README.md

metadata

library_name: transformers
license: mit
base_model: vinai/phobert-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: phobert-human-tl-seg-seed-69
    results: []

phobert-human-tl-seg-seed-69

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

• Loss: 0.4484
• Accuracy: 0.8428
• Precision: 0.6472
• Recall: 0.4680
• F1: 0.4918

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: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	346	0.5132	0.8245	0.5325	0.3537	0.3399
0.5879	2.0	692	0.4699	0.8372	0.6490	0.4338	0.4492
0.4602	3.0	1038	0.4677	0.8365	0.6414	0.4133	0.4311
0.4602	4.0	1384	0.4650	0.8387	0.6755	0.4232	0.4445
0.4569	5.0	1730	0.4584	0.8372	0.6211	0.4259	0.4459
0.4498	6.0	2076	0.4587	0.8398	0.6538	0.4421	0.4608
0.4498	7.0	2422	0.4631	0.8372	0.6655	0.4118	0.4320
0.4517	8.0	2768	0.4523	0.8409	0.6333	0.4497	0.4717
0.4471	9.0	3114	0.4536	0.8391	0.6607	0.4230	0.4457
0.4471	10.0	3460	0.4559	0.8439	0.6747	0.4650	0.4830
0.4509	11.0	3806	0.4531	0.8383	0.6474	0.4233	0.4484
0.4438	12.0	4152	0.4484	0.8428	0.6472	0.4680	0.4918
0.4438	13.0	4498	0.4502	0.8383	0.6414	0.4330	0.4603
0.4503	14.0	4844	0.4528	0.8398	0.6454	0.4265	0.4506
0.4423	15.0	5190	0.4527	0.8387	0.6390	0.4217	0.4450
0.45	16.0	5536	0.4661	0.8372	0.6662	0.4110	0.4317
0.45	17.0	5882	0.4474	0.8417	0.6431	0.4438	0.4686

Framework versions

• Transformers 4.51.1
• Pytorch 2.5.1+cu124
• Datasets 3.5.0
• Tokenizers 0.21.0