metadata

license: mit
base_model: haryoaw/scenario-teacher-data-hate_speech_filipino-model-xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - hate_speech_filipino
metrics:
  - accuracy
  - f1
model-index:
  - name: >-
      scenario-non-kd-from-post-finetune-div-2-data-hate_speech_filipino-model-haryoaw
    results: []

scenario-non-kd-from-post-finetune-div-2-data-hate_speech_filipino-model-haryoaw

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

Loss: 1.3495
Accuracy: 0.7779
F1: 0.7601

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	0.5751	0.7339	0.7510
No log	0.64	200	0.5171	0.7717	0.7450
No log	0.96	300	0.4969	0.7784	0.7539
No log	1.28	400	0.5408	0.7819	0.7548
0.4175	1.6	500	0.5410	0.7635	0.7597
0.4175	1.92	600	0.5166	0.7767	0.7324
0.4175	2.24	700	0.5823	0.7651	0.7450
0.4175	2.56	800	0.5731	0.7672	0.7293
0.4175	2.88	900	0.6860	0.7769	0.7458
0.2936	3.19	1000	0.7409	0.7684	0.7659
0.2936	3.51	1100	0.6544	0.7772	0.7487
0.2936	3.83	1200	0.6719	0.7604	0.7613
0.2936	4.15	1300	0.8242	0.7781	0.7471
0.2936	4.47	1400	0.8741	0.7838	0.7472
0.199	4.79	1500	0.7415	0.7755	0.7509
0.199	5.11	1600	0.9389	0.7897	0.7615
0.199	5.43	1700	0.7985	0.7840	0.7693
0.199	5.75	1800	0.9223	0.7741	0.7600
0.199	6.07	1900	1.0076	0.7727	0.7667
0.1553	6.39	2000	0.8541	0.7800	0.7682
0.1553	6.71	2100	0.9460	0.7810	0.7600
0.1553	7.03	2200	1.0575	0.7791	0.7571
0.1553	7.35	2300	1.0487	0.7687	0.7657
0.1553	7.67	2400	0.8495	0.7732	0.7568
0.1316	7.99	2500	0.9467	0.7812	0.7658
0.1316	8.31	2600	1.0491	0.7722	0.7611
0.1316	8.63	2700	1.0363	0.7691	0.7275
0.1316	8.95	2800	0.9130	0.7758	0.7448
0.1316	9.27	2900	1.4607	0.7717	0.7562
0.1137	9.58	3000	1.1874	0.7727	0.7567
0.1137	9.9	3100	1.1752	0.7836	0.7672
0.1137	10.22	3200	1.3495	0.7779	0.7601

Framework versions

Transformers 4.33.3
Pytorch 2.0.1
Datasets 2.14.5
Tokenizers 0.13.3