QuantBridge/distilbert-energy-intelligence-multitask-v2

DistilBERT Energy Intelligence Multitask NER — v2

Model ID: Quantbridge/distilbert-energy-intelligence-multitask-v2

A domain-specific fine-tuned DistilBERT model for Named Entity Recognition across energy markets, financial instruments, geopolitics, corporate events, and technology. This is a broad-coverage multitask NER model designed for intelligence extraction from financial news and market commentary.

The model recognises 59 entity types (119 BIO labels including B-/I- prefixes) spanning multiple intelligence domains.

Entity Taxonomy

Financial Instruments & Markets

Financial Institutions

Macro & Policy

Energy Domain

Geopolitical

Corporate Events

Infrastructure & Supply Chain

Risk & Events

Technology

People & Organizations

Usage

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="Quantbridge/distilbert-energy-intelligence-multitask-v2",
    aggregation_strategy="simple",
)

text = (
    "The Federal Reserve held interest rates steady as Brent crude fell below $75 "
    "following OPEC+ production cuts and renewed sanctions on Russian energy exports."
)

results = ner(text)
for entity in results:
    print(f"{entity['word']:<35} {entity['entity_group']:<25} {entity['score']:.3f}")

Example output:

Federal Reserve                     CENTRAL_BANK              0.961
Brent                               TRADING_HUB               0.954
OPEC+                               REGULATORY_BODY           0.947
Russian energy exports              SANCTION                  0.932

Load model directly

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_name = "Quantbridge/distilbert-energy-intelligence-multitask-v2"

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

text = "Goldman Sachs cut its oil price forecast after OPEC+ agreed to extend output cuts."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

predicted_ids = outputs.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" and not token.startswith("["):
        print(f"{token.lstrip('##'):<25} {label}")

Model Details

Intended Use

This model is designed for financial and energy intelligence extraction — automated NER over news feeds, earnings transcripts, regulatory filings, and geopolitical reports. It is a base model suitable for:

• —Structured data extraction from unstructured financial news
• —Entity linking and knowledge graph population
• —Signal detection for trading and risk systems
• —Geopolitical risk monitoring

Out-of-scope use

• —General-purpose NER on non-financial text
• —Languages other than English
• —Documents with heavy technical jargon outside the financial/energy domain

Limitations

• —English-only
• —Optimised for news-style formal writing; may underperform on social media or informal text
• —59-label taxonomy may produce overlapping predictions for ambiguous entities (e.g. a company that is also an energy company)
• —BIO scheme does not support nested entities

License

Apache 2.0 — see LICENSE.