metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9240632253785785
- name: Recall
type: recall
value: 0.9352276541000112
- name: F1
type: f1
value: 0.9296119203825197
- name: Accuracy
type: accuracy
value: 0.9833034139831922
distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0612
- Precision: 0.9241
- Recall: 0.9352
- F1: 0.9296
- Accuracy: 0.9833
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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2418 | 1.0 | 878 | 0.0703 | 0.8958 | 0.9178 | 0.9067 | 0.9794 |
| 0.0513 | 2.0 | 1756 | 0.0604 | 0.9250 | 0.9314 | 0.9282 | 0.9830 |
| 0.0302 | 3.0 | 2634 | 0.0612 | 0.9241 | 0.9352 | 0.9296 | 0.9833 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cpu
- Datasets 4.6.1
- Tokenizers 0.22.2