Token Classification
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bert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("majid2001/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("majid2001/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: 0.0605
- Precision: 0.9351
- Recall: 0.9509
- F1: 0.9429
- Accuracy: 0.9865
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 |
|---|---|---|---|---|---|---|---|
| 0.0742 | 1.0 | 1756 | 0.0622 | 0.9147 | 0.9389 | 0.9267 | 0.9837 |
| 0.036 | 2.0 | 3512 | 0.0666 | 0.9338 | 0.9468 | 0.9403 | 0.9850 |
| 0.0219 | 3.0 | 5268 | 0.0605 | 0.9351 | 0.9509 | 0.9429 | 0.9865 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for majid2001/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train majid2001/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.935
- Recall on conll2003validation set self-reported0.951
- 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="majid2001/bert-finetuned-ner")