Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("PascalY/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("PascalY/bert-finetuned-ner")Quick Links
bert-finetuned-ner
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0579
- Precision: 0.9375
- Recall: 0.9490
- F1: 0.9432
- Accuracy: 0.9874
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 |
|---|---|---|---|---|---|---|---|
| 0.0729 | 1.0 | 1756 | 0.0607 | 0.9117 | 0.9366 | 0.9240 | 0.9839 |
| 0.0361 | 2.0 | 3512 | 0.0538 | 0.9250 | 0.9468 | 0.9358 | 0.9864 |
| 0.0205 | 3.0 | 5268 | 0.0579 | 0.9375 | 0.9490 | 0.9432 | 0.9874 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
- Downloads last month
- 2
Model tree for PascalY/bert-finetuned-ner
Base model
google-bert/bert-base-uncasedDataset used to train PascalY/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.937
- Recall on conll2003validation set self-reported0.949
- 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="PascalY/bert-finetuned-ner")