# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("rdpatilds/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("rdpatilds/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: 0.0556
- Precision: 0.9310
- Recall: 0.9498
- F1: 0.9404
- 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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2222 | 1.0 | 878 | 0.0678 | 0.9042 | 0.9323 | 0.9181 | 0.9814 |
| 0.0452 | 2.0 | 1756 | 0.0543 | 0.9229 | 0.9453 | 0.9340 | 0.9857 |
| 0.0263 | 3.0 | 2634 | 0.0556 | 0.9310 | 0.9498 | 0.9404 | 0.9864 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for rdpatilds/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train rdpatilds/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.931
- Recall on conll2003validation set self-reported0.950
- F1 on conll2003validation set self-reported0.940
- Accuracy on conll2003validation set self-reported0.986
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rdpatilds/bert-finetuned-ner")