metadata

license: apache-2.0
base_model: lxyuan/distilbert-base-multilingual-cased-sentiments-student
tags:
  - generated_from_trainer
datasets:
  - indonlu
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: sentiment2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: indonlu
          type: indonlu
          config: smsa
          split: validation
          args: smsa
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.915079365079365
          - name: Precision
            type: precision
            value: 0.9152979362942885
          - name: Recall
            type: recall
            value: 0.915079365079365
          - name: F1
            type: f1
            value: 0.9149940431800128

sentiment2

This model is a fine-tuned version of lxyuan/distilbert-base-multilingual-cased-sentiments-student on the indonlu dataset. It achieves the following results on the evaluation set:

Loss: 0.6085
Accuracy: 0.9151
Precision: 0.9153
Recall: 0.9151
F1: 0.9150

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: 5e-05
train_batch_size: 40
eval_batch_size: 40
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.01
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	275	0.2543	0.9190	0.9213	0.9190	0.9196
0.2191	2.0	550	0.2710	0.9143	0.9133	0.9143	0.9134
0.2191	3.0	825	0.3715	0.9135	0.9144	0.9135	0.9114
0.0714	4.0	1100	0.4751	0.9071	0.9085	0.9071	0.9077
0.0714	5.0	1375	0.4859	0.9206	0.9214	0.9206	0.9203
0.0263	6.0	1650	0.5383	0.9143	0.9155	0.9143	0.9143
0.0263	7.0	1925	0.5630	0.9167	0.9166	0.9167	0.9165
0.0126	8.0	2200	0.5916	0.9151	0.9151	0.9151	0.9146
0.0126	9.0	2475	0.6073	0.9135	0.9130	0.9135	0.9131
0.0056	10.0	2750	0.6085	0.9151	0.9153	0.9151	0.9150

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2