scenario-non-kd-from-pre-finetune-div-2-data-hate_speech_filipino-model-xlm-robe

This model is a fine-tuned version of xlm-roberta-base on the hate_speech_filipino dataset. It achieves the following results on the evaluation set:

• Loss: 1.0267
• Accuracy: 0.7750
• F1: 0.7538

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 6969

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.32	100	0.5927	0.6957	0.7102
No log	0.64	200	0.5270	0.7372	0.7150
No log	0.96	300	0.5211	0.7427	0.7347
No log	1.28	400	0.5186	0.7628	0.7359
0.5494	1.6	500	0.5580	0.7476	0.7544
0.5494	1.92	600	0.4977	0.7462	0.6728
0.5494	2.24	700	0.4961	0.7580	0.7335
0.5494	2.56	800	0.4659	0.7776	0.7556
0.5494	2.88	900	0.5498	0.7682	0.7341
0.3965	3.19	1000	0.5319	0.7814	0.7666
0.3965	3.51	1100	0.5999	0.7765	0.7671
0.3965	3.83	1200	0.5410	0.7746	0.7599
0.3965	4.15	1300	0.7042	0.7779	0.7638
0.3965	4.47	1400	0.6954	0.7800	0.7513
0.2696	4.79	1500	0.6503	0.7767	0.7516
0.2696	5.11	1600	0.8617	0.7781	0.7654
0.2696	5.43	1700	0.6982	0.7698	0.7586
0.2696	5.75	1800	0.7800	0.7696	0.7618
0.2696	6.07	1900	0.8446	0.7802	0.7657
0.1866	6.39	2000	0.8388	0.7722	0.7566
0.1866	6.71	2100	1.0689	0.7540	0.7584
0.1866	7.03	2200	0.9079	0.7566	0.7596
0.1866	7.35	2300	0.8218	0.7691	0.7618
0.1866	7.67	2400	0.7888	0.7691	0.7177
0.149	7.99	2500	0.8802	0.7760	0.7637
0.149	8.31	2600	0.9296	0.7680	0.7700
0.149	8.63	2700	0.9560	0.7795	0.7518
0.149	8.95	2800	0.7176	0.7776	0.7542
0.149	9.27	2900	1.2068	0.7724	0.7689
0.1258	9.58	3000	1.0880	0.7727	0.7621
0.1258	9.9	3100	0.8090	0.7746	0.7430
0.1258	10.22	3200	1.1715	0.7753	0.7611
0.1258	10.54	3300	1.2526	0.7715	0.7630
0.1258	10.86	3400	1.1210	0.7791	0.7550
0.1183	11.18	3500	1.0898	0.7821	0.7632
0.1183	11.5	3600	1.1455	0.7795	0.7666
0.1183	11.82	3700	1.1337	0.7729	0.7635
0.1183	12.14	3800	1.2093	0.7814	0.7541
0.1183	12.46	3900	1.0898	0.7793	0.7604
0.0903	12.78	4000	1.1205	0.7743	0.7613
0.0903	13.1	4100	1.0267	0.7750	0.7538

Framework versions

• Transformers 4.33.3
• Pytorch 2.0.1
• Datasets 2.14.5
• Tokenizers 0.13.3