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scenario-kd-scr-ner-full-mdeberta_data-univner_full55

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metadata
base_model: haryoaw/scenario-TCR-NER_data-univner_full
library_name: transformers
license: mit
metrics:
  - precision
  - recall
  - f1
  - accuracy
tags:
  - generated_from_trainer
model-index:
  - name: scenario-kd-scr-ner-full-mdeberta_data-univner_full55
    results: []

scenario-kd-scr-ner-full-mdeberta_data-univner_full55

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

  • Loss: 182.5497
  • Precision: 0.6804
  • Recall: 0.6154
  • F1: 0.6463
  • Accuracy: 0.9657

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: 55
  • 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
627.6279 0.2911 500 560.3030 0.0 0.0 0.0 0.9241
529.0723 0.5822 1000 503.1468 0.3145 0.0378 0.0675 0.9253
481.2237 0.8732 1500 462.0725 0.3110 0.0811 0.1286 0.9284
443.6023 1.1643 2000 431.7476 0.4241 0.0822 0.1378 0.9304
413.7332 1.4554 2500 404.1758 0.4897 0.3199 0.3870 0.9448
389.6206 1.7465 3000 381.5862 0.5329 0.3800 0.4437 0.9494
368.3142 2.0375 3500 363.5359 0.5888 0.3769 0.4596 0.9507
349.4665 2.3286 4000 346.6397 0.5410 0.4793 0.5083 0.9539
333.3893 2.6197 4500 331.5422 0.6223 0.4291 0.5079 0.9550
318.8641 2.9108 5000 316.8669 0.5984 0.5612 0.5792 0.9597
303.8825 3.2019 5500 303.3675 0.6190 0.5569 0.5863 0.9608
290.8802 3.4929 6000 291.3924 0.6347 0.5390 0.5830 0.9606
279.9562 3.7840 6500 281.3740 0.6484 0.5403 0.5894 0.9613
268.853 4.0751 7000 270.4638 0.6513 0.5578 0.6009 0.9615
257.9733 4.3662 7500 260.5476 0.6536 0.5817 0.6156 0.9635
248.9305 4.6573 8000 251.8452 0.6631 0.5926 0.6258 0.9638
240.7242 4.9483 8500 243.8925 0.6587 0.5882 0.6215 0.9633
232.3709 5.2394 9000 236.3189 0.6514 0.6077 0.6288 0.9640
224.6698 5.5305 9500 229.3991 0.6675 0.5722 0.6162 0.9629
218.3664 5.8216 10000 223.3077 0.6788 0.5823 0.6269 0.9639
212.9249 6.1126 10500 217.2704 0.6717 0.6003 0.6340 0.9643
206.6058 6.4037 11000 211.8754 0.6570 0.6226 0.6393 0.9649
201.722 6.6948 11500 207.1151 0.6680 0.6210 0.6436 0.9650
197.034 6.9859 12000 202.9470 0.6805 0.6047 0.6403 0.9649
192.5555 7.2770 12500 199.1373 0.6749 0.6130 0.6425 0.9651
189.1607 7.5680 13000 195.9332 0.6605 0.6279 0.6438 0.9652
186.1884 7.8591 13500 193.1577 0.6772 0.6057 0.6395 0.9652
183.2947 8.1502 14000 190.2176 0.6697 0.6318 0.6502 0.9654
180.5764 8.4413 14500 187.9859 0.6970 0.6091 0.6501 0.9657
178.5341 8.7324 15000 186.4189 0.6843 0.5976 0.6380 0.9645
176.79 9.0234 15500 184.5720 0.6846 0.6198 0.6506 0.9661
175.528 9.3145 16000 183.8221 0.7059 0.5905 0.6431 0.9650
174.3179 9.6056 16500 182.7365 0.6842 0.6188 0.6498 0.9658
174.2736 9.8967 17000 182.5497 0.6804 0.6154 0.6463 0.9657

Framework versions

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