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muril-base-cased-finetuned-code-mixed-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: 0.9319
  • Accuracy: 0.6982
  • Precision: 0.6327
  • Recall: 0.6314
  • F1: 0.6320

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
1.0542 1.98 248 0.9786 0.5976 0.3936 0.5454 0.4330
0.9307 3.97 496 0.8836 0.5996 0.4072 0.5604 0.4399
0.8323 5.95 744 0.8266 0.5996 0.5508 0.5720 0.4527
0.7554 7.94 992 0.8006 0.6318 0.5601 0.5838 0.5232
0.6821 9.92 1240 0.8777 0.6740 0.5929 0.5875 0.5836
0.6173 11.9 1488 0.8389 0.6640 0.5918 0.6031 0.5881
0.5552 13.89 1736 0.9003 0.6962 0.6240 0.6160 0.6191
0.4932 15.87 1984 0.8979 0.6982 0.6266 0.6231 0.6245
0.4446 17.86 2232 0.9104 0.7002 0.6310 0.6290 0.6298
0.4084 19.84 2480 0.9284 0.7002 0.6278 0.6255 0.6264
0.3763 21.82 2728 0.9228 0.7082 0.6436 0.6380 0.6398
0.3575 23.81 2976 0.9319 0.6982 0.6327 0.6314 0.6320

Framework versions

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