Modelpublic

QuantBridge/energy-intelligence-multitask-custom-ner

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

0likes7downloads

Model Card

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.

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

Usage

python

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

python

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}")

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

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

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.

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.

Citation

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

bibtex

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

License

Apache 2.0 — see LICENSE.