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scenario-non-kd-po-ner-full-xlmr_data-univner_en66

Token Classification
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xlm-roberta
Generated from Trainer
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metadata
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_en
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: scenario-non-kd-po-ner-full-xlmr_data-univner_en66
    results: []

scenario-non-kd-po-ner-full-xlmr_data-univner_en66

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: 0.1477
  • Precision: 0.8101
  • Recall: 0.7992
  • F1: 0.8046
  • Accuracy: 0.9836

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: 66
  • 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
0.0036 1.2755 500 0.1194 0.8112 0.7785 0.7945 0.9831
0.0035 2.5510 1000 0.1172 0.7899 0.8137 0.8016 0.9838
0.0029 3.8265 1500 0.1173 0.7804 0.7909 0.7856 0.9822
0.0019 5.1020 2000 0.1175 0.7907 0.7940 0.7924 0.9841
0.0018 6.3776 2500 0.1271 0.7935 0.7836 0.7885 0.9830
0.0021 7.6531 3000 0.1281 0.7975 0.7909 0.7942 0.9837
0.0012 8.9286 3500 0.1170 0.7908 0.8023 0.7965 0.9843
0.0018 10.2041 4000 0.1324 0.8084 0.7950 0.8017 0.9840
0.0011 11.4796 4500 0.1304 0.7926 0.8188 0.8055 0.9839
0.0007 12.7551 5000 0.1370 0.7958 0.7867 0.7913 0.9838
0.0011 14.0306 5500 0.1297 0.7867 0.8095 0.7980 0.9837
0.0012 15.3061 6000 0.1254 0.7772 0.8126 0.7945 0.9830
0.0007 16.5816 6500 0.1374 0.8304 0.7909 0.8102 0.9831
0.0006 17.8571 7000 0.1369 0.7903 0.8116 0.8008 0.9832
0.0003 19.1327 7500 0.1379 0.7961 0.8043 0.8002 0.9841
0.0003 20.4082 8000 0.1365 0.7953 0.8002 0.7977 0.9838
0.0004 21.6837 8500 0.1458 0.7879 0.8230 0.8051 0.9835
0.0004 22.9592 9000 0.1475 0.8101 0.7992 0.8046 0.9835
0.0004 24.2347 9500 0.1405 0.7931 0.8054 0.7992 0.9837
0.0001 25.5102 10000 0.1391 0.7949 0.8147 0.8047 0.9843
0.0002 26.7857 10500 0.1432 0.8067 0.8033 0.8050 0.9840
0.0002 28.0612 11000 0.1439 0.8067 0.7992 0.8029 0.9836
0.0001 29.3367 11500 0.1477 0.8101 0.7992 0.8046 0.9836

Framework versions

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