metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner11
results: []
bert-finetuned-ner11
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0704
- Precision: 0.9376
- Recall: 0.9537
- F1: 0.9456
- Accuracy: 0.9871
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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.073 | 1.0 | 1756 | 0.0638 | 0.9027 | 0.9303 | 0.9163 | 0.9818 |
0.0387 | 2.0 | 3512 | 0.0679 | 0.9309 | 0.9448 | 0.9378 | 0.9848 |
0.0245 | 3.0 | 5268 | 0.0658 | 0.9353 | 0.9510 | 0.9431 | 0.9864 |
0.0144 | 4.0 | 7024 | 0.0708 | 0.9338 | 0.9498 | 0.9418 | 0.9866 |
0.0062 | 5.0 | 8780 | 0.0704 | 0.9376 | 0.9537 | 0.9456 | 0.9871 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1