# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("dantedgp/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("dantedgp/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: 0.0618
- Precision: 0.9360
- Recall: 0.9519
- F1: 0.9438
- Accuracy: 0.9864
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 |
|---|---|---|---|---|---|---|---|
| 0.0756 | 1.0 | 1756 | 0.0647 | 0.9038 | 0.9347 | 0.9190 | 0.9819 |
| 0.0351 | 2.0 | 3512 | 0.0694 | 0.9337 | 0.9475 | 0.9405 | 0.9853 |
| 0.0224 | 3.0 | 5268 | 0.0618 | 0.9360 | 0.9519 | 0.9438 | 0.9864 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for dantedgp/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train dantedgp/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.936
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.944
- Accuracy on conll2003validation set self-reported0.986
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dantedgp/bert-finetuned-ner")