gliner-large-data-mentions

GLiNER Large — Data Mention Extraction

Fine-tuned from urchade/gliner_large-v2.1 for extracting data source mentions in social science and global health research papers.

Status: Training job pending. See train_gliner.py for the complete self-contained training script.

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Entity Types

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Usage (after training)

from gliner import GLiNER

model = GLiNER.from_pretrained("rafmacalaba/gliner-large-data-mentions")

text = "We used GPS-tagged household locations from the 2018 DHS and linked them to the WorldPop gridded population database."

entities = model.predict_entities(
    text,
    labels=["SURVEY", "DATASET", "DATABASE", "GEOCODED_DATA", "VAGUE_MENTION"],
    threshold=0.4,
)

for e in entities:
    print(f"[{e['label']}] '{e['text']}' (score={e['score']:.3f})")
# [GEOCODED_DATA] 'GPS-tagged household locations' (score=0.87)
# [SURVEY]        '2018 DHS' (score=0.92)
# [DATABASE]      'WorldPop gridded population database' (score=0.83)

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Training Data

• 121 manually annotated sentences / 175 entity spans
• Domain: Social science, global health, demographic surveys, GIS/spatial data
• No public dataset available — custom-built for this task
• All data is embedded inline in train_gliner.py for portability

Entity distribution:

SURVEY        : 55 spans (31.4%)
GEOCODED_DATA : 40 spans (22.9%)
DATABASE      : 38 spans (21.7%)
DATASET       : 24 spans (13.7%)
VAGUE_MENTION : 18 spans (10.3%)

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Training Configuration

Based on the original GLiNER paper (AAAI 2024, arxiv:2311.08526) fine-tuning recipe:

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How to Run Training

Option A: HF Jobs (recommended)

from hf_jobs import run

run(
    script="https://huggingface.co/rafmacalaba/gliner-large-data-mentions/raw/main/train_gliner.py",
    dependencies=["gliner==0.2.26", "torch", "trackio"],
    hardware_flavor="t4-small",
    timeout="3h",
    env={
        "TRACKIO_SPACE_ID": "rafmacalaba/ml-intern-gliner01",
        "TRACKIO_PROJECT": "gliner-data-mentions",
    }
)

Or via the ML Intern agent tool:

hf_jobs(
  operation="run",
  script="https://huggingface.co/rafmacalaba/gliner-large-data-mentions/raw/main/train_gliner.py",
  dependencies=["gliner==0.2.26", "torch", "trackio"],
  hardware_flavor="t4-small",
  timeout="3h",
  trackio_space_id="rafmacalaba/ml-intern-gliner01",
  trackio_project="gliner-data-mentions"
)

Option B: Local GPU

pip install gliner==0.2.26 torch trackio
python train_gliner.py
# or smoke test:
python train_gliner.py --smoke_test

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Agent Execution Plan

For an autonomous agent completing this task, follow these steps in order:

Step 1 — Verify prerequisites ✅ (already done)

• gliner==0.2.26 API validated: Trainer, TrainingArguments, model._create_data_collator()
• TrainingArguments has others_lr, loss_reduction, negatives, masking, focal_loss_* params
• GLiNER.push_to_hub() confirmed available
• report_to="trackio" natively supported in TrainingArguments
• Training data: 121 examples, 175 spans, 0 validation errors
• Hub repo created: rafmacalaba/gliner-large-data-mentions

Step 2 — Submit training job

hf_jobs(
  operation="run",
  script="https://huggingface.co/rafmacalaba/gliner-large-data-mentions/raw/main/train_gliner.py",
  dependencies=["gliner==0.2.26", "torch", "trackio"],
  hardware_flavor="t4-small",
  timeout="3h",
  trackio_space_id="rafmacalaba/ml-intern-gliner01",
  trackio_project="gliner-data-mentions"
)

Expected: job starts, downloads urchade/gliner_large-v2.1 (~900MB), trains 25 epochs (~103 examples, 13 steps/epoch = 325 total steps), ~20-40 min on T4.

Step 3 — Monitor

hf_jobs(operation="logs", job_id="<job_id_from_step2>")

Look for: === Starting training ===, then {'loss': ..., 'epoch': ...} every 5 steps. Watch for OOM (reduce batch_size to 4, increase gradient_accumulation_steps to 2).

Step 4 — Verify Hub push

hf_repo_files(operation="list", repo_id="rafmacalaba/gliner-large-data-mentions")

Expect: pytorch_model.bin or model.safetensors, config.json, tokenizer_config.json.

Step 5 — Evaluate (post-training)

Run inference on test sentences and confirm entities are extracted with score > 0.4:

• "The Demographic and Health Survey collected data in 47 countries." → [SURVEY] Demographic and Health Survey
• "Geocoded DHS cluster coordinates were overlaid with flood maps." → [GEOCODED_DATA], [SURVEY]
• "A survey from Ghana collected child nutrition data." → [VAGUE_MENTION] A survey from Ghana

Step 6 — Iterate (if results are poor)

If entity scores are low (< 0.5) or entities are missed:

1. Add more training examples (especially VAGUE_MENTION — currently underrepresented at 18 examples)
2. Increase epochs to 40 (small dataset benefits from more epochs)
3. Consider lowering encoder LR to 5e-6 if loss is oscillating

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Key References

• GLiNER paper: arxiv:2311.08526 (AAAI 2024)
• Dataset mention extraction: arxiv:2502.10263 (World Bank, 2025)
• GSAP-NER (scholarly entity extraction): arxiv:2311.09860
• Coleridge "Show US the Data" Kaggle: best public dataset for dataset mentions in social science papers

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