metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - hate_speech_filipino
metrics:
  - accuracy
  - f1
model-index:
  - name: >-
      scenario-non-kd-from-scratch-data-hate_speech_filipino-model-xlm-roberta-base
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: hate_speech_filipino
          type: hate_speech_filipino
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7247164461247637
          - name: F1
            type: f1
            value: 0.7256887214504355

scenario-non-kd-from-scratch-data-hate_speech_filipino-model-xlm-roberta-base

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: 1.0612
Accuracy: 0.7247
F1: 0.7257

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	0.6595	0.6307	0.6853
No log	0.64	200	0.5676	0.7032	0.6620
No log	0.96	300	0.5294	0.7358	0.7069
No log	1.28	400	0.5112	0.7493	0.7084
0.585	1.6	500	0.5554	0.7283	0.7420
0.585	1.92	600	0.5201	0.7349	0.6679
0.585	2.24	700	0.5838	0.7361	0.7415
0.585	2.56	800	0.5693	0.7325	0.7421
0.585	2.88	900	0.5469	0.7517	0.7128
0.3954	3.19	1000	0.6406	0.7509	0.7361
0.3954	3.51	1100	0.5834	0.7401	0.7158
0.3954	3.83	1200	0.6038	0.7538	0.7324
0.3954	4.15	1300	0.7079	0.7436	0.7230
0.3954	4.47	1400	0.7422	0.7474	0.7182
0.2591	4.79	1500	0.6393	0.75	0.7307
0.2591	5.11	1600	0.7890	0.7481	0.7307
0.2591	5.43	1700	1.0788	0.7332	0.6651
0.2591	5.75	1800	0.8036	0.7353	0.7157
0.2591	6.07	1900	1.0868	0.7474	0.7167
0.1729	6.39	2000	1.3150	0.7441	0.7027
0.1729	6.71	2100	1.0097	0.7351	0.7268
0.1729	7.03	2200	1.0160	0.7389	0.7074
0.1729	7.35	2300	1.0612	0.7247	0.7257

Framework versions

Transformers 4.33.3
Pytorch 2.0.1
Datasets 2.14.5
Tokenizers 0.13.3