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language: en
license: cc-by-sa-4.0
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
  - generated_from_span_marker_trainer
  - climate-change
  - earth-science
widget:
  - text: >-
      While a significant positive impact of solid-state cultivation using white
      rot fungi on enzymatic digestibility was reported in some studies [68,
      69], a negative effect of fungal pretreatment on enzymatic hydrolysis was
      noted by investigators like Shi et al . (2009) [33], who reported a
      glucose yield of 55 . 6 mg g − 1 of cotton stalks pretreated with P .
      chrysosporium, which was approximately 17% lower than the yield of
      untreated cotton stalks after enzymatic hydrolysis in spite of significant
      lignin degradation.
  - text: >-
      We quantify changes in the properties and amount of bottom water entering
      the basin by combining repeat hydrographic observations, direct velocity
      measurements and flow structure derived from a 0 . 1 ° global ocean
      sea-ice model that realistically simulates AABW formation sites and export
      pathways.
  - text: >-
      The impact of these differences on cloud forcing can be signi or more .
      cant and as high as 30 W m In recent years, observations from satellite
      data have been revised considerably after significant development efforts,
      especially after utilizing new high-quality reference measurements from
      active sensors in space, and some datasets have also improved polar cloud
      detection.
  - text: >-
      If the response is significant, how does the solar forcing impact the EASM
      rainfall variability? In this study, we will address these questions based
      on the simulation results derived from one AD 850 control experiment
      (CTRL) and four solar-only forcing experiments [spectral solar irradiance
      (SSI) experiments], which were conducted by the Community Earth System
      (CESM-LME) Model – Last Millennium Ensemble modeling project
      (Otto-Bliesner et al . 2016).
  - text: >-
      Measurements from single moorings at each gateway reveal that the speed of
      bottom water flow into the Australian Antarctic Basin varies with
      location, season and density (Fig . 3a, c, e).
pipeline_tag: token-classification
library_name: span-marker
metrics:
  - precision
  - recall
  - f1
datasets:
  - P0L3/CliReNER_v_1_1_28_SILVER
  - P0L3/CliReNER_v_1_1_28_GOLD
base_model: google-bert/bert-base-uncased
model-index:
  - name: SpanMarker with google-bert/bert-base-uncased
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: CliReNER_silver
          type: P0L3/CliReNER_v_1_1_28_SILVER
          split: eval
        metrics:
          - type: f1
            value: 0.6275911365260901
            name: F1
          - type: precision
            value: 0.6253561253561254
            name: Precision
          - type: recall
            value: 0.629842180774749
            name: Recall

SpanMarker-BERT-base-uncased for Climate Research NER

This model is a SpanMarker model fine-tuned for fine-grained Named Entity Recognition (NER) in the climate change research domain, extracting 28 distinct entity types. It uses google-bert/bert-base-uncased as the underlying encoder.

📌 Model Details

Model Type: SpanMarker
Encoder: google-bert/bert-base-uncased
Maximum Sequence Length: 512 tokens
Maximum Entity Length: 14 words
Language: English
License: cc-by-sa-4.0

Model Labels

Label	Examples
Asset	"raw material", "water resources", "mental health"
Body Part	"leaves", "deep tissue compartment", "plant leaves"
Body of Water	"rivers", "peripheral rivers", "Dhaleshwari river"
Chemical	"domoic acid", "cathode materials", "marine algal toxin"
Disease	"chronic epileptic syndrome", "acute neurologic signs", "seizures"
Ecosystem	"polluted environment", "Tropical montane cloud forest", "cloud forests"
Energy Source	"12-cell series battery-pack prototype", "fossil fuels", "battery cells"
Field of Study	"reference laboratory", "veterinary medicine", "study"
Geographical Feature	"heterogenous topography", "low point", "mountainous regions"
Intellectual Artefact	"Daily husbandry records", "Veterinary medical records", "data"
Location	"Westbrook", "beaches", "wild"
Mathematical Expression	"difference", "gradient", "Stepwise machine hour constraints"
Measuring Device	"MRI scan", "EEG", "station"
Meteorological Phenomenon	"rainfall", "climate change", "climatic variability"
Method	"dosing", "serum monitoring", "clinical efficacy"
Natural Disaster	"environmental pollution", "seasonal air pollution", "heavy metal contamination"
Natural Phenomenon	"biochemical changes", "changing ocean conditions", "algal blooms"
Organism	"species", "California sea lions", "Zalophus californianus"
Organization	"long-term care facility", "reference laboratory", "NOAA National Marine Fisheries Service"
Other	"normal eating", "reports", "marine mammal health"
Person	"clinicians", "Clinicians", "staff"
Physical Artefact	"paved east – west road", "electric vehicle", "EVs"
Physical Phenomenon	"normal food intake", "seasonal changes", "structural abnormalities"
Policy	"energy security", "pollution", "safety"
Quantity	"200 mAhg − 1", ">", "energy density"
Satellite	"satellites", "TRMM", "Tropical Rainfall Measuring Mission"
System	"global overturning circulation", "climate", "system structure"
Time Period	"several decades", "101 days", "periods of prolonged anorexia"

🚀 Main Results (Selected Checkpoint)

This repository provides the best-performing checkpoint selected from 5 runs with different random seeds. While the internal training logs tracked performance on the validation split of CliReNER_silver, the final model selection and the metrics below are evaluated on the independent, expert-annotated CliReNER_gold dataset.

