# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Vrepol/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("Vrepol/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: 0.0652
- Precision: 0.9357
- Recall: 0.9505
- F1: 0.9431
- Accuracy: 0.9860
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: Use OptimizerNames.ADAMW_TORCH 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 |
|---|---|---|---|---|---|---|---|
| 0.0753 | 1.0 | 1756 | 0.0616 | 0.9150 | 0.9424 | 0.9285 | 0.9839 |
| 0.0321 | 2.0 | 3512 | 0.0694 | 0.9258 | 0.9443 | 0.9349 | 0.9848 |
| 0.0196 | 3.0 | 5268 | 0.0652 | 0.9357 | 0.9505 | 0.9431 | 0.9860 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.2.2+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
google-bert/bert-base-casedDataset used to train Vrepol/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.936
- Recall on conll2003validation set self-reported0.951
- F1 on conll2003validation set self-reported0.943
- Accuracy on conll2003validation set self-reported0.986
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Vrepol/bert-finetuned-ner")