twitter-roberta-base-sentiment-sentiment-memes-30epcohs

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

• Loss: 1.3027
• Accuracy: 0.8517
• Precision: 0.8536
• Recall: 0.8517
• F1: 0.8523

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• 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
0.2504	1.0	2147	0.7129	0.8087	0.8112	0.8087	0.8036
0.2449	2.0	4294	0.7500	0.8229	0.8279	0.8229	0.8240
0.2652	3.0	6441	0.7460	0.8181	0.8185	0.8181	0.8149
0.2585	4.0	8588	0.7906	0.8155	0.8152	0.8155	0.8153
0.2534	5.0	10735	0.8178	0.8061	0.8180	0.8061	0.8080
0.2498	6.0	12882	0.8139	0.8166	0.8163	0.8166	0.8164
0.2825	7.0	15029	0.7494	0.8155	0.8210	0.8155	0.8168
0.2459	8.0	17176	0.8870	0.8061	0.8122	0.8061	0.8075
0.2303	9.0	19323	0.8699	0.7987	0.8060	0.7987	0.8003
0.2425	10.0	21470	0.8043	0.8244	0.8275	0.8244	0.8253
0.2143	11.0	23617	0.9163	0.8208	0.8251	0.8208	0.8219
0.2054	12.0	25764	0.8330	0.8239	0.8258	0.8239	0.8245
0.208	13.0	27911	1.0673	0.8134	0.8216	0.8134	0.8150
0.1668	14.0	30058	0.9071	0.8270	0.8276	0.8270	0.8273
0.1571	15.0	32205	0.9294	0.8339	0.8352	0.8339	0.8344
0.1857	16.0	34352	0.9909	0.8354	0.8350	0.8354	0.8352
0.1476	17.0	36499	0.9747	0.8433	0.8436	0.8433	0.8434
0.1341	18.0	38646	0.9372	0.8422	0.8415	0.8422	0.8415
0.1181	19.0	40793	1.0301	0.8433	0.8443	0.8433	0.8437
0.1192	20.0	42940	1.1332	0.8407	0.8415	0.8407	0.8410
0.0983	21.0	45087	1.2002	0.8428	0.8498	0.8428	0.8440
0.0951	22.0	47234	1.2141	0.8475	0.8504	0.8475	0.8483
0.0784	23.0	49381	1.1652	0.8407	0.8453	0.8407	0.8417
0.0623	24.0	51528	1.1730	0.8417	0.8443	0.8417	0.8425
0.054	25.0	53675	1.2900	0.8454	0.8496	0.8454	0.8464
0.0584	26.0	55822	1.2831	0.8480	0.8497	0.8480	0.8486
0.0531	27.0	57969	1.3043	0.8506	0.8524	0.8506	0.8512
0.0522	28.0	60116	1.2891	0.8527	0.8554	0.8527	0.8534
0.037	29.0	62263	1.3077	0.8538	0.8559	0.8538	0.8544
0.038	30.0	64410	1.3027	0.8517	0.8536	0.8517	0.8523

Framework versions

• Transformers 4.13.0.dev0
• Pytorch 1.11.0+cu102
• Datasets 1.15.2.dev0
• Tokenizers 0.10.1