---
library_name: transformers
license: apache-2.0
datasets:
- conll2003
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: token-classification
---

# bert-base-cased-finetuned-conll2003-ner-v2

BERT ("bert-base-cased") finetuned on CoNLL-2003 (Conference on Computational Natural Language Learning).

The model performs named entity recognition (NER). It pertains to section 2 of chapter 7 of the Hugging Face "NLP Course" (https://huggingface.co/learn/nlp-course/chapter7/2).

It was trained using a custom PyTorch loop with Hugging Face Accelerate.

Code: https://github.com/sambitmukherjee/huggingface-notebooks/blob/main/course/en/chapter7/section2_pt.ipynb

Experiment tracking: https://wandb.ai/sadhaklal/bert-base-cased-finetuned-conll2003-ner-v2

## Usage

```
from transformers import pipeline

model_checkpoint = "sadhaklal/bert-base-cased-finetuned-conll2003-ner-v2"
token_classifier = pipeline("token-classification", model=model_checkpoint, aggregation_strategy="simple")

print(token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn."))
```

## Dataset

From the dataset page:

> The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.

Examples: https://huggingface.co/datasets/conll2003/viewer

## Metrics

Accuracy on the 'validation' split of CoNLL-2003: 0.9858

Precision on the 'validation' split of CoNLL-2003: 0.9243

Recall on the 'validation' split of CoNLL-2003: 0.947

F1 on the 'validation' split of CoNLL-2003: 0.9355