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---
license: apache-2.0
datasets:
- eriktks/conll2003
language:
- en
base_model:
- chandar-lab/NeoBERT
tags:
- ner
---
# ✨ 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](https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification)
can be used for fine-tuning:
```bash
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:
```python
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:
```json
[
{'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}
]
``` |