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Quantbridge/distilbert-energy-intelligence-multitask-v2| Label | Description |
|---|---|
EQUITY | Stocks and equity instruments |
DERIVATIVE | Futures, options, swaps |
CURRENCY | FX pairs and currencies |
FIXED_INCOME | Bonds, treasuries, notes |
ASSET_CLASS | Broad asset class references |
INDEX | Market indices (S&P 500, FTSE, etc.) |
COMMODITY | Physical commodities (oil, gas, metals) |
TRADING_HUB | Price benchmarks and trading hubs |
| Label | Description |
|---|---|
FINANCIAL_INSTITUTION | Banks, brokerages, investment firms |
CENTRAL_BANK | Central banks (Fed, ECB, BoE) |
HEDGE_FUND | Hedge funds and asset managers |
RATING_AGENCY | Credit rating agencies |
EXCHANGE | Stock and commodity exchanges |
| Label | Description |
|---|---|
MACRO_INDICATOR | GDP, inflation, unemployment figures |
MONETARY_POLICY | Interest rate decisions, QE programmes |
FISCAL_POLICY | Government spending, tax policy |
TRADE_POLICY | Tariffs, trade agreements, WTO actions |
ECONOMIC_BLOC | G7, G20, EU, ASEAN, etc. |
| Label | Description |
|---|---|
ENERGY_COMPANY | Oil majors, utilities, renewable firms |
ENERGY_SOURCE | Oil, gas, coal, solar, nuclear, etc. |
PIPELINE | Energy pipelines and transmission lines |
REFINERY | Oil refineries and processing plants |
ENERGY_POLICY | OPEC decisions, energy legislation |
ENERGY_TRANSITION | Decarbonisation, net-zero, EV, hydrogen |
GRID | Power grids and electricity networks |
| Label | Description |
|---|---|
GEOPOLITICAL_EVENT | Summits, elections, geopolitical shifts |
SANCTION | Economic sanctions and embargoes |
TREATY | International agreements and accords |
CONFLICT_ZONE | Active or historic conflict regions |
DIPLOMATIC_ACTION | Diplomatic moves, expulsions, negotiations |
COUNTRY | Nation states |
REGION | Geographic regions (Middle East, EU, etc.) |
CITY | Cities and urban locations |
| Label | Description |
|---|---|
COMPANY | General companies |
M_AND_A | Mergers and acquisitions |
IPO | Initial public offerings |
EARNINGS_EVENT | Quarterly earnings, revenue reports |
EXECUTIVE | Named C-suite executives |
CORPORATE_ACTION | Dividends, buybacks, restructuring |
| Label | Description |
|---|---|
INFRA | Physical infrastructure (general) |
SUPPLY_CHAIN | Supply chain disruptions and logistics |
SHIPPING_VESSEL | Named ships and tankers |
PORT | Ports and maritime hubs |
| Label | Description |
|---|---|
EVENT | General newsworthy events |
RISK_FACTOR | Risk factors and vulnerabilities |
NATURAL_DISASTER | Hurricanes, earthquakes, floods |
CYBER_EVENT | Cyber attacks and digital incidents |
DISRUPTION | Supply or market disruptions |
| Label | Description |
|---|---|
TECH_COMPANY | Technology companies |
AI_MODEL | AI systems and models |
SEMICONDUCTOR | Chips and semiconductor companies |
TECH_REGULATION | Technology regulation and policy |
| Label | Description |
|---|---|
PERSON | Named individuals |
THINK_TANK | Policy research organizations |
NEWS_SOURCE | Media and news outlets |
REGULATORY_BODY | Government regulators (SEC, FCA, etc.) |
ORG | General organizations |
1from transformers import pipeline
2
3ner = pipeline(
4 "token-classification",
5 model="Quantbridge/distilbert-energy-intelligence-multitask-v2",
6 aggregation_strategy="simple",
7)
8
9text = (
10 "The Federal Reserve held interest rates steady as Brent crude fell below $75 "
11 "following OPEC+ production cuts and renewed sanctions on Russian energy exports."
12)
13
14results = ner(text)
15for entity in results:
16 print(f"{entity['word']:<35} {entity['entity_group']:<25} {entity['score']:.3f}")Federal Reserve CENTRAL_BANK 0.961
Brent TRADING_HUB 0.954
OPEC+ REGULATORY_BODY 0.947
Russian energy exports SANCTION 0.9321from transformers import AutoTokenizer, AutoModelForTokenClassification
2import torch
3
4model_name = "Quantbridge/distilbert-energy-intelligence-multitask-v2"
5
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7model = AutoModelForTokenClassification.from_pretrained(model_name)
8model.eval()
9
10text = "Goldman Sachs cut its oil price forecast after OPEC+ agreed to extend output cuts."
11inputs = tokenizer(text, return_tensors="pt")
12
13with torch.no_grad():
14 outputs = model(**inputs)
15
16predicted_ids = outputs.logits.argmax(dim=-1)[0]
17tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
18
19for token, label_id in zip(tokens, predicted_ids):
20 label = model.config.id2label[label_id.item()]
21 if label != "O" and not token.startswith("["):
22 print(f"{token.lstrip('##'):<25} {label}")| Property | Value |
|---|---|
| Base architecture | distilbert-base-uncased |
| Architecture type | DistilBertForTokenClassification |
| Entity types | 59 types (119 BIO labels) |
| Hidden dimension | 768 |
| Attention heads | 12 |
| Layers | 6 |
| Vocabulary size | 30,522 |
| Max sequence length | 512 tokens |