metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - amazon_reviews_multi
metrics:
  - accuracy
  - f1
  - precision
  - recall
base_model: distilbert-base-uncased
model-index:
  - name: distilbert-base-uncased-finetuned-amazon-review
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: amazon_reviews_multi
          type: amazon_reviews_multi
          args: es
        metrics:
          - type: accuracy
            value: 0.693
            name: Accuracy
          - type: f1
            value: 0.7002653469272611
            name: F1
          - type: precision
            value: 0.709541681233075
            name: Precision
          - type: recall
            value: 0.693
            name: Recall

distilbert-base-uncased-finetuned-amazon-review

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

Loss: 1.3494
Accuracy: 0.693
F1: 0.7003
Precision: 0.7095
Recall: 0.693

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	0.5	500	0.8287	0.7104	0.7120	0.7152	0.7104
0.4238	1.0	1000	0.8917	0.7094	0.6989	0.6917	0.7094
0.4238	1.5	1500	0.9367	0.6884	0.6983	0.7151	0.6884
0.3152	2.0	2000	0.9845	0.7116	0.7144	0.7176	0.7116
0.3152	2.5	2500	1.0752	0.6814	0.6968	0.7232	0.6814
0.2454	3.0	3000	1.1215	0.6918	0.6954	0.7068	0.6918
0.2454	3.5	3500	1.2905	0.6976	0.7048	0.7138	0.6976
0.1989	4.0	4000	1.2938	0.694	0.7016	0.7113	0.694
0.1989	4.5	4500	1.3623	0.6972	0.7014	0.7062	0.6972
0.1746	5.0	5000	1.3494	0.693	0.7003	0.7095	0.693

Framework versions

Transformers 4.15.0
Pytorch 1.10.0+cu111
Datasets 1.17.0
Tokenizers 0.10.3