Instructions to use AnonymousARR42/LongBEL_8B_SPACCC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnonymousARR42/LongBEL_8B_SPACCC with Transformers:

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

pipe = pipeline("text-generation", model="AnonymousARR42/LongBEL_8B_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_8B_SPACCC", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AnonymousARR42/LongBEL_8B_SPACCC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnonymousARR42/LongBEL_8B_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_8B_SPACCC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnonymousARR42/LongBEL_8B_SPACCC

SGLang

How to use AnonymousARR42/LongBEL_8B_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_8B_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_8B_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_8B_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_8B_SPACCC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AnonymousARR42/LongBEL_8B_SPACCC with Docker Model Runner:
```
docker model run hf.co/AnonymousARR42/LongBEL_8B_SPACCC
```

AnonymousARR42 commited on May 12

Commit

117aacb

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1 Parent(s): dad17fb

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@@ -16,6 +16,9 @@ tags:
   - healthcare
   - snomed
   - spaccc
   - text-generation
   - constrained-decoding
   - causal-lm
@@ -40,13 +43,32 @@ model-index:
           type: entity-linking
           name: Biomedical Entity Linking
         dataset:
-          type: bigbio/spaccc
-          name: SPACCC
-          config: spaccc_bigbio_kb
         metrics:
           - type: recall
             name: Recall@1
-            value: 0.666
 ---
 # LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking
@@ -110,37 +132,30 @@ bigbio_pages = [
                 "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": [],
@@ -172,114 +187,108 @@ for i in range(0, len(predictions), num_beams):
 **Example Output:**
 ```text
-## 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 <u>underlined</u>.
 | Model | MM-ST21PV<br>(English) | QUAERO-EMEA<br>(French) | SympTEMIST<br>(Spanish) | DisTEMIST<br>(Spanish) | MedProcNER<br>(Spanish) |
 | :--- | :---: | :---: | :---: | :---: | :---: |
@@ -297,7 +306,7 @@ Entity linking performance is reported using Recall@1 with bootstrap confidence
 | **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 | <u>77.2 ± 3.0</u> | 61.8 ± 2.5 | <u>64.3 ± 2.2</u> | <u>69.0 ± 2.0</u> |
-| **LongBEL-8B** | <u>79.3 ± 0.8</u> | 75.4 ± 3.4 | <u>62.0 ± 2.6</u> | 63.6 ± 2.1 | <u>69.0 ± 2.1</u> |
 | 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-8B model**. The ensemble result requires fusing several LongBEL input configurations and is not produced by this checkpoint alone.
@@ -328,16 +337,3 @@ This model is intended for research use. It should not be used for clinical deci
 Code and evaluation scripts are available in this [GitHub repository](https://anonymous.4open.science/r/LongBEL-31AD).
 Trained model checkpoints and processed datasets are available in the anonymous Hugging Face collection associated with LongBEL.
-<!-- ## Citation
-If you use this model, please cite the LongBEL paper.
-```bibtex
-@inproceedings{longbel2026,
-  title = {LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking},
-  author = {Anonymous},
-  booktitle = {Anonymous submission},
-  year = {2026}
-}
-``` -->

   - healthcare
   - snomed
   - spaccc
+  - medprocner
+  - symptemist
+  - distemist
   - text-generation
   - constrained-decoding
   - causal-lm
           type: entity-linking
           name: Biomedical Entity Linking
         dataset:
+          type: AnonymousARR42/SPACCC
+          name: SympTEMIST
         metrics:
           - type: recall
             name: Recall@1
+            value: 0.620
+      - task:
+          type: entity-linking
+          name: Biomedical Entity Linking
+        dataset:
+          type: AnonymousARR42/SPACCC
+          name: DisTEMIST
+        metrics:
+          - type: recall
+            name: Recall@1
+            value: 0.636
+      - task:
+          type: entity-linking
+          name: Biomedical Entity Linking
+        dataset:
+          type: AnonymousARR42/SPACCC
+          name: MedProcNER
+        metrics:
+          - type: recall
+            name: Recall@1
+            value: 0.690
 ---
 # LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking
                 "type": "paragraph",
                 "text": [
