metadata
license: apache-2.0
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: 0.9256965944272446
- name: Recall
type: recall
value: 0.9365700861393892
- name: F1
type: f1
value: 0.9311015959517323
- name: Accuracy
type: accuracy
value: 0.983859437304399
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: 0.0590
- Precision: 0.9257
- Recall: 0.9366
- F1: 0.9311
- Accuracy: 0.9839
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 |
---|---|---|---|---|---|---|---|
0.2377 | 1.0 | 878 | 0.0699 | 0.9192 | 0.9210 | 0.9201 | 0.9811 |
0.0537 | 2.0 | 1756 | 0.0577 | 0.9236 | 0.9344 | 0.9290 | 0.9835 |
0.0293 | 3.0 | 2634 | 0.0590 | 0.9257 | 0.9366 | 0.9311 | 0.9839 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2