# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("sunwei2011/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("sunwei2011/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: 0.0609
- Precision: 0.9319
- Recall: 0.9507
- F1: 0.9412
- Accuracy: 0.9867
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 |
|---|---|---|---|---|---|---|---|
| 0.0891 | 1.0 | 1756 | 0.0688 | 0.9154 | 0.9377 | 0.9264 | 0.9828 |
| 0.0343 | 2.0 | 3512 | 0.0637 | 0.9316 | 0.9487 | 0.9400 | 0.9859 |
| 0.0177 | 3.0 | 5268 | 0.0609 | 0.9319 | 0.9507 | 0.9412 | 0.9867 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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Dataset used to train sunwei2011/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.932
- Recall on conll2003validation set self-reported0.951
- F1 on conll2003validation set self-reported0.941
- Accuracy on conll2003validation set self-reported0.987
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sunwei2011/bert-finetuned-ner")