bert-base-cased-finetuned-conll2003-ner

bert-base-cased-finetuned-conll2003-ner

This model is a fine-tuned version of BERT (bert-base-cased) on the CoNLL-2003 (Conference on Computational Natural Language Learning) dataset.

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 the Trainer API of Hugging Face Transformers.

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

Usage

from transformers import pipeline

model_checkpoint = "sadhaklal/bert-base-cased-finetuned-conll2003-ner"
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

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.37.2
• PyTorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2