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: 0.9253698388165158
- name: Recall
type: recall
value: 0.9376887795055375
- name: F1
type: f1
value: 0.9314885814302384
- name: Accuracy
type: accuracy
value: 0.9842565968195466
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: 0.0601
- Precision: 0.9254
- Recall: 0.9377
- F1: 0.9315
- Accuracy: 0.9843
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 |
|---|---|---|---|---|---|---|---|
| 0.244 | 1.0 | 878 | 0.0695 | 0.9145 | 0.9235 | 0.9190 | 0.9812 |
| 0.0545 | 2.0 | 1756 | 0.0602 | 0.9211 | 0.9355 | 0.9282 | 0.9833 |
| 0.0298 | 3.0 | 2634 | 0.0601 | 0.9254 | 0.9377 | 0.9315 | 0.9843 |
Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2