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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-CM-run-4
    results: []

hing-roberta-CM-run-4

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: 2.5827
  • Accuracy: 0.7525
  • Precision: 0.6967
  • Recall: 0.7004
  • F1: 0.6980

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
0.8734 1.0 497 0.7673 0.7203 0.6617 0.6600 0.6604
0.6245 2.0 994 0.7004 0.7485 0.6951 0.7137 0.7015
0.4329 3.0 1491 1.0469 0.7223 0.6595 0.6640 0.6538
0.2874 4.0 1988 1.3103 0.7586 0.7064 0.7157 0.7104
0.1837 5.0 2485 1.7916 0.7425 0.6846 0.6880 0.6861
0.1121 6.0 2982 2.0721 0.7465 0.7064 0.7041 0.7003
0.0785 7.0 3479 2.3469 0.7425 0.6898 0.6795 0.6807
0.0609 8.0 3976 2.2775 0.7404 0.6819 0.6881 0.6845
0.0817 9.0 4473 2.1992 0.7686 0.7342 0.7147 0.7166
0.042 10.0 4970 2.2359 0.7565 0.7211 0.7141 0.7106
0.0463 11.0 5467 2.2291 0.7646 0.7189 0.7186 0.7177
0.027 12.0 5964 2.3955 0.7525 0.6994 0.7073 0.7028
0.0314 13.0 6461 2.4256 0.7565 0.7033 0.7153 0.7082
0.0251 14.0 6958 2.4578 0.7565 0.7038 0.7025 0.7027
0.0186 15.0 7455 2.5984 0.7565 0.7141 0.6945 0.6954
0.0107 16.0 7952 2.5068 0.7425 0.6859 0.7016 0.6912
0.0134 17.0 8449 2.5876 0.7606 0.7018 0.7041 0.7029
0.0145 18.0 8946 2.6011 0.7626 0.7072 0.7079 0.7073
0.0108 19.0 9443 2.5861 0.7545 0.6973 0.7017 0.6990
0.0076 20.0 9940 2.5827 0.7525 0.6967 0.7004 0.6980

Framework versions

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