metadata
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: allenai/scibert_scivocab_uncased
model-index:
- name: SpanMarker with allenai/scibert_scivocab_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: 0.6542591267000716
name: F1
- type: precision
value: 0.6528571428571428
name: Precision
- type: recall
value: 0.6556671449067432
name: Recall
SpanMarker-SciBERT 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 utilizes the domain-specific allenai/scibert_scivocab_uncased as the underlying encoder.
📌 Model Details
- Model Type: SpanMarker
- Encoder: allenai/scibert_scivocab_uncased
- Maximum Sequence Length: 512 tokens
- Maximum Entity Length: 14 words
- Language: English
- License: cc-by-sa-4.0
Model Labels
| Label | Examples |
|---|---|
| Asset | "mental health", "raw material", "water resources" |
| Body Part | "plant leaves", "deep tissue compartment", "leaves" |
| Body of Water | "peripheral rivers", "Dhaleshwari river", "rivers" |
| Chemical | "cathode materials", "domoic acid", "marine algal toxin" |
| Disease | "seizures", "chronic epileptic syndrome", "acute neurologic signs" |
| Ecosystem | "polluted environment", "Tropical montane cloud forest", "cloud forests" |
| Energy Source | "battery cells", "fossil fuels", "12-cell series battery-pack prototype" |
| Field of Study | "veterinary medicine", "study", "reference laboratory" |
| Geographical Feature | "mountainous regions", "heterogenous topography", "low point" |
| Intellectual Artefact | "Veterinary medical records", "Daily husbandry records", "data" |
| Location | "Westbrook", "beaches", "wild" |
| Mathematical Expression | "gradient", "Stepwise machine hour constraints", "difference" |
| Measuring Device | "EEG", "MRI scan", "station" |
| Meteorological Phenomenon | "climatic variability", "rainfall", "climate change" |
| Method | "serum monitoring", "clinical efficacy", "dosing" |
| Natural Disaster | "environmental pollution", "heavy metal contamination", "seasonal air pollution" |
| Natural Phenomenon | "biochemical changes", "algal blooms", "changing ocean conditions" |
| Organism | "Zalophus californianus", "species", "California sea lions" |
| Organization | "long-term care facility", "NOAA National Marine Fisheries Service", "reference laboratory" |
| Other | "normal eating", "reports", "marine mammal health" |
| Person | "Clinicians", "staff", "clinicians" |
| Physical Artefact | "electric vehicle", "paved east – west road", "EVs" |
| Physical Phenomenon | "seasonal changes", "structural abnormalities", "normal food intake" |
| Policy | "safety", "pollution", "energy security" |
| Quantity | "energy density", ">", "200 mAhg − 1" |
| Satellite | "TRMM", "satellites", "Tropical Rainfall Measuring Mission" |
| System | "global overturning circulation", "climate", "system structure" |
| Time Period | "periods of prolonged anorexia", "several decades", "101 days" |
🚀 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 CliReNERsilver, the final model selection and the metrics below are evaluated on the independent, expert-annotated CliReNERgold 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: CliReNERsilver
- Splits used: Stratified 80:10:10 ratio (Train/Validation/Test). The 80% split was used for training.
- Evaluation Dataset: CliReNERgold
- Splits used: Evaluated on the combined 192 sentences (expert-annotated via Weighted Expert Voting).
- Preprocessing:
- Texts were tokenized using the standard SciBERT WordPiece tokenizer (
scivocab). - The dataset utilizes a flat NER schema (nested entities are excluded, and overlapping entities are resolved to the most relevant span).
- Texts were tokenized using the standard SciBERT WordPiece tokenizer (
- 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-scibert_scivocab_uncased")
# Run inference
text = "At the same time, terrestrial systems are shifting as warming drives rapid changes in frozen soils. These soils, which cover 20% of the Earth’s land surface, are degrading, with cascading effects on water, energy, and carbon cycles (Zhao et al. 2026)."
entities = model.predict(text)
for entity in entities:
print(f"Entity: {entity['span']} | Label: {entity['label']} | Score: {entity['score']:.4f}")
# Entity: terrestrial systems | Label: System | Score: 0.6377
# Entity: warming | Label: Physical Phenomenon | Score: 0.4777
# Entity: frozen soils | Label: Geographical Feature | Score: 0.3317
# Entity: soils | Label: Body of Water | Score: 0.3320
# Entity: 20% | Label: Quantity | Score: 0.9814
# Entity: Earth | Label: Location | Score: 0.9857
# Entity: land surface | Label: Geographical Feature | Score: 0.5472
# Entity: degrading | Label: Other | Score: 0.4863
# Entity: energy | Label: Chemical | Score: 0.4364
# Entity: water | Label: Chemical | Score: 0.6675
# Entity: carbon cycles | Label: Physical Phenomenon | Score: 0.6238
# Entity: Zhao et al. | Label: Person | Score: 0.8318
# Entity: 2026 | Label: Time Period | Score: 0.9117
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: 0
- 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 (CliReNERsilver Validation Split)
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|---|---|---|---|---|---|---|
| 1.0 | 62 | 0.1052 | 0.0 | 0.0 | 0.0 | 0.6075 |
| 2.0 | 124 | 0.0534 | 0.5492 | 0.4648 | 0.5035 | 0.7995 |
| 3.0 | 186 | 0.0392 | 0.7086 | 0.5409 | 0.6135 | 0.8201 |
| 4.0 | 248 | 0.0415 | 0.6404 | 0.6184 | 0.6292 | 0.8374 |
| 5.0 | 310 | 0.0382 | 0.6823 | 0.6471 | 0.6642 | 0.8513 |
| 6.0 | 372 | 0.0449 | 0.6888 | 0.6098 | 0.6469 | 0.8468 |
| 7.0 | 434 | 0.0483 | 0.6611 | 0.6298 | 0.6451 | 0.8498 |
| 8.0 | 496 | 0.0497 | 0.6529 | 0.6557 | 0.6543 | 0.8531 |
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}
}