muril-base-cased-finetuned-TRAC-DS

This model is a fine-tuned version of google/muril-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.1894
• Accuracy: 0.6838
• Precision: 0.6534
• Recall: 0.6513
• F1: 0.6522

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: 32
• eval_batch_size: 32
• seed: 43
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.0109	1.99	612	0.9284	0.5948	0.4327	0.5193	0.4509
0.8635	3.99	1224	0.8556	0.6291	0.6012	0.5865	0.5888
0.764	5.98	1836	0.8585	0.6609	0.6249	0.6275	0.6260
0.6744	7.97	2448	0.8469	0.6732	0.6391	0.6408	0.6398
0.5865	9.97	3060	0.8438	0.6667	0.6424	0.6395	0.6395
0.4978	11.96	3672	0.9269	0.6855	0.6532	0.6582	0.6542
0.4245	13.95	4284	0.9934	0.6699	0.6397	0.6482	0.6396
0.378	15.95	4896	1.0488	0.6830	0.6530	0.6446	0.6474
0.3349	17.94	5508	1.0548	0.6806	0.6505	0.6536	0.6518
0.3019	19.93	6120	1.1092	0.6757	0.6476	0.6497	0.6482
0.2869	21.93	6732	1.1515	0.6814	0.6507	0.6514	0.6510
0.2575	23.92	7344	1.1894	0.6838	0.6534	0.6513	0.6522

Framework versions

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