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scenario-kd-scr-ner-full-xlmr_data-univner_half66

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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_half66
    results: []

scenario-kd-scr-ner-full-xlmr_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: 239.7216
  • Precision: 0.3760
  • Recall: 0.2925
  • F1: 0.3290
  • Accuracy: 0.9383

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
445.7648 0.5828 500 369.2686 0.0 0.0 0.0 0.9241
345.7662 1.1655 1000 344.5894 0.3114 0.0688 0.1127 0.9255
317.4014 1.7483 1500 321.3392 0.3477 0.0814 0.1319 0.9268
296.2663 2.3310 2000 304.3693 0.4262 0.0837 0.1399 0.9278
278.6608 2.9138 2500 293.9949 0.3311 0.1453 0.2020 0.9306
265.0553 3.4965 3000 282.5093 0.3451 0.1672 0.2253 0.9328
253.4131 4.0793 3500 275.8793 0.3284 0.2137 0.2589 0.9337
243.5783 4.6620 4000 268.4692 0.3237 0.2345 0.2719 0.9341
235.5956 5.2448 4500 262.3924 0.3467 0.2506 0.2909 0.9355
228.406 5.8275 5000 257.0986 0.3634 0.2381 0.2877 0.9361
222.6923 6.4103 5500 250.0558 0.3799 0.2350 0.2904 0.9380
218.056 6.9930 6000 246.7546 0.3904 0.2479 0.3032 0.9383
213.6749 7.5758 6500 245.7390 0.3713 0.2721 0.3141 0.9373
210.549 8.1585 7000 242.9818 0.3644 0.2611 0.3043 0.9367
207.9283 8.7413 7500 240.6766 0.3721 0.2789 0.3188 0.9376
206.1746 9.3240 8000 239.1118 0.3990 0.2772 0.3271 0.9391
205.302 9.9068 8500 239.7216 0.3760 0.2925 0.3290 0.9383

Framework versions

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