bert-crf-ner-conll2003 – AI Model by sebastiao-teixeira | AlphaNeural AI

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sebastiao-teixeira/
bert-crf-ner-conll2003

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token-classification

eriktks/conll2003

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BERT + CRF for Named Entity Recognition

A BERT-base model with a Conditional Random Field (CRF) layer fine-tuned on CoNLL-2003 for Named Entity Recognition.

Model Description

Base Model: bert-base-cased
Architecture: BERT encoder + Linear projection + CRF decoder
Task: Named Entity Recognition (NER)
Dataset: CoNLL-2003 (eriktks/conll2003)

Training

Epochs: 10
Batch Size: 16
Learning Rate: 2e-5
Max Sequence Length: 128
Optimizer: AdamW with linear warmup (10%)

Performance

Split	Precision	Recall	F1
Validation	95.27%	95.72%	95.50%
Test	91.02%	92.31%	91.66%

Training Curves

Epoch	Train Loss	Val F1
1	5.72	92.15%
2	0.59	94.12%
3	0.31	94.75%
4	0.21	95.18%
5	0.13	95.19%
6	0.07	95.21%
7	0.05	95.44%
8	0.02	95.34%
9	0.02	95.52%
10	0.01	95.50%

Labels

The model recognizes 4 entity types in BIO format (9 classes total):

PER: Person names
ORG: Organizations
LOC: Locations
MISC: Miscellaneous entities

Usage

python

from model import BertCrfForTokenClassification
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download
import torch

# Load model and tokenizer
MODEL_REPO = "sebastiao-teixeira/bert-crf-ner-conll2003"
tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)

model = BertCrfForTokenClassification("bert-base-cased", num_labels=9)
weights_path = hf_hub_download(repo_id=MODEL_REPO, filename="pytorch_model.bin")
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()

# Inference
sentence = "John works at Google in New York."
inputs = tokenizer(sentence, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs["logits"][0].tolist()

Citation

bibtex

@misc{bert-crf-ner,
  author = {Sebastiao Teixeira},
  title = {BERT + CRF for Named Entity Recognition},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
}