bert-finetuned-ner4-new
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: 0.1264
- Precision: 0.7894
- Recall: 0.8324
- F1: 0.8103
- Accuracy: 0.9711
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 |
---|---|---|---|---|---|---|---|
0.1019 | 1.0 | 1510 | 0.0943 | 0.7172 | 0.8246 | 0.7672 | 0.9652 |
0.0642 | 2.0 | 3020 | 0.0906 | 0.7828 | 0.8093 | 0.7958 | 0.9693 |
0.0313 | 3.0 | 4530 | 0.1052 | 0.7851 | 0.8338 | 0.8087 | 0.9705 |
0.0269 | 4.0 | 6040 | 0.1164 | 0.7927 | 0.8143 | 0.8034 | 0.9702 |
0.0153 | 5.0 | 7550 | 0.1264 | 0.7894 | 0.8324 | 0.8103 | 0.9711 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for Nathali99/bert-finetuned-ner4-new
Base model
google-bert/bert-base-cased