scenario-NON-KD-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual

This model is a fine-tuned version of xlm-roberta-base on the tweet_sentiment_multilingual dataset. It achieves the following results on the evaluation set:

• Loss: 5.5508
• Accuracy: 0.4985
• F1: 0.4951

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: 11213
• 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
1.1212	1.09	500	1.1080	0.3789	0.2909
0.9761	2.17	1000	1.1799	0.4823	0.4755
0.625	3.26	1500	1.5124	0.5015	0.4837
0.3503	4.35	2000	1.5134	0.5220	0.5211
0.205	5.43	2500	2.4452	0.5143	0.5135
0.1271	6.52	3000	2.9579	0.4911	0.4721
0.1042	7.61	3500	2.7856	0.4892	0.4895
0.079	8.7	4000	3.0258	0.5035	0.4994
0.061	9.78	4500	3.6011	0.4884	0.4892
0.0504	10.87	5000	3.5818	0.4819	0.4791
0.0474	11.96	5500	3.3451	0.5031	0.4913
0.0462	13.04	6000	3.5810	0.4988	0.4966
0.0317	14.13	6500	3.6952	0.4988	0.4967
0.0325	15.22	7000	3.9715	0.4919	0.4847
0.0269	16.3	7500	3.8701	0.4942	0.4934
0.0268	17.39	8000	4.2094	0.4826	0.4834
0.0256	18.48	8500	3.9200	0.4934	0.4947
0.0228	19.57	9000	3.7341	0.4946	0.4932
0.0183	20.65	9500	4.2147	0.4915	0.4867
0.0176	21.74	10000	4.3251	0.4985	0.4942
0.012	22.83	10500	4.2855	0.5042	0.4920
0.011	23.91	11000	4.2243	0.4946	0.4874
0.0106	25.0	11500	4.4153	0.4877	0.4855
0.0085	26.09	12000	4.6839	0.4946	0.4920
0.0105	27.17	12500	4.5992	0.4923	0.4933
0.0095	28.26	13000	4.7752	0.4985	0.4952
0.0083	29.35	13500	4.7973	0.4942	0.4948
0.007	30.43	14000	4.7373	0.4969	0.4937
0.0041	31.52	14500	5.0320	0.4954	0.4816
0.0042	32.61	15000	5.1395	0.4934	0.4921
0.0049	33.7	15500	4.9622	0.4958	0.4957
0.0054	34.78	16000	5.2670	0.4846	0.4826
0.0042	35.87	16500	5.1694	0.4958	0.4951
0.004	36.96	17000	5.2387	0.4938	0.4867
0.002	38.04	17500	5.4227	0.4842	0.4797
0.0025	39.13	18000	5.4860	0.4896	0.4849
0.003	40.22	18500	5.3279	0.4923	0.4870
0.0026	41.3	19000	5.2518	0.4923	0.4924
0.0019	42.39	19500	5.1927	0.4996	0.4989
0.0018	43.48	20000	5.2447	0.4992	0.4991
0.0007	44.57	20500	5.4302	0.5015	0.4986
0.0002	45.65	21000	5.4714	0.4950	0.4936
0.0001	46.74	21500	5.5014	0.4969	0.4955
0.0003	47.83	22000	5.5333	0.4973	0.4934
0.0001	48.91	22500	5.5591	0.4969	0.4942
0.0002	50.0	23000	5.5508	0.4985	0.4951

Framework versions

• Transformers 4.33.3
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.13.3