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hing-roberta-NCM-run-1

Text Classification
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PyTorch
xlm-roberta
Generated from Trainer
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hing-roberta-NCM-run-1 / README.md
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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-1
    results: []

hing-roberta-NCM-run-1

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: 3.2912
  • Accuracy: 0.6667
  • Precision: 0.6513
  • Recall: 0.6494
  • F1: 0.6502

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
0.8968 1.0 927 0.8552 0.6257 0.6508 0.5961 0.5969
0.7022 2.0 1854 1.1142 0.3937 0.3270 0.3273 0.2051
0.5569 3.0 2781 0.9130 0.6591 0.6566 0.6612 0.6509
0.363 4.0 3708 1.6630 0.6526 0.6634 0.6414 0.6436
0.2801 5.0 4635 2.0458 0.6451 0.6339 0.6345 0.6330
0.1925 6.0 5562 2.3378 0.6570 0.6439 0.6254 0.6277
0.1297 7.0 6489 2.5205 0.6839 0.6719 0.6651 0.6675
0.114 8.0 7416 2.8373 0.6505 0.6379 0.6249 0.6280
0.0994 9.0 8343 2.5358 0.6634 0.6539 0.6446 0.6474
0.0977 10.0 9270 2.8244 0.6537 0.6489 0.6210 0.6238
0.0623 11.0 10197 2.7593 0.6764 0.6602 0.6487 0.6510
0.0537 12.0 11124 2.9823 0.6677 0.6679 0.6450 0.6488
0.0432 13.0 12051 3.0792 0.6537 0.6465 0.6352 0.6378
0.0406 14.0 12978 3.0707 0.6688 0.6592 0.6509 0.6534
0.0296 15.0 13905 3.3289 0.6667 0.6596 0.6452 0.6486
0.0288 16.0 14832 3.2147 0.6645 0.6592 0.6512 0.6528
0.024 17.0 15759 3.3284 0.6645 0.6470 0.6405 0.6425
0.0201 18.0 16686 3.2428 0.6688 0.6515 0.6515 0.6515
0.0176 19.0 17613 3.2680 0.6710 0.6574 0.6536 0.6547
0.0168 20.0 18540 3.2912 0.6667 0.6513 0.6494 0.6502

Framework versions

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