metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: distilbert-base-cased
model-index:
- name: distilbert-base-cased-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- type: precision
value: 0.916955017301038
name: Precision
- type: recall
value: 0.9272384712004307
name: Recall
- type: f1
value: 0.9220680733371994
name: F1
- type: accuracy
value: 0.9804409254135515
name: Accuracy
distilbert-base-cased-finetuned-ner
This model is a fine-tuned version of distilbert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0709
- Precision: 0.9170
- Recall: 0.9272
- F1: 0.9221
- Accuracy: 0.9804
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 |
|---|---|---|---|---|---|---|---|
| 0.2732 | 1.0 | 878 | 0.0916 | 0.8931 | 0.8961 | 0.8946 | 0.9736 |
| 0.0717 | 2.0 | 1756 | 0.0726 | 0.9166 | 0.9212 | 0.9189 | 0.9794 |
| 0.0364 | 3.0 | 2634 | 0.0709 | 0.9170 | 0.9272 | 0.9221 | 0.9804 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.0.0
- Tokenizers 0.12.1