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scenario-kd-scr-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-scr-ner-full-xlmr_data-univner_half44
    results: []

scenario-kd-scr-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: 238.5785
  • Precision: 0.3821
  • Recall: 0.2681
  • F1: 0.3151
  • Accuracy: 0.9380

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
446.8617 0.5828 500 369.9693 0.0 0.0 0.0 0.9241
345.828 1.1655 1000 339.4001 0.4474 0.0074 0.0145 0.9243
316.4135 1.7483 1500 320.2157 0.3593 0.0778 0.1279 0.9271
295.3783 2.3310 2000 303.7513 0.4250 0.0740 0.1261 0.9274
278.865 2.9138 2500 291.9803 0.3462 0.1311 0.1902 0.9310
265.0536 3.4965 3000 282.1734 0.3378 0.1561 0.2135 0.9319
252.7824 4.0793 3500 274.9576 0.3486 0.2065 0.2593 0.9342
243.1838 4.6620 4000 265.2098 0.3825 0.1775 0.2424 0.9352
235.3429 5.2448 4500 260.4372 0.3720 0.2352 0.2882 0.9369
227.8851 5.8275 5000 256.3334 0.3570 0.2534 0.2964 0.9365
221.9237 6.4103 5500 250.2192 0.3931 0.2375 0.2961 0.9383
217.836 6.9930 6000 245.7306 0.3999 0.2268 0.2894 0.9385
213.3779 7.5758 6500 242.3217 0.3961 0.2378 0.2972 0.9391
209.3609 8.1585 7000 241.0757 0.3846 0.2448 0.2992 0.9381
207.6172 8.7413 7500 239.1901 0.3905 0.2535 0.3074 0.9391
205.3707 9.3240 8000 239.5822 0.3728 0.2759 0.3171 0.9378
204.5786 9.9068 8500 238.5785 0.3821 0.2681 0.3151 0.9380

Framework versions

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