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scenario-kd-po-ner-full-xlmr_data-univner_half44

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metadata
base_model: haryoaw/scenario-TCR-NER_data-univner_half
library_name: transformers
license: mit
metrics:
  - precision
  - recall
  - f1
  - accuracy
tags:
  - generated_from_trainer
model-index:
  - name: scenario-kd-po-ner-full-xlmr_data-univner_half44
    results: []

scenario-kd-po-ner-full-xlmr_data-univner_half44

This model is a fine-tuned version of haryoaw/scenario-TCR-NER_data-univner_half on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 53.5855
  • Precision: 0.7926
  • Recall: 0.7941
  • F1: 0.7934
  • Accuracy: 0.9792

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: 32
  • seed: 44
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
94.1316 0.5828 500 77.7639 0.7573 0.7254 0.7410 0.9749
69.1816 1.1655 1000 70.0861 0.7700 0.7553 0.7626 0.9771
62.5325 1.7483 1500 66.1010 0.7697 0.7728 0.7712 0.9774
58.8032 2.3310 2000 63.2843 0.7722 0.7813 0.7767 0.9781
55.8439 2.9138 2500 61.3899 0.7711 0.7839 0.7774 0.9777
53.6386 3.4965 3000 59.5744 0.7829 0.7804 0.7816 0.9782
51.8854 4.0793 3500 58.4745 0.7896 0.7831 0.7864 0.9784
50.3704 4.6620 4000 57.3648 0.7888 0.7917 0.7902 0.9787
49.2945 5.2448 4500 56.3673 0.8003 0.7807 0.7904 0.9787
48.3678 5.8275 5000 55.7695 0.7906 0.7840 0.7873 0.9787
47.4721 6.4103 5500 55.1454 0.7836 0.7964 0.7900 0.9792
46.9783 6.9930 6000 54.6410 0.7931 0.7976 0.7953 0.9790
46.3896 7.5758 6500 54.2132 0.8004 0.7902 0.7953 0.9792
45.895 8.1585 7000 53.9535 0.7906 0.7945 0.7925 0.9792
45.6796 8.7413 7500 53.7738 0.7918 0.7895 0.7906 0.9788
45.4159 9.3240 8000 53.6266 0.7904 0.7950 0.7927 0.9793
45.3274 9.9068 8500 53.5855 0.7926 0.7941 0.7934 0.9792

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.19.1