                     "Una mujer embarazada de 29 años consultó por hipertensión grave, "
+                    "cefalea y dolor epigástrico. Las pruebas de laboratorio mostraron proteinuria. "
+                    "Fue ingresada durante la noche por sospecha de PET y se inició tratamiento urgente."
                 ],
+                "offsets": [[0, 227]],
             }
         ],
         "entities": [
             {
                 "id": "T1",
+                "type": "ENFERMEDAD",
                 "text": ["hipertensión grave"],
                 "offsets": [[45, 63]],
             },
             {
+                "id": "T2",
+                "type": "ENFERMEDAD",
                 "text": ["proteinuria"],
                 "offsets": [[131, 142]],
             },
             {
+                "id": "T3",
+                "type": "ENFERMEDAD",
                 "text": ["PET"],
+                "offsets": [[191, 194]],
             },
         ],
         "events": [],
 **Example Output:**
 ```text
+## Mention 1: hipertensión grave
    - Beam 1:
+     Predicted concept name: hipertensión arterial
+     Predicted code: 38341003
+     Beam score: 0.993
    - Beam 2:
+     Predicted concept name: degeneración vascular hipertensiva
+     Predicted code: 38341003
+     Beam score: 0.249
    - Beam 3:
+     Predicted concept name: hipertensión arterial maligna
+     Predicted code: 70272006
+     Beam score: 0.046
    - Beam 4:
+     Predicted concept name: degeneración macular senil
+     Predicted code: 267718000
+     Beam score: 0.004
    - Beam 5:
+     Predicted concept name: hipertensión maligna secundaria, SAI
+     Predicted code: 194784007
+     Beam score: 0.001
+## Mention 2: proteinuria
    - Beam 1:
+     Predicted concept name: proteinuria de causa desconocida
+     Predicted code: 231860006
+     Beam score: 0.000
    - Beam 2:
+     Predicted concept name: proteína de la membrana mitocondrial asociada con neurodegeneración
+     Predicted code: 709415008
+     Beam score: 0.000
    - Beam 3:
+     Predicted concept name: proteinuria aislada concomitante con glomerulonefritis membranoproliferativa tipo III y debida a ella
+     Predicted code: 368931000119104
+     Beam score: 0.000
    - Beam 4:
+     Predicted concept name: proteinosis alveolar pulmonar congénita
+     Predicted code: 707442002
+     Beam score: 0.000
    - Beam 5:
+     Predicted concept name: proteinosis alveolar pulmonar
+     Predicted code: 10501004
+     Beam score: 0.000
+## Mention 3: PET
    - Beam 1:
+     Predicted concept name: preeclampsia
+     Predicted code: 398254007
+     Beam score: 0.285
    - Beam 2:
+     Predicted concept name: preeclampsia en el puerperio
+     Predicted code: 765182005
+     Beam score: 0.068
    - Beam 3:
+     Predicted concept name: púrpura trombocitopénica
+     Predicted code: 302873008
+     Beam score: 0.000
    - Beam 4:
+     Predicted concept name: púrpura de la vulva
+     Predicted code: 289487000
+     Beam score: 0.000
    - Beam 5:
+     Predicted concept name: pústula maligna
+     Predicted code: 84980006
+     Beam score: 0.000
+```
+### Saliency map example
+The model can also return token-level saliency maps during inference.
+```python
+predictions, saliency_maps = model.sample(
+    bigbio_pages=bigbio_pages,
+    num_beams=num_beams,
+    with_saliency_maps=True,
+)
+model.display_saliency_map(saliency_maps[2])
+````
+Example saliency map for the mention `PET`:
+<p align="center">
+  <img src="saliency_map.png" alt="Saliency map for PET prediction" width="900">
+</p>
 ## 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 <u>underlined</u> and ⭐ marks the main LongBEL-8B model.
 | Model | MM-ST21PV<br>(English) | QUAERO-EMEA<br>(French) | SympTEMIST<br>(Spanish) | DisTEMIST<br>(Spanish) | MedProcNER<br>(Spanish) |
 | :--- | :---: | :---: | :---: | :---: | :---: |
 | **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 | <u>77.2 ± 3.0</u> | 61.8 ± 2.5 | <u>64.3 ± 2.2</u> | <u>69.0 ± 2.0</u> |
+| **⭐ LongBEL-8B** | <u>79.3 ± 0.8</u> | 75.4 ± 3.4 | <u>62.0 ± 2.6</u> | 63.6 ± 2.1 | <u>69.0 ± 2.1</u> |
 | 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-8B model**. The ensemble result requires fusing several LongBEL input configurations and is not produced by this checkpoint alone.
 Code and evaluation scripts are available in this [GitHub repository](https://anonymous.4open.science/r/LongBEL-31AD).
 Trained model checkpoints and processed datasets are available in the anonymous Hugging Face collection associated with LongBEL.