metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - hate_speech_filipino
metrics:
  - accuracy
  - f1
model-index:
  - name: >-
      scenario-kd-from-pre-finetune-silver-div-2-data-hate_speech_filipino-model-xlm-r
    results: []

scenario-kd-from-pre-finetune-silver-div-2-data-hate_speech_filipino-model-xlm-r

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: 0.4122
Accuracy: 0.7810
F1: 0.7699

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	1.4902	0.7143	0.7119
No log	0.64	200	1.0099	0.7379	0.7416
No log	0.96	300	0.9000	0.7531	0.7410
No log	1.28	400	0.7416	0.7595	0.7548
1.3478	1.6	500	0.7405	0.7703	0.7495
1.3478	1.92	600	0.6557	0.7743	0.7569
1.3478	2.24	700	0.6338	0.7661	0.7656
1.3478	2.56	800	0.6421	0.7682	0.7429
1.3478	2.88	900	0.5503	0.7746	0.7649
0.667	3.19	1000	0.5596	0.7741	0.7623
0.667	3.51	1100	0.6638	0.7729	0.7365
0.667	3.83	1200	0.5504	0.7798	0.7545
0.667	4.15	1300	0.5163	0.7769	0.7613
0.667	4.47	1400	0.6817	0.7498	0.7641
0.4362	4.79	1500	0.5097	0.7717	0.7725
0.4362	5.11	1600	0.5382	0.7765	0.7460
0.4362	5.43	1700	0.5237	0.7800	0.7493
0.4362	5.75	1800	0.4712	0.7786	0.7552
0.4362	6.07	1900	0.4671	0.7755	0.7570
0.3401	6.39	2000	0.4376	0.7859	0.7752
0.3401	6.71	2100	0.4753	0.7706	0.7720
0.3401	7.03	2200	0.4661	0.7854	0.7706
0.3401	7.35	2300	0.4555	0.7774	0.7605
0.3401	7.67	2400	0.4362	0.7812	0.7683
0.2844	7.99	2500	0.4511	0.7864	0.7734
0.2844	8.31	2600	0.4433	0.7767	0.7707
0.2844	8.63	2700	0.5001	0.7656	0.7699
0.2844	8.95	2800	0.4337	0.7810	0.7723
0.2844	9.27	2900	0.4145	0.7812	0.7668
0.2436	9.58	3000	0.4143	0.7840	0.7698
0.2436	9.9	3100	0.3993	0.7786	0.7687
0.2436	10.22	3200	0.4030	0.7899	0.7734
0.2436	10.54	3300	0.4054	0.7826	0.7666
0.2436	10.86	3400	0.3996	0.7762	0.7716
0.2246	11.18	3500	0.4122	0.7810	0.7699

Framework versions

Transformers 4.33.3
Pytorch 2.0.1
Datasets 2.14.5
Tokenizers 0.13.3