metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- type: precision
value: 0.9210439378923027
name: Precision
- type: recall
value: 0.9356751314464705
name: Recall
- type: f1
value: 0.9283018867924528
name: F1
- type: accuracy
value: 0.983176322938345
name: Accuracy
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: 0.0611
- Precision: 0.9210
- Recall: 0.9357
- F1: 0.9283
- Accuracy: 0.9832
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2341 | 1.0 | 878 | 0.0734 | 0.9118 | 0.9206 | 0.9162 | 0.9799 |
| 0.0546 | 2.0 | 1756 | 0.0591 | 0.9210 | 0.9350 | 0.9279 | 0.9829 |
| 0.0297 | 3.0 | 2634 | 0.0611 | 0.9210 | 0.9357 | 0.9283 | 0.9832 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0