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: 0.2930
- Precision: 0.5538
- Recall: 0.3577
- F1: 0.4347
- Accuracy: 0.9460
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 | 0.2876 | 0.5724 | 0.2419 | 0.3401 | 0.9382 |
| No log | 2.0 | 426 | 0.2626 | 0.5434 | 0.3133 | 0.3974 | 0.9431 |
| 0.1852 | 3.0 | 639 | 0.2846 | 0.5399 | 0.3262 | 0.4067 | 0.9446 |
| 0.1852 | 4.0 | 852 | 0.2875 | 0.5536 | 0.3494 | 0.4284 | 0.9458 |
| 0.0547 | 5.0 | 1065 | 0.2930 | 0.5538 | 0.3577 | 0.4347 | 0.9460 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4