sec-bert-finer-ord-ner

A token-classification (NER) fine-tune of nlpaueb/sec-bert-base on the FiNER-ORD dataset, for extracting person (PER), location (LOC), and organization (ORG) entities from financial/SEC-filing-style English text.

This is an academic exercise: a small pipeline that runs financial-news NLP (sentiment + NER) over a corpus of company news articles.

Intended use

• Named entity recognition (PER/LOC/ORG) on financial news, earnings-call transcripts, and SEC-filing-style English text.
• Non-commercial use only — see License below.

Not intended for: general-domain NER (it's tuned for financial text), languages other than English, or any production/commercial deployment.

Label scheme

BIO tagging over three entity types:

id	label
0	O
1	B-PER
2	I-PER
3	B-LOC
4	I-LOC
5	B-ORG
6	I-ORG

Training data

FiNER-ORD (gtfintechlab/finer-ord), a manually annotated financial NER dataset. Token-level rows were regrouped into sentences (grouped by doc_idx/sent_idx) and labels aligned to WordPiece subwords, keeping only the first subword of each token labeled (other subwords set to -100, ignored in the loss).

split	sentences
train	3,262
validation	402
test	1,075

Training procedure

Fine-tuned from nlpaueb/sec-bert-base with Hugging Face Trainer:

• learning rate: 3e-5
• batch size: 16 (train) / 32 (eval)
• epochs: 8
• weight decay: 0.01
• mixed precision (fp16)
• load_best_model_at_end=True, selected by validation F1
• seed: 42

Evaluation results

Metrics on the FiNER-ORD test split, using the best-validation-F1 checkpoint (selected during training):

metric	value
F1	0.770
Precision	0.741
Recall	0.802
Accuracy	0.979

(Best validation-split F1 during training: 0.820.)

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("gamug/sec-bert-finer-ord-ner")
model = AutoModelForTokenClassification.from_pretrained("gamug/sec-bert-finer-ord-ner")

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
ner("3M Company (NYSE:MMM) reported results with CEO Mike Roman on the call.")

License

This model is a derivative of two upstream works with different licenses:

• Base model nlpaueb/sec-bert-base: CC-BY-SA-4.0
• Training data gtfintechlab/finer-ord: CC-BY-NC-4.0

Because the training data is non-commercial-only, this fine-tuned model is released under CC-BY-NC-4.0: attribution required, non-commercial use only. Please credit both upstream works (linked above) if you use or build on this model.