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scenario-kd-po-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-po-ner-full-mdeberta_data-univner_full55
    results: []

scenario-kd-po-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: 46.9305
  • Precision: 0.8196
  • Recall: 0.8292
  • F1: 0.8244
  • Accuracy: 0.9823

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
135.8523 0.2911 500 108.5680 0.5906 0.3842 0.4656 0.9517
100.5734 0.5822 1000 94.6790 0.7351 0.6641 0.6978 0.9712
91.2154 0.8732 1500 88.0640 0.7607 0.7570 0.7588 0.9762
85.164 1.1643 2000 83.3722 0.8082 0.7302 0.7672 0.9767
80.2474 1.4554 2500 78.9752 0.7780 0.7925 0.7852 0.9791
76.4386 1.7465 3000 75.6330 0.8035 0.7945 0.7990 0.9801
73.0828 2.0375 3500 72.4945 0.7997 0.7970 0.7983 0.9804
69.7284 2.3286 4000 69.7705 0.7983 0.8048 0.8016 0.9804
67.0314 2.6197 4500 67.3742 0.8113 0.7970 0.8041 0.9805
64.9596 2.9108 5000 65.2223 0.8108 0.8025 0.8066 0.9805
62.6221 3.2019 5500 63.1795 0.8049 0.8169 0.8109 0.9810
60.6361 3.4929 6000 61.4200 0.8124 0.8186 0.8155 0.9814
58.8661 3.7840 6500 59.9772 0.8102 0.8192 0.8147 0.9815
57.5058 4.0751 7000 58.4410 0.8114 0.8168 0.8141 0.9811
55.9259 4.3662 7500 57.1486 0.8151 0.8179 0.8165 0.9814
54.6494 4.6573 8000 55.9362 0.8206 0.8155 0.8180 0.9814
53.5407 4.9483 8500 54.8810 0.8152 0.8205 0.8179 0.9816
52.3581 5.2394 9000 53.9021 0.8169 0.8266 0.8217 0.9816
51.3581 5.5305 9500 53.0325 0.8200 0.8204 0.8202 0.9816
50.5535 5.8216 10000 52.1425 0.8182 0.8282 0.8232 0.9818
49.8392 6.1126 10500 51.4247 0.8178 0.8254 0.8216 0.9817
48.9716 6.4037 11000 50.6978 0.8191 0.8338 0.8264 0.9823
48.3296 6.6948 11500 50.1578 0.8164 0.8290 0.8227 0.9818
47.712 6.9859 12000 49.5760 0.8234 0.8266 0.8250 0.9824
47.0545 7.2770 12500 49.0523 0.8227 0.8354 0.8290 0.9821
46.6326 7.5680 13000 48.6282 0.8174 0.8287 0.8230 0.9820
46.2306 7.8591 13500 48.2713 0.8208 0.8254 0.8231 0.9819
45.9118 8.1502 14000 47.9235 0.8185 0.8259 0.8222 0.9817
45.5272 8.4413 14500 47.6086 0.8241 0.8259 0.8250 0.9822
45.2228 8.7324 15000 47.3476 0.8250 0.8321 0.8285 0.9822
44.9978 9.0234 15500 47.1635 0.8204 0.8263 0.8233 0.9821
44.8309 9.3145 16000 47.0839 0.8264 0.8285 0.8274 0.9821
44.6998 9.6056 16500 46.9565 0.8228 0.8292 0.8260 0.9824
44.6759 9.8967 17000 46.9305 0.8196 0.8292 0.8244 0.9823

Framework versions

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