metadata

license: apache-2.0
base_model: distilbert-base-cased
tags:
  - generated_from_trainer
metrics:
  - f1
  - accuracy
model-index:
  - name: distilbert-finetuned-headings
    results: []

distilbert-finetuned-headings

This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1669
F1 Positive: 0.9112
F1 Negative: 0.9854
F1: 0.9749
Roc Auc: 0.9457
Accuracy: 0.9749

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: 1e-05
train_batch_size: 8
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Positive	F1 Negative	F1	Roc Auc	Accuracy
0.1869	1.0	1785	0.1452	0.8621	0.9793	0.9640	0.8922	0.9640
0.1306	2.0	3570	0.1190	0.8738	0.9807	0.9665	0.9031	0.9665
0.1182	3.0	5355	0.1460	0.8831	0.9818	0.9685	0.9137	0.9685
0.0841	4.0	7140	0.1431	0.8990	0.9844	0.9730	0.9201	0.9730
0.061	5.0	8925	0.1540	0.9066	0.9846	0.9736	0.9431	0.9736
0.0381	6.0	10710	0.1630	0.9070	0.9851	0.9743	0.9359	0.9743
0.0268	7.0	12495	0.1669	0.9112	0.9854	0.9749	0.9457	0.9749
0.024	8.0	14280	0.2216	0.8964	0.9827	0.9704	0.9412	0.9704
0.0182	9.0	16065	0.2294	0.9032	0.9843	0.9730	0.9371	0.9730
0.0176	10.0	17850	0.2239	0.9057	0.9847	0.9736	0.9393	0.9736
0.0197	11.0	19635	0.2441	0.8966	0.9832	0.9710	0.9340	0.9710
0.0128	12.0	21420	0.2541	0.8899	0.9820	0.9691	0.9310	0.9691

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1