FinBERT — Company NER (Financial News)
A ProsusAI/finbert model fine-tuned for Named Entity Recognition (NER) to detect company names in financial news articles. Trained on Indian financial news but generalises to broader financial English text.
Model Details
- Base model:
ProsusAI/finbert(110M params, BERT pre-trained on financial text) - Task: Token classification — company name detection using IOB tagging
- Language: English (financial domain)
- Label scheme:
O,B-COMPANY,I-COMPANY
Performance
Evaluated on a held-out test set of 167 articles.
| Metric | Entity-level | Token-level |
|---|---|---|
| Precision | 0.43 | 0.67 |
| Recall | 0.58 | 0.88 |
| F1 | 0.49 | 0.76 |
Confusion matrix (token level):
| Pred O | Pred COMPANY | |
|---|---|---|
| True O | 76,046 | 948 |
| True COMPANY | 262 | 1,910 |
This model has high recall (0.88) — it finds the majority of company tokens but fires more false positives than the bert-base-NER variant. Prefer this model when missing a company is more costly than an occasional false alarm.
Training
- Dataset: 1,664 Indian financial news articles from MarketAux API, ground-truthed using MarketAux equity entity tags
- Split: 1,331 train / 166 val / 167 test
- Epochs: 4
- Batch size: 16
- Learning rate: 2e-5
- Weight decay: 0.01
- Hardware: NVIDIA RTX 3070 — ~103 seconds
Usage
from transformers import pipeline
nlp = pipeline(
"ner",
model="ritam-m/finbert-company-ner",
aggregation_strategy="first",
)
text = "HDFC Bank reported strong Q1 earnings, while Infosys maintained its FY27 guidance."
results = nlp(text)
for entity in results:
if entity["entity_group"] == "COMPANY":
print(entity["word"], entity["score"])
Limitations
- Ground truth derived from MarketAux API entity tags — not manually verified
- Trained primarily on Indian financial news; performance may vary on other markets
- Entity-level F1 of 0.49 reflects a first training run on ~1,300 examples; more data will improve results
- Compare with
ritam-m/bert-base-company-nerwhich trades recall for precision