metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_classification_NER
results: []
token_classification_NER
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2840
- Precision: 0.5572
- Recall: 0.3930
- F1: 0.4609
- Accuracy: 0.9470
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2875 | 0.6047 | 0.2651 | 0.3686 | 0.9398 |
| No log | 2.0 | 426 | 0.2644 | 0.5878 | 0.3133 | 0.4087 | 0.9431 |
| 0.181 | 3.0 | 639 | 0.2795 | 0.5697 | 0.3485 | 0.4324 | 0.9459 |
| 0.181 | 4.0 | 852 | 0.2831 | 0.5650 | 0.3707 | 0.4477 | 0.9467 |
| 0.0548 | 5.0 | 1065 | 0.2840 | 0.5572 | 0.3930 | 0.4609 | 0.9470 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4