metadata

license: apache-2.0
base_model: distilbert-base-cased
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilBERT-infoExtract
    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: 0.9133716160787531
          - name: Recall
            type: recall
            value: 0.9368899360484685
          - name: F1
            type: f1
            value: 0.9249813076347928
          - name: Accuracy
            type: accuracy
            value: 0.9832077471007241
language:
  - en

distilBERT-infoExtract

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: 0.0718
Precision: 0.9134
Recall: 0.9369
F1: 0.9250
Accuracy: 0.9832

Model description

The model can identify human name, organization and location so far (no time recognition). It was trained for 5 minutes with T4 GPU on Colab.

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: 8
eval_batch_size: 8
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
0.0954	1.0	1756	0.0846	0.8880	0.9194	0.9034	0.9769
0.0498	2.0	3512	0.0699	0.9057	0.9310	0.9182	0.9815
0.031	3.0	5268	0.0718	0.9134	0.9369	0.9250	0.9832

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1