roberta-large-finetuned-code-mixed-DS

This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 3.1340
• Accuracy: 0.7203
• Precision: 0.6584
• Recall: 0.6548
• F1: 0.6558

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 32
• seed: 43
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.9729	1.0	248	0.7491	0.6922	0.6434	0.6625	0.6358
0.7474	1.99	496	0.6947	0.7183	0.6712	0.6915	0.6760
0.5938	2.99	744	0.7370	0.7123	0.6624	0.6839	0.6642
0.4264	3.98	992	0.8820	0.7123	0.6540	0.6636	0.6492
0.2806	4.98	1240	1.2022	0.7404	0.6807	0.6694	0.6742
0.2239	5.98	1488	1.3933	0.7223	0.6593	0.6587	0.6568
0.1585	6.97	1736	1.8543	0.7304	0.6730	0.6763	0.6737
0.1302	7.97	1984	2.0783	0.7143	0.6495	0.6520	0.6504
0.1008	8.96	2232	2.3523	0.7183	0.6588	0.6561	0.6552
0.0793	9.96	2480	2.5260	0.7163	0.6516	0.6566	0.6538
0.0498	10.96	2728	2.6074	0.7425	0.6902	0.6817	0.6830
0.0484	11.95	2976	2.6758	0.7284	0.6687	0.6734	0.6709
0.0409	12.95	3224	2.8658	0.7425	0.6817	0.6756	0.6781
0.0239	13.94	3472	2.9484	0.7465	0.6980	0.6818	0.6870
0.025	14.94	3720	3.0827	0.7304	0.6778	0.6577	0.6641
0.0286	15.94	3968	3.0011	0.7183	0.6509	0.6475	0.6491
0.0264	16.93	4216	3.1581	0.7264	0.6645	0.6563	0.6595
0.009	17.93	4464	3.1200	0.7223	0.6589	0.6561	0.6569
0.012	18.92	4712	3.1364	0.7203	0.6573	0.6503	0.6525
0.017	19.92	4960	3.1340	0.7203	0.6584	0.6548	0.6558

Framework versions

• Transformers 4.20.1
• Pytorch 1.10.1+cu111
• Datasets 2.3.2
• Tokenizers 0.12.1