# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("yangwooko/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("yangwooko/bert-finetuned-ner")Quick Links
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0137
- Precision: 0.9433
- Recall: 0.9512
- F1: 0.9472
- Accuracy: 0.9913
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
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 |
|---|---|---|---|---|---|---|---|
| 0.0179 | 1.0 | 1756 | 0.0141 | 0.9337 | 0.9416 | 0.9377 | 0.9901 |
| 0.0071 | 2.0 | 3512 | 0.0135 | 0.9442 | 0.9512 | 0.9477 | 0.9915 |
| 0.0036 | 3.0 | 5268 | 0.0137 | 0.9433 | 0.9512 | 0.9472 | 0.9913 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2
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Dataset used to train yangwooko/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.943
- Recall on conll2003validation set self-reported0.951
- F1 on conll2003validation set self-reported0.947
- Accuracy on conll2003validation set self-reported0.991
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="yangwooko/bert-finetuned-ner")