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scenario-non-kd-po-ner-full-xlmr_data-univner_en44

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metadata
base_model: haryoaw/scenario-TCR-NER_data-univner_en
library_name: transformers
license: mit
metrics:
  - precision
  - recall
  - f1
  - accuracy
tags:
  - generated_from_trainer
model-index:
  - name: scenario-non-kd-po-ner-full-xlmr_data-univner_en44
    results: []

scenario-non-kd-po-ner-full-xlmr_data-univner_en44

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

  • Loss: 0.1427
  • Precision: 0.7495
  • Recall: 0.7774
  • F1: 0.7632
  • Accuracy: 0.9810

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: 32
  • eval_batch_size: 32
  • seed: 44
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0397 1.2755 500 0.0761 0.6856 0.7629 0.7222 0.9790
0.0137 2.5510 1000 0.0903 0.7430 0.7391 0.7410 0.9804
0.008 3.8265 1500 0.0999 0.7250 0.7723 0.7479 0.9802
0.005 5.1020 2000 0.1024 0.7375 0.7650 0.7510 0.9808
0.0033 6.3776 2500 0.1141 0.7329 0.7754 0.7535 0.9792
0.0025 7.6531 3000 0.1259 0.7346 0.7536 0.7440 0.9800
0.002 8.9286 3500 0.1351 0.7534 0.7495 0.7514 0.9799
0.0022 10.2041 4000 0.1209 0.7444 0.7536 0.7490 0.9799
0.0017 11.4796 4500 0.1340 0.7317 0.7567 0.7440 0.9796
0.0012 12.7551 5000 0.1338 0.7455 0.7671 0.7561 0.9803
0.0012 14.0306 5500 0.1365 0.7439 0.7609 0.7523 0.9794
0.0011 15.3061 6000 0.1322 0.7324 0.7764 0.7538 0.9805
0.0008 16.5816 6500 0.1314 0.7453 0.7816 0.7630 0.9810
0.0009 17.8571 7000 0.1310 0.7495 0.7588 0.7541 0.9808
0.0008 19.1327 7500 0.1396 0.7639 0.7536 0.7587 0.9815
0.0005 20.4082 8000 0.1405 0.7579 0.7712 0.7645 0.9810
0.0005 21.6837 8500 0.1397 0.7584 0.7702 0.7643 0.9814
0.0004 22.9592 9000 0.1429 0.7452 0.7660 0.7555 0.9808
0.0003 24.2347 9500 0.1437 0.7485 0.7702 0.7592 0.9806
0.0004 25.5102 10000 0.1402 0.7618 0.7712 0.7665 0.9817
0.0003 26.7857 10500 0.1420 0.7659 0.7723 0.7691 0.9815
0.0003 28.0612 11000 0.1420 0.7533 0.7650 0.7591 0.9812
0.0002 29.3367 11500 0.1427 0.7495 0.7774 0.7632 0.9810

Framework versions

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