scenario-kd-scr-ner-full-mdeberta_data-univner_half66

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: 370.8975
  • Precision: 0.3767
  • Recall: 0.4188
  • F1: 0.3966
  • Accuracy: 0.9231

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: 66
  • 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
639.3549 0.5828 500 570.6539 0.7143 0.0014 0.0029 0.9241
539.3257 1.1655 1000 524.4883 0.3354 0.0698 0.1156 0.9262
488.2303 1.7483 1500 491.0639 0.3430 0.1431 0.2020 0.9307
453.2781 2.3310 2000 469.1648 0.3407 0.2689 0.3006 0.9306
427.1957 2.9138 2500 451.5781 0.3777 0.3150 0.3435 0.9321
406.9511 3.4965 3000 440.5732 0.3440 0.3317 0.3377 0.9249
390.8488 4.0793 3500 429.3404 0.3468 0.3828 0.3639 0.9241
376.6864 4.6620 4000 421.3924 0.3388 0.4020 0.3677 0.9188
365.0702 5.2448 4500 406.7076 0.3634 0.4164 0.3881 0.9260
354.3511 5.8275 5000 395.5925 0.4385 0.3870 0.4111 0.9351
345.6264 6.4103 5500 389.3924 0.4030 0.4069 0.4049 0.9305
338.4962 6.9930 6000 387.7135 0.3570 0.4142 0.3835 0.9201
331.6204 7.5758 6500 377.1432 0.4232 0.3945 0.4083 0.9326
326.8345 8.1585 7000 375.8239 0.3924 0.4242 0.4077 0.9253
322.7788 8.7413 7500 373.5773 0.3755 0.4210 0.3970 0.9233
320.0888 9.3240 8000 369.3580 0.4010 0.4204 0.4105 0.9279
318.3835 9.9068 8500 370.8975 0.3767 0.4188 0.3966 0.9231

Framework versions

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