

   - Beam 2:
   - Predicted concept name:Hypertension (in some patients)
   - Predicted code: C3280936
   - Beam score: 0.022

   - Beam 3:
   - Predicted concept name:Hypertensive disease (disorder)
   - Predicted code: C0020538
   - Beam score: 0.010

   - Beam 4:
   - Predicted concept name:Hypertension, severe
   - Predicted code: C4013784
   - Beam score: 0.010

   - Beam 5:
   - Predicted concept name:Hypertension (patient A)
   - Predicted code: C4313262
   - Beam score: 0.004

## Mention 3: proteinuria
   - Beam 1:
   - Predicted concept name:Proteinurias
   - Predicted code: C0033687
   - Beam score: 1.000

   - Beam 2:
   - Predicted concept name:Proteinuric diabetic nephropathy (disorder)
   - Predicted code: C0403519
   - Beam score: 0.003

   - Beam 3:
   - Predicted concept name:Proteinuria
   - Predicted code: C0033687
   - Beam score: 0.003

   - Beam 4:
   - Predicted concept name:Proteinuric diabetic nephropathy
   - Predicted code: C0403519
   - Beam score: 0.002

   - Beam 5:
   - Predicted concept name:Proteinuric hypertension of pregnancy (disorder)
   - Predicted code: C0032914
   - Beam score: 0.001

## Mention 4: PET
   - Beam 1:
   - Predicted concept name:PET - Pre-eclamptic toxemia
   - Predicted code: C0032914
   - Beam score: 0.075

   - Beam 2:
   - Predicted concept name:PET - Pre-eclamptic toxaemia
   - Predicted code: C0032914
   - Beam score: 0.039

   - Beam 3:
   - Predicted concept name:Preeclamptic toxemia
   - Predicted code: C2931877
   - Beam score: 0.027

   - Beam 4:
   - Predicted concept name:Preeclampsia
   - Predicted code: C0032914
   - Beam score: 0.023

   - Beam 5:
   - Predicted concept name:Preeclampsia with Severe Features
   - Predicted code: C0341950
   - Beam score: 0.019

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.

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.