metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
bert-finetuned-ner
This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1592
- Precision: 0.7852
- Recall: 0.8012
- F1: 0.7931
- Accuracy: 0.9701
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: 8e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 131 | 0.1607 | 0.6254 | 0.6801 | 0.6516 | 0.9538 |
| No log | 2.0 | 262 | 0.1188 | 0.7437 | 0.7695 | 0.7564 | 0.9670 |
| No log | 3.0 | 393 | 0.1264 | 0.7556 | 0.7750 | 0.7652 | 0.9675 |
| 0.0923 | 4.0 | 524 | 0.1344 | 0.7622 | 0.7858 | 0.7738 | 0.9680 |
| 0.0923 | 5.0 | 655 | 0.1442 | 0.7741 | 0.7835 | 0.7788 | 0.9694 |
| 0.0923 | 6.0 | 786 | 0.1501 | 0.7892 | 0.8104 | 0.7997 | 0.9703 |
| 0.0923 | 7.0 | 917 | 0.1584 | 0.7750 | 0.7964 | 0.7856 | 0.9694 |
| 0.0133 | 8.0 | 1048 | 0.1592 | 0.7852 | 0.8012 | 0.7931 | 0.9701 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3