metadata
license: apache-2.0
base_model: distilbert-base-uncased
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: 0.5161740999462654
- name: Recall
type: recall
value: 0.5373084237610471
- name: F1
type: f1
value: 0.5265292698969525
- name: Accuracy
type: accuracy
value: 0.8882393124374474
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: 0.3540
- Precision: 0.5162
- Recall: 0.5373
- F1: 0.5265
- Accuracy: 0.8882
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 |
---|---|---|---|---|---|---|---|
0.6731 | 1.0 | 878 | 0.4315 | 0.5372 | 0.3880 | 0.4505 | 0.8681 |
0.3888 | 2.0 | 1756 | 0.3721 | 0.4748 | 0.5319 | 0.5017 | 0.8802 |
0.3067 | 3.0 | 2634 | 0.3540 | 0.5162 | 0.5373 | 0.5265 | 0.8882 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3