metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- token_classification_v2
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: favs_token_classification_v2_updated_data
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: token_classification_v2
type: token_classification_v2
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0.6923076923076923
- name: Recall
type: recall
value: 0.8357142857142857
- name: F1
type: f1
value: 0.7572815533980584
- name: Accuracy
type: accuracy
value: 0.8493150684931506
favs_token_classification_v2_updated_data
This model is a fine-tuned version of bert-base-cased on the token_classification_v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5346
- Precision: 0.6923
- Recall: 0.8357
- F1: 0.7573
- Accuracy: 0.8493
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: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
2.3096 | 1.0 | 13 | 1.9927 | 0.3011 | 0.2 | 0.2403 | 0.3726 |
2.038 | 2.0 | 26 | 1.7093 | 0.2569 | 0.2643 | 0.2606 | 0.4274 |
1.8391 | 3.0 | 39 | 1.4452 | 0.3057 | 0.4214 | 0.3544 | 0.5562 |
1.4912 | 4.0 | 52 | 1.2176 | 0.4130 | 0.5429 | 0.4691 | 0.6493 |
1.3296 | 5.0 | 65 | 1.0368 | 0.4973 | 0.6643 | 0.5688 | 0.7123 |
1.2036 | 6.0 | 78 | 0.9084 | 0.5053 | 0.6786 | 0.5793 | 0.7260 |
0.9244 | 7.0 | 91 | 0.8148 | 0.5543 | 0.7286 | 0.6296 | 0.7616 |
0.8293 | 8.0 | 104 | 0.7482 | 0.5698 | 0.7286 | 0.6395 | 0.7726 |
0.7422 | 9.0 | 117 | 0.6961 | 0.5833 | 0.75 | 0.6562 | 0.7836 |
0.6379 | 10.0 | 130 | 0.6613 | 0.6124 | 0.7786 | 0.6855 | 0.8027 |
0.6071 | 11.0 | 143 | 0.6357 | 0.6193 | 0.7786 | 0.6899 | 0.8082 |
0.5526 | 12.0 | 156 | 0.6033 | 0.6433 | 0.7857 | 0.7074 | 0.8164 |
0.537 | 13.0 | 169 | 0.5813 | 0.6512 | 0.8 | 0.7179 | 0.8301 |
0.4806 | 14.0 | 182 | 0.5706 | 0.6608 | 0.8071 | 0.7267 | 0.8329 |
0.4503 | 15.0 | 195 | 0.5594 | 0.6647 | 0.8071 | 0.7290 | 0.8356 |
0.4149 | 16.0 | 208 | 0.5503 | 0.6805 | 0.8214 | 0.7443 | 0.8438 |
0.4175 | 17.0 | 221 | 0.5430 | 0.6824 | 0.8286 | 0.7484 | 0.8438 |
0.4337 | 18.0 | 234 | 0.5396 | 0.6923 | 0.8357 | 0.7573 | 0.8493 |
0.3965 | 19.0 | 247 | 0.5361 | 0.6882 | 0.8357 | 0.7548 | 0.8493 |
0.3822 | 20.0 | 260 | 0.5346 | 0.6923 | 0.8357 | 0.7573 | 0.8493 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1