metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.985193893275295
distilbert-base-uncased-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: 0.0664
- Precision: 0.9332
- Recall: 0.9423
- F1: 0.9377
- Accuracy: 0.9852
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2042 | 1.0 | 878 | 0.0636 | 0.9230 | 0.9253 | 0.9241 | 0.9822 |
| 0.0428 | 2.0 | 1756 | 0.0577 | 0.9286 | 0.9370 | 0.9328 | 0.9841 |
| 0.0199 | 3.0 | 2634 | 0.0606 | 0.9364 | 0.9401 | 0.9383 | 0.9851 |
| 0.0121 | 4.0 | 3512 | 0.0641 | 0.9339 | 0.9380 | 0.9360 | 0.9847 |
| 0.0079 | 5.0 | 4390 | 0.0664 | 0.9332 | 0.9423 | 0.9377 | 0.9852 |
Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3