QuantBridge/energy-intelligence-multitask-custom-ner

Energy Intelligence NER

Model ID: Quantbridge/energy-intelligence-multitask-ner

A fine-tuned DistilBERT model for Named Entity Recognition in the energy markets and geopolitical domain. The model identifies nine entity types relevant to energy intelligence — companies, commodities, infrastructure, markets, events, and more.

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Entity Types

Label	Description	Examples
COMPANY	Energy sector companies	ExxonMobil, BP, Saudi Aramco
COMMODITY	Energy commodities and resources	crude oil, natural gas, LNG, coal
COUNTRY	Nation states	United States, Russia, Saudi Arabia
LOCATION	Geographic locations, regions	Persian Gulf, North Sea, Permian Basin
INFRASTRUCTURE	Physical energy infrastructure	pipelines, refineries, LNG terminals
MARKET	Energy markets and trading hubs	Henry Hub, Brent, WTI, TTF
EVENT	Market events, geopolitical events	sanctions, OPEC+ cut, supply disruption
ORGANIZATION	Non-company organizations, bodies	OPEC, IEA, G7, US Energy Department
PERSON	Named individuals	ministers, executives, analysts

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Usage

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="Quantbridge/energy-intelligence-multitask-ner",
    aggregation_strategy="simple",
)

text = (
    "Saudi Aramco announced a production cut of 1 million barrels per day "
    "amid falling crude oil prices at the Brent benchmark market."
)

results = ner(text)
for entity in results:
    print(f"{entity['word']:<30} {entity['entity_group']:<20} score={entity['score']:.3f}")

Example output:

Saudi Aramco                   COMPANY              score=0.981
crude oil                      COMMODITY            score=0.974
Brent                          MARKET               score=0.968

Load model directly

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_name = "Quantbridge/energy-intelligence-multitask-ner"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

inputs = tokenizer("Brent crude fell below $70 as OPEC+ met in Vienna.", return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits
predicted_ids = logits.argmax(dim=-1)[0]
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])

for token, label_id in zip(tokens, predicted_ids):
    label = model.config.id2label[label_id.item()]
    if label != "O":
        print(f"{token:<20} {label}")

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Model Details

Property	Value
Base model	distilbert-base-uncased
Architecture	DistilBERT + token classification head
Parameters	~67M
Max sequence length	256 tokens
Training precision	FP16
Optimizer	AdamW
Learning rate	2e-5
Warmup ratio	10%
Weight decay	0.01
Epochs	5

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Training Data

The model was trained on a domain-specific dataset of English-language articles covering energy markets, commodities trading, geopolitics, and infrastructure. The dataset contains over 11,000 annotated examples with BIO (Beginning-Inside-Outside) tagging.

Dataset split:

Split	Records
Train	~9,200
Validation	~1,150
Test	~1,150

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Evaluation

Evaluated on the held-out test set using seqeval (entity-level span matching).

Metric	Score
Overall F1	reported after training
Overall Precision	reported after training
Overall Recall	reported after training

Per-entity F1 scores are available in label_map.json in the model repository.

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Limitations

• Trained exclusively on English text.
• Best suited for formal news-style writing about energy markets and geopolitics.
• Performance may degrade on highly technical engineering documents or non-standard text formats.
• Entity boundaries follow a BIO scheme; overlapping or nested entities are not supported.

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Citation

If you use this model in your work, please cite:

@misc{quantbridge-energy-ner-2025,
  title  = {Energy Intelligence NER},
  author = {Quantbridge},
  year   = {2025},
  url    = {https://huggingface.co/Quantbridge/energy-intelligence-multitask-ner}
}

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License

Apache 2.0 — see LICENSE.