# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("guydebruyn/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("guydebruyn/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: 0.0575
- Precision: 0.9335
- Recall: 0.9522
- F1: 0.9428
- Accuracy: 0.9868
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 |
|---|---|---|---|---|---|---|---|
| 0.0784 | 1.0 | 1756 | 0.0842 | 0.9049 | 0.9347 | 0.9195 | 0.9786 |
| 0.0414 | 2.0 | 3512 | 0.0577 | 0.9329 | 0.9498 | 0.9413 | 0.9859 |
| 0.0261 | 3.0 | 5268 | 0.0575 | 0.9335 | 0.9522 | 0.9428 | 0.9868 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
- Downloads last month
- 5
Model tree for guydebruyn/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train guydebruyn/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.934
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.943
- Accuracy on conll2003validation set self-reported0.987
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="guydebruyn/bert-finetuned-ner")