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Librarian Bot: Add base_model information to model (#1)
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metadata
license: cc-by-4.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
base_model: l3cube-pune/hing-roberta
model-index:
  - name: hing-roberta-NCM-run-2
    results: []

hing-roberta-NCM-run-2

This model is a fine-tuned version of l3cube-pune/hing-roberta on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 3.3647
  • Accuracy: 0.6483
  • Precision: 0.6369
  • Recall: 0.6325
  • F1: 0.6341

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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • 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
0.8973 1.0 927 0.8166 0.6483 0.6545 0.6576 0.6460
0.6827 2.0 1854 0.9071 0.6526 0.6444 0.6261 0.6299
0.4672 3.0 2781 1.1600 0.6764 0.6657 0.6634 0.6643
0.3388 4.0 3708 1.7426 0.6548 0.6406 0.6442 0.6418
0.2786 5.0 4635 1.9385 0.6505 0.6484 0.6437 0.6434
0.1794 6.0 5562 2.3158 0.6472 0.6564 0.6365 0.6388
0.12 7.0 6489 2.6961 0.6591 0.6458 0.6531 0.6466
0.1298 8.0 7416 2.7196 0.6505 0.6523 0.6307 0.6342
0.0941 9.0 8343 2.5853 0.6548 0.6406 0.6426 0.6415
0.0696 10.0 9270 2.8386 0.6613 0.6616 0.6314 0.6348
0.0722 11.0 10197 2.9658 0.6537 0.6356 0.6356 0.6355
0.0509 12.0 11124 3.3286 0.6429 0.6262 0.6192 0.6214
0.0444 13.0 12051 3.1654 0.6483 0.6347 0.6302 0.6319
0.0341 14.0 12978 2.9509 0.6537 0.6430 0.6394 0.6401
0.0345 15.0 13905 3.3416 0.6656 0.6514 0.6488 0.6499
0.0303 16.0 14832 3.3874 0.6419 0.6267 0.6339 0.6272
0.0245 17.0 15759 3.2854 0.6570 0.6428 0.6420 0.6421
0.0174 18.0 16686 3.2863 0.6602 0.6569 0.6427 0.6465
0.0136 19.0 17613 3.3674 0.6494 0.6361 0.6341 0.6349
0.0111 20.0 18540 3.3647 0.6483 0.6369 0.6325 0.6341

Framework versions

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