stefan-it/neobert-ner-conll03

✨ NeoBERT for NER

This repository hosts an NeoBERT model that was fine-tuned on the CoNLL-2003 NER dataset.

Please notice the following caveats:

• ⚠️ Work in progress, as e.g. new hyper-parameter changes or bug fixes for the implemented NeoBERTForTokenClassification class can occur.
• ⚠️ At the moment, don't expect BERT-like performance, more experiments are needed. (Is RoPE causing this?)

📝 Implementation

An own NeoBERTForTokenClassification class was implemented to conduct experiments with Transformers.

For all experiments, Transformers in version 4.50.0.dev0 is currently used including a recent built of xFormers, as NeoBERT depends on that for the SwiGLU implementation.

For following code (based on the PyTorch Token Classification example can be used for fine-tuning:

python3 run_ner.py \
  --model_name_or_path /home/stefan/Repositories/NeoBERT \
  --dataset_name conll2003 \
  --output_dir ./neobert-conll2003-lr1e-05-e10-bs16-1 \
  --seed 1 \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 16 \
  --num_train_epochs 10 \
  --learning_rate 1e-05 \
  --eval_strategy epoch \
  --save_strategy epoch \
  --overwrite_output_dir \
  --trust_remote_code True \
  --load_best_model_at_end \
  --metric_for_best_model "eval_f1" \
  --greater_is_better True

📊 Performance

A very basic hyper-parameter search is performanced for five different seeds, with reported averaged micro F1-Score on the development set of CoNLL-2003:

Configuration	Run 1	Run 2	Run 3	Run 4	Run 5	Avg.
bs=16,e=10,lr=1e-05	95.71	95.42	95.53	95.56	95.43	95.53
bs=16,e=10,lr=2e-05	95.25	95.33	95.28	95.35	95.26	95.29
bs=16,e=10,lr=3e-05	94.98	95.22	94.86	94.72	94.93	94.94
bs=16,e=10,lr=4e-05	94.61	94.39	94.57	94.65	94.87	94.61
bs=16,e=10,lr=5e-05	93.82	93.94	94.36	91.14	94.38	94.15

The performance of the current uploaded model is marked in bold.

📣 Usage

The following code can be used to test the model and recognize named entities for a given sentence:

from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer

model_name = "stefan-it/neobert-ner-conll03"


model = AutoModelForTokenClassification.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

ner = pipeline(task="ner",
               model=model,
               tokenizer=tokenizer,
               trust_remote_code=True)

print(ner("George Washington went to Washington in the US."))

This outputs:

[
 {'entity': 'B-PER', 'score': 0.99981505, 'index': 1, 'word': 'george', 'start': 0, 'end': 6},
 {'entity': 'I-PER', 'score': 0.9997435, 'index': 2, 'word': 'washington', 'start': 7, 'end': 17},
 {'entity': 'B-LOC', 'score': 0.99955124, 'index': 5, 'word': 'washington', 'start': 26, 'end': 36},
 {'entity': 'B-LOC', 'score': 0.99958867, 'index': 8, 'word': 'us', 'start': 44, 'end': 46}
]