Metric	Score
Precision	XX.XX
Recall	XX.XX
F1	XX.XX

This checkpoint corresponds to the seed with the highest strict F1 on the gold evaluation set.

📊 Results Across Seeds

We fine-tuned the model using 5 different random seeds to assess the stability and robustness of the architecture on the domain-specific text.

Seed	Precision	Recall	Strict F1
1	XX.XX	XX.XX	XX.XX
2	XX.XX	XX.XX	XX.XX
3	XX.XX	XX.XX	XX.XX
4	XX.XX	XX.XX	XX.XX
5	XX.XX	XX.XX	XX.XX

Summary:

F1: mean = XX.XX, std = XX.XX
Precision: mean = XX.XX, std = XX.XX
Recall: mean = XX.XX, std = XX.XX

Model Selection Strategy: The uploaded checkpoint is the single best seed (highest strict F1 on the gold dataset), ensuring strong real-world performance and high-fidelity alignment with domain-expert consensus.

📂 Dataset & Evaluation

Training Dataset: CliReNER_silver
- Splits used: Stratified 80:10:10 ratio (Train/Validation/Test). The 80% split was used for training.
Evaluation Dataset: CliReNER_gold
- Splits used: Evaluated on the combined 192 sentences (expert-annotated via Weighted Expert Voting).
Preprocessing:
- Texts were tokenized using the standard BERT WordPiece tokenizer.
- The dataset utilizes a flat NER schema (nested entities are excluded, and overlapping entities are resolved to the most relevant span).
Metric Details:
- F1 type: Strict F1 (Entity-level exact match).
- Evaluation was performed ensuring entities match both the exact boundary span and the exact semantic label to be considered correct.

⚖️ Precision vs Recall Behavior

(Note to author: Describe the model’s tendency here based on your results. Example: "The model slightly favors recall over precision" or "Balanced precision and recall")

⚙️ Usage

Direct Use for Inference

Because this model was trained using the SpanMarker framework, it requires the span_marker library for inference.

pip install span_marker

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("P0L3/CliReNER-bert-base-uncased")

# Run inference
text = "In the atmosphere, the frequency of extreme events is projected to rise sharply; for example, Chapman et al. (2023) show that in regions such as East Africa, without significant emission reductions, a historical 1-in-100-year rainfall event could become a 1-in-23-year occurrence by the end of the century. "
entities = model.predict(text)

for entity in entities:
    print(f"Entity: {entity['span']} | Label: {entity['label']} | Score: {entity['score']:.4f}")

# Entity: atmosphere | Label: Location | Score: 0.3388
# Entity: frequency | Label: Quantity | Score: 0.7958
# Entity: extreme events | Label: Meteorological Phenomenon | Score: 0.5825
# Entity: Chapman et al. | Label: Person | Score: 0.9214
# Entity: 2023 | Label: Time Period | Score: 0.9920
# Entity: regions | Label: Location | Score: 0.9397
# Entity: emission reductions | Label: Physical Phenomenon | Score: 0.5641
# Entity: 1-in-100-year | Label: Time Period | Score: 0.8089
# Entity: rainfall event | Label: Meteorological Phenomenon | Score: 0.6794
# Entity: 1-in-23-year | Label: Time Period | Score: 0.6802
# Entity: end of the century | Label: Time Period | Score: 0.9589

Downstream Use

You can easily continue fine-tuning this model on your own dataset.

Click to expand

from span_marker import SpanMarkerModel, Trainer
from datasets import load_dataset

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("your-huggingface-username/your-model-name")

# Specify a Dataset with "tokens" and "ner_tags" columns
dataset = load_dataset("your_custom_dataset")

# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("span_marker_model_id-finetuned")

📉 Training Details

Training Set Metrics

Training set	Min	Median	Max
Sentence length	3	31.4819	97
Entities per sentence	1	7.0100	22

Training Hyperparameters

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: adamw_torch with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training Results (CliReNER_silver Validation Split)

Epoch	Step	Validation Loss	Validation Precision	Validation Recall	Validation F1	Validation Accuracy
1.0	62	0.1452	0.0	0.0	0.0	0.6075
2.0	124	0.0814	0.2957	0.1277	0.1784	0.6472
3.0	186	0.0533	0.5517	0.3673	0.4410	0.7577
4.0	248	0.0454	0.6020	0.5165	0.5560	0.8104
5.0	310	0.0466	0.5820	0.5753	0.5786	0.8159
6.0	372	0.0449	0.6346	0.5581	0.5939	0.8228
7.0	434	0.0497	0.6259	0.5954	0.6103	0.8243
8.0	496	0.0483	0.6398	0.6040	0.6214	0.8349
9.0	558	0.0562	0.6254	0.6298	0.6276	0.8346

Framework Versions

Python: 3.10.19
SpanMarker: 1.7.0
Transformers: 4.50.0
PyTorch: 2.9.1
Datasets: 3.0.0
Tokenizers: 0.21.4

📚 Citation

If you use this model or the CliReNER datasets in your research, please cite the project:

@misc{poleksic2026named,
  author       = {Poleksić, Andrija and Martinčić-Ipšić, Sanda},
  title        = {Named Entity Recognition for Climate Change Research},
  year         = {2026},
  howpublished = {Research Square},
  note         = {Preprint}
}

Please also acknowledge the SpanMarker framework:

@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}