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.
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
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0125 | 1.0 | 1756 | 0.0729 | 0.9095 | 0.9339 | 0.9215 | 0.9810 |
0.0001 | 2.0 | 3512 | 0.0558 | 0.9265 | 0.9487 | 0.9375 | 0.9862 |
0.0001 | 3.0 | 5268 | 0.0578 | 0.9366 | 0.9515 | 0.9440 | 0.9867 |
Framework versions
- Transformers 4.37.2
- PyTorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for sadhaklal/bert-base-cased-finetuned-conll2003-ner
Base model
google-bert/bert-base-cased