README.md

metadata

library_name: transformers
license: gpl-3.0
base_model: ckiplab/albert-tiny-chinese
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: tuned-albert-tiny
    results: []

tuned-albert-tiny

This model is a fine-tuned version of ckiplab/albert-tiny-chinese on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6326
• Accuracy: 1.0
• Precision: 1.0
• Recall: 1.0
• F1: 1.0

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6878	0.2	1	0.6827	0.625	0.75	0.7	0.6190
0.6849	0.4	2	0.6786	0.625	0.75	0.7	0.6190
0.6747	0.6	3	0.6729	0.875	0.875	0.9	0.8730
0.6763	0.8	4	0.6672	0.9375	0.9286	0.95	0.9352
0.6634	1.0	5	0.6628	0.9375	0.9286	0.95	0.9352
0.6562	1.2	6	0.6589	0.9375	0.9286	0.95	0.9352
0.6516	1.4	7	0.6553	0.9375	0.9286	0.95	0.9352
0.6525	1.6	8	0.6517	0.9375	0.9286	0.95	0.9352
0.6497	1.8	9	0.6477	0.9375	0.9286	0.95	0.9352
0.6432	2.0	10	0.6439	0.9375	0.9286	0.95	0.9352
0.6488	2.2	11	0.6403	0.9375	0.9286	0.95	0.9352
0.6384	2.4	12	0.6373	0.9375	0.9286	0.95	0.9352
0.6236	2.6	13	0.6349	1.0	1.0	1.0	1.0
0.6254	2.8	14	0.6334	1.0	1.0	1.0	1.0
0.6346	3.0	15	0.6326	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.5.0
• Tokenizers 0.21.0