bert-finetuned-goodsmemo-ner
This model is a fine-tuned version of bert-base-cased on the goodsmemo dataset. It achieves the following results on the evaluation set:
- Loss: 0.1899
- Precision: 0.1455
- Recall: 0.1495
- F1: 0.1475
- Accuracy: 0.9294
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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 46 | 0.3317 | 0.0 | 0.0 | 0.0 | 0.9018 |
No log | 2.0 | 92 | 0.3051 | 0.0090 | 0.0280 | 0.0137 | 0.8640 |
No log | 3.0 | 138 | 0.2561 | 0.0207 | 0.0467 | 0.0287 | 0.8966 |
No log | 4.0 | 184 | 0.2345 | 0.0383 | 0.0748 | 0.0506 | 0.9118 |
No log | 5.0 | 230 | 0.2319 | 0.0491 | 0.1028 | 0.0665 | 0.9018 |
No log | 6.0 | 276 | 0.2108 | 0.1085 | 0.1308 | 0.1186 | 0.9245 |
No log | 7.0 | 322 | 0.2042 | 0.1181 | 0.1402 | 0.1282 | 0.9268 |
No log | 8.0 | 368 | 0.2077 | 0.1262 | 0.1215 | 0.1238 | 0.9263 |
No log | 9.0 | 414 | 0.1951 | 0.1524 | 0.1495 | 0.1509 | 0.9297 |
No log | 10.0 | 460 | 0.1899 | 0.1455 | 0.1495 | 0.1475 | 0.9294 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
- Downloads last month
- 9
Finetuned from
Evaluation results
- Precision on goodsmemovalidation set self-reported0.145
- Recall on goodsmemovalidation set self-reported0.150
- F1 on goodsmemovalidation set self-reported0.147
- Accuracy on goodsmemovalidation set self-reported0.929