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card = """--- license: apache-2.0 language: en base_model: bert-base-cased tags: - token-classification - named-entity-recognition - finance - finer-ord datasets: - gtfintechlab/finer-ord metrics: - precision - recall - f1 pipeline_tag: token-classification
FiNER-ORD NER (BERT-base-cased)
Fine-tuned bert-base-cased for named entity recognition on financial text, trained on the FiNER-ORD dataset. Recognises persons (PER), locations (LOC), and organisations (ORG) in financial news and filings.
Results (validation set)
| Metric | Score |
|---|---|
| Precision | 0.840 |
| Recall | 0.856 |
| F1 | 0.848 |
| Accuracy | 0.984 |
Trained for 3 epochs, batch size 16, learning rate 2e-5, best checkpoint selected on F1.
Label scheme
O, B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG
Usage
from transformers import pipeline
ner = pipeline("token-classification", model="rajaadil/finer-ord-ner",
aggregation_strategy="simple")
ner("Goldman Sachs analyst John Smith met clients in London.")
Author
Adil Hussain — Hugging Face """
from huggingface_hub import ModelCard ModelCard(card).push_to_hub("rajaadil/finer-ord-ner")
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