roberta-large-finetuned-non-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: 3.1265
• Accuracy: 0.6936
• Precision: 0.6794
• Recall: 0.6782
• F1: 0.6784

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
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.0688	1.0	463	0.8847	0.6127	0.6038	0.6032	0.6014
0.8226	2.0	926	0.7622	0.6796	0.6769	0.6822	0.6716
0.6844	2.99	1389	0.8391	0.6828	0.6718	0.6563	0.6602
0.536	3.99	1852	0.8218	0.6990	0.6950	0.6807	0.6844
0.3938	4.99	2315	0.9616	0.6958	0.6967	0.7056	0.6880
0.2674	5.99	2778	1.1389	0.7033	0.6868	0.6895	0.6879
0.2073	6.98	3241	1.5578	0.6915	0.6786	0.6807	0.6792
0.1641	7.98	3704	1.9538	0.6850	0.6734	0.6715	0.6717
0.1394	8.98	4167	2.3230	0.6893	0.6733	0.6742	0.6736
0.1248	9.98	4630	2.4050	0.6936	0.6824	0.6819	0.6815
0.1002	10.98	5093	2.4227	0.6947	0.6832	0.6932	0.6795
0.0776	11.97	5556	2.5782	0.7012	0.6876	0.6923	0.6887
0.0685	12.97	6019	2.7967	0.6915	0.6836	0.6930	0.6820
0.045	13.97	6482	2.8884	0.7044	0.6873	0.6855	0.6863
0.0462	14.97	6945	2.9528	0.6947	0.6754	0.6749	0.6751
0.0444	15.97	7408	3.0356	0.6904	0.6778	0.6805	0.6778
0.0343	16.96	7871	3.0123	0.6936	0.6784	0.6762	0.6771
0.0245	17.96	8334	3.0160	0.6893	0.6720	0.6735	0.6727
0.0198	18.96	8797	3.1597	0.6904	0.6741	0.6727	0.6732
0.0189	19.96	9260	3.1265	0.6936	0.6794	0.6782	0.6784

Framework versions

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