README.md

metadata

license: apache-2.0
base_model: distilbert-base-cased
tags:
  - generated_from_trainer
datasets:
  - reuters21578
metrics:
  - f1
  - accuracy
model-index:
  - name: distilbert-finetuned-reuters21578-multilabel
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: reuters21578
          type: reuters21578
          config: ModApte
          split: test
          args: ModApte
        metrics:
          - name: F1
            type: f1
            value: 0.8628858578607322
          - name: Accuracy
            type: accuracy
            value: 0.8195625759416768

distilbert-finetuned-reuters21578-multilabel

This model is a fine-tuned version of distilbert-base-cased on the reuters21578 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0110
• F1: 0.8629
• Roc Auc: 0.9063
• Accuracy: 0.8196

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.1801	1.0	300	0.0439	0.3896	0.6210	0.3566
0.0345	2.0	600	0.0287	0.6289	0.7318	0.5954
0.0243	3.0	900	0.0219	0.6721	0.7579	0.6084
0.0178	4.0	1200	0.0177	0.7505	0.8128	0.6908
0.014	5.0	1500	0.0151	0.7905	0.8376	0.7278
0.0115	6.0	1800	0.0135	0.8132	0.8589	0.7555
0.0096	7.0	2100	0.0124	0.8291	0.8727	0.7725
0.0082	8.0	2400	0.0124	0.8335	0.8757	0.7822
0.0071	9.0	2700	0.0119	0.8392	0.8847	0.7883
0.0064	10.0	3000	0.0123	0.8339	0.8810	0.7828
0.0058	11.0	3300	0.0114	0.8538	0.8999	0.8047
0.0053	12.0	3600	0.0113	0.8525	0.8967	0.8044
0.0048	13.0	3900	0.0115	0.8520	0.8982	0.8029
0.0045	14.0	4200	0.0111	0.8566	0.8962	0.8104
0.0042	15.0	4500	0.0110	0.8610	0.9060	0.8165
0.0039	16.0	4800	0.0112	0.8583	0.9021	0.8138
0.0037	17.0	5100	0.0110	0.8620	0.9055	0.8196
0.0035	18.0	5400	0.0110	0.8629	0.9063	0.8196
0.0035	19.0	5700	0.0111	0.8624	0.9062	0.8180
0.0034	20.0	6000	0.0111	0.8626	0.9055	0.8177

Framework versions

• Transformers 4.33.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.14.3
• Tokenizers 0.13.3