metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-model
results: []
ner-model
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: 0.1297
- Precision: 0.8229
- Recall: 0.8866
- F1: 0.8535
- Accuracy: 0.9667
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: 1e-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 |
|---|---|---|---|---|---|---|---|
| 0.1752 | 1.0 | 2489 | 0.1261 | 0.7649 | 0.8137 | 0.7885 | 0.9549 |
| 0.1076 | 2.0 | 4978 | 0.1184 | 0.7881 | 0.8592 | 0.8221 | 0.9611 |
| 0.074 | 3.0 | 7467 | 0.1137 | 0.7985 | 0.8802 | 0.8374 | 0.9634 |
| 0.0634 | 4.0 | 9956 | 0.1239 | 0.8125 | 0.8927 | 0.8507 | 0.9651 |
| 0.0387 | 5.0 | 12445 | 0.1297 | 0.8229 | 0.8866 | 0.8535 | 0.9667 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1