metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: roberta-hateful-meme
    results: []

roberta-hateful-meme

This model is a fine-tuned version of FacebookAI/roberta-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.4643
Accuracy: 0.631
Precision: 0.6057
Recall: 0.6081
F1: 0.6066

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	282	0.5978	0.696	0.6714	0.6698	0.6705
0.5742	2.0	564	0.6133	0.696	0.6691	0.6558	0.6599
0.5742	3.0	846	0.6326	0.694	0.6681	0.6385	0.6430
0.4682	4.0	1128	0.7503	0.662	0.6473	0.6564	0.6482
0.4682	5.0	1410	0.8875	0.687	0.6805	0.6004	0.5918
0.3907	6.0	1692	0.8072	0.651	0.6393	0.6489	0.6389
0.3907	7.0	1974	1.0383	0.639	0.6205	0.6266	0.6215
0.3445	8.0	2256	1.0678	0.643	0.6212	0.6257	0.6225
0.2996	9.0	2538	1.1221	0.649	0.6131	0.6025	0.6046
0.2996	10.0	2820	1.3531	0.646	0.6315	0.6397	0.6319
0.2749	11.0	3102	1.1258	0.655	0.6186	0.6031	0.6053
0.2749	12.0	3384	1.3594	0.641	0.6149	0.6166	0.6156
0.2635	13.0	3666	1.6264	0.637	0.5983	0.5884	0.5899
0.2635	14.0	3948	1.7278	0.636	0.6101	0.6120	0.6109
0.2382	15.0	4230	1.5670	0.635	0.6125	0.6165	0.6137
0.2387	16.0	4512	1.6334	0.63	0.6098	0.6149	0.6108
0.2387	17.0	4794	1.8990	0.632	0.5963	0.5908	0.5923
0.2214	18.0	5076	1.5244	0.644	0.6116	0.6073	0.6088
0.2214	19.0	5358	1.7948	0.639	0.6017	0.5928	0.5946
0.2138	20.0	5640	1.7595	0.638	0.6040	0.5991	0.6006
0.2138	21.0	5922	1.9552	0.633	0.6086	0.6114	0.6096
0.2038	22.0	6204	1.9481	0.636	0.6160	0.6214	0.6171
0.2038	23.0	6486	1.7951	0.648	0.6216	0.6227	0.6221
0.2003	24.0	6768	2.0499	0.636	0.6042	0.6016	0.6026
0.188	25.0	7050	2.3175	0.64	0.6147	0.6169	0.6156
0.188	26.0	7332	2.3284	0.634	0.6109	0.6145	0.6120
0.1838	27.0	7614	2.4239	0.634	0.6049	0.6046	0.6048
0.1838	28.0	7896	2.4116	0.639	0.6118	0.6127	0.6122
0.177	29.0	8178	2.4789	0.63	0.6052	0.6079	0.6062
0.177	30.0	8460	2.4643	0.631	0.6057	0.6081	0.6066

Framework versions

Transformers 4.30.2
Pytorch 1.13.1+cu117
Datasets 2.13.2
Tokenizers 0.11.0