Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("tehranixyz/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("tehranixyz/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: 0.0684
- Precision: 0.9247
- Recall: 0.9465
- F1: 0.9355
- Accuracy: 0.9849
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0356 | 1.0 | 1756 | 0.0684 | 0.9247 | 0.9465 | 0.9355 | 0.9849 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for tehranixyz/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train tehranixyz/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.925
- Recall on conll2003validation set self-reported0.946
- F1 on conll2003validation set self-reported0.935
- Accuracy on conll2003validation set self-reported0.985
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tehranixyz/bert-finetuned-ner")