metadata

tags:
  - generated_from_trainer
datasets:
  - wisesight_sentiment
base_model: airesearch/wangchanberta-base-att-spm-uncased
model-index:
  - name: Wangchanberta-Depress-Finetuned
    results: []

Wangchanberta-Depress-Finetuned

This model is a fine-tuned version of airesearch/wangchanberta-base-att-spm-uncased on the wisesight_sentiment dataset. It achieves the following results on the evaluation set:

Loss: 0.5910

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 400
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
1.0114	0.08	200	0.9538
0.8617	0.15	400	0.8280
0.7882	0.23	600	0.7472
0.7132	0.3	800	0.7264
0.7226	0.38	1000	0.7265
0.6854	0.45	1200	0.6792
0.621	0.53	1400	0.6451
0.6093	0.61	1600	0.6364
0.6099	0.68	1800	0.6128
0.5766	0.76	2000	0.6388
0.6033	0.83	2200	0.6148
0.5966	0.91	2400	0.6440
0.6208	0.98	2600	0.5910
0.5178	1.06	2800	0.6340
0.4863	1.13	3000	0.7177
0.4852	1.21	3200	0.6766
0.4711	1.29	3400	0.6739
0.5203	1.36	3600	0.6429
0.5167	1.44	3800	0.6539
0.5053	1.51	4000	0.6172
0.5076	1.59	4200	0.6053
0.4704	1.66	4400	0.6474
0.4807	1.74	4600	0.6225
0.4792	1.82	4800	0.6282
0.5177	1.89	5000	0.6011
0.4839	1.97	5200	0.6231
0.4155	2.04	5400	0.6668
0.3923	2.12	5600	0.6886
0.3713	2.19	5800	0.6895
0.364	2.27	6000	0.6886
0.3774	2.34	6200	0.7117
0.4001	2.42	6400	0.7081
0.3531	2.5	6600	0.7465
0.3768	2.57	6800	0.7706
0.3324	2.65	7000	0.7456
0.3597	2.72	7200	0.7507
0.3868	2.8	7400	0.7542
0.4141	2.87	7600	0.7223
0.3701	2.95	7800	0.7374
0.3175	3.03	8000	0.7615
0.2951	3.1	8200	0.7880
0.2885	3.18	8400	0.8158
0.2913	3.25	8600	0.8565
0.2815	3.33	8800	0.8649
0.2748	3.4	9000	0.8783
0.2776	3.48	9200	0.8851
0.2982	3.56	9400	0.8922
0.2939	3.63	9600	0.8796
0.2712	3.71	9800	0.8873
0.2918	3.78	10000	0.8973
0.3144	3.86	10200	0.8978
0.2988	3.93	10400	0.8951

Framework versions

Transformers 4.11.2
Pytorch 1.11.0+cu113
Datasets 2.1.0
Tokenizers 0.10.3