scenario-NON-KD-PO-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_

This model is a fine-tuned version of haryoaw/scenario-MDBT-TCR-TSM on the tweet_sentiment_multilingual dataset. It achieves the following results on the evaluation set:

• Loss: 5.1975
• Accuracy: 0.5644
• F1: 0.5639

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: 55
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.9766	1.0870	500	0.9668	0.5679	0.5672
0.7795	2.1739	1000	0.9971	0.5880	0.5855
0.6025	3.2609	1500	1.2126	0.5760	0.5682
0.4143	4.3478	2000	1.3620	0.5733	0.5720
0.288	5.4348	2500	1.7279	0.5644	0.5624
0.2004	6.5217	3000	2.0524	0.5617	0.5629
0.1439	7.6087	3500	2.4904	0.5590	0.5594
0.1197	8.6957	4000	2.3080	0.5606	0.5581
0.1096	9.7826	4500	2.6392	0.5667	0.5629
0.0904	10.8696	5000	2.8438	0.5478	0.5498
0.0783	11.9565	5500	2.9731	0.5625	0.5558
0.0617	13.0435	6000	3.5176	0.5586	0.5596
0.0571	14.1304	6500	3.5156	0.5644	0.5657
0.0524	15.2174	7000	3.1091	0.5594	0.5574
0.0535	16.3043	7500	2.9773	0.5664	0.5634
0.0423	17.3913	8000	3.6352	0.5633	0.5641
0.0385	18.4783	8500	3.7201	0.5675	0.5647
0.0372	19.5652	9000	4.0422	0.5625	0.5599
0.0332	20.6522	9500	3.5064	0.5706	0.5708
0.0293	21.7391	10000	4.0279	0.5617	0.5617
0.0192	22.8261	10500	4.4137	0.5671	0.5637
0.0278	23.9130	11000	4.0800	0.5621	0.5599
0.0239	25.0	11500	3.9079	0.5606	0.5599
0.0221	26.0870	12000	4.1928	0.5702	0.5672
0.0164	27.1739	12500	4.3024	0.5586	0.5524
0.0155	28.2609	13000	4.4464	0.5660	0.5659
0.0206	29.3478	13500	4.4741	0.5579	0.5569
0.0153	30.4348	14000	4.2231	0.5505	0.5514
0.0112	31.5217	14500	4.4476	0.5644	0.5620
0.0119	32.6087	15000	4.4276	0.5583	0.5561
0.0109	33.6957	15500	4.4156	0.5621	0.5617
0.0113	34.7826	16000	4.0354	0.5633	0.5621
0.0084	35.8696	16500	4.7380	0.5590	0.5568
0.0085	36.9565	17000	4.3942	0.5644	0.5632
0.01	38.0435	17500	4.4828	0.5687	0.5682
0.0056	39.1304	18000	4.7518	0.5640	0.5605
0.0041	40.2174	18500	4.8487	0.5725	0.5719
0.0058	41.3043	19000	4.5515	0.5698	0.5701
0.0044	42.3913	19500	4.9174	0.5640	0.5630
0.0049	43.4783	20000	4.8322	0.5664	0.5659
0.004	44.5652	20500	4.8014	0.5660	0.5656
0.0008	45.6522	21000	5.1207	0.5644	0.5647
0.0033	46.7391	21500	5.1209	0.5637	0.5636
0.002	47.8261	22000	5.1817	0.5610	0.5605
0.0012	48.9130	22500	5.2011	0.5640	0.5630
0.0022	50.0	23000	5.1975	0.5644	0.5639

Framework versions

• Transformers 4.44.2
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.19.1