---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
- token-classification
- ner
- bert
datasets:
- eriktks/conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results: []
language:
- en
---


# BERT fine-tuned for Named Entity Recognition (CoNLL-2003)

A fine-tuned version of [`bert-base-cased`](https://huggingface.co/bert-base-cased) 
for Named Entity Recognition (NER), trained on the CoNLL-2003 English dataset as part 
of working through the [Hugging Face LLM Course](https://huggingface.co/learn/llm-course), 
Chapter 7.
It achieves the following results on the evaluation set:
- Loss: 0.0599
- Precision: 0.9319
- Recall: 0.9507
- F1: 0.9412
- Accuracy: 0.9867

## Model details

| Attribute        | Value                         |
|------------------|-------------------------------|
| Base model       | `bert-base-cased`             |
| Architecture     | Transformer Encoder (BERT)    |
| Task             | Token Classification (NER)    |
| Training dataset | CoNLL-2003 (English)          |
| Training epochs  | 3                             |
| Learning rate    | 2e-5                          |
| Weight decay     | 0.01                          |
| Hardware         | Google Colab (T4 GPU)         |

## Entity types

The model recognises four entity types in IOB2 format:

| Label  | Description   |
|--------|---------------|
| PER    | Person        |
| ORG    | Organisation  |
| LOC    | Location      |
| MISC   | Miscellaneous |

## Usage

```python
from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="AlexStamp/bert-finetuned-ner",
    aggregation_strategy="simple"
)

ner("Alexis works at CERN in Switzerland.")
```

## Training procedure

Fine-tuning was performed using the Hugging Face `Trainer` API with 
`DataCollatorForTokenClassification` and evaluated using the `seqeval` 
library, which computes entity-level F1 — stricter than token-level accuracy 
since the entire entity span must be correctly identified.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0759        | 1.0   | 1756 | 0.0651          | 0.8905    | 0.9310 | 0.9103 | 0.9812   |
| 0.0355        | 2.0   | 3512 | 0.0681          | 0.9321    | 0.9473 | 0.9397 | 0.9853   |
| 0.0224        | 3.0   | 5268 | 0.0599          | 0.9319    | 0.9507 | 0.9412 | 0.9867   |


### Framework versions

- Transformers 5.12.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2

## Limitations

- Trained on English news wire text (Reuters corpus); may generalise poorly 
  to other domains or languages
- `bert-base-cased` is case-sensitive by design, which is appropriate for NER 
  but means casing errors in input text can degrade performance

## Notes

This model was trained as a portfolio exercise. The base model choice 
(`bert-base-cased` over `bert-base-uncased`) is deliberate — NER is 
case-sensitive since capitalisation is a strong signal for entity detection.