Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("grace-pro/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("grace-pro/bert-finetuned-ner", device_map="auto")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: 0.0596
- Precision: 0.9350
- Recall: 0.9488
- F1: 0.9419
- Accuracy: 0.9863
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 |
|---|---|---|---|---|---|---|---|
| 0.0881 | 1.0 | 1756 | 0.0666 | 0.9162 | 0.9342 | 0.9251 | 0.9825 |
| 0.0332 | 2.0 | 3512 | 0.0608 | 0.9272 | 0.9478 | 0.9374 | 0.9860 |
| 0.0178 | 3.0 | 5268 | 0.0596 | 0.9350 | 0.9488 | 0.9419 | 0.9863 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Dataset used to train grace-pro/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.935
- Recall on conll2003validation set self-reported0.949
- F1 on conll2003validation set self-reported0.942
- Accuracy on conll2003validation set self-reported0.986
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="grace-pro/bert-finetuned-ner")