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scenario-kd-scr-ner-full-mdeberta_data-univner_half44

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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-mdeberta_data-univner_half44
    results: []

scenario-kd-scr-ner-full-mdeberta_data-univner_half44

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: 364.7336
  • Precision: 0.3918
  • Recall: 0.4292
  • F1: 0.4096
  • Accuracy: 0.9267

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: 44
  • 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
637.6988 0.5828 500 570.9927 0.6154 0.0012 0.0023 0.9241
541.2212 1.1655 1000 524.6858 0.3571 0.0353 0.0643 0.9251
490.5141 1.7483 1500 492.2816 0.3048 0.1754 0.2227 0.9310
455.7006 2.3310 2000 474.9406 0.3064 0.2626 0.2828 0.9273
430.062 2.9138 2500 452.2111 0.3632 0.3073 0.3329 0.9298
408.0248 3.4965 3000 434.8791 0.3994 0.3220 0.3566 0.9341
390.2744 4.0793 3500 424.2673 0.3727 0.3444 0.3580 0.9307
374.3932 4.6620 4000 411.0975 0.4020 0.3979 0.3999 0.9328
362.2752 5.2448 4500 403.7659 0.3614 0.3963 0.3781 0.9239
350.9508 5.8275 5000 392.9673 0.3736 0.3855 0.3795 0.9296
341.7654 6.4103 5500 385.3136 0.4030 0.3972 0.4001 0.9302
334.4205 6.9930 6000 380.2038 0.3773 0.4142 0.3949 0.9263
327.3654 7.5758 6500 375.4951 0.3694 0.4276 0.3964 0.9227
322.1269 8.1585 7000 372.3464 0.3650 0.4338 0.3965 0.9209
318.558 8.7413 7500 366.4694 0.3970 0.4191 0.4078 0.9295
315.4182 9.3240 8000 365.6752 0.3861 0.4409 0.4117 0.9260
314.0958 9.9068 8500 364.7336 0.3918 0.4292 0.4096 0.9267

Framework versions

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