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scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only44

Text Classification
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PyTorch
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
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metadata
base_model: microsoft/mdeberta-v3-base
library_name: transformers
license: mit
metrics:
  - accuracy
  - f1
tags:
  - generated_from_trainer
model-index:
  - name: scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only44
    results: []

scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only44

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 6.2603
  • Accuracy: 0.3657
  • F1: 0.3633

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: 5e-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 Accuracy F1
No log 1.0870 250 1.3365 0.3519 0.3479
0.9017 2.1739 500 1.9777 0.3565 0.3508
0.9017 3.2609 750 2.9438 0.3542 0.3298
0.3023 4.3478 1000 3.1702 0.3611 0.3489
0.3023 5.4348 1250 3.4689 0.3534 0.3522
0.1011 6.5217 1500 4.0537 0.3627 0.3608
0.1011 7.6087 1750 4.5352 0.3573 0.3504
0.0549 8.6957 2000 4.5030 0.3495 0.3449
0.0549 9.7826 2250 4.6084 0.3519 0.3479
0.0339 10.8696 2500 4.7223 0.3565 0.3505
0.0339 11.9565 2750 4.9936 0.3565 0.3518
0.0232 13.0435 3000 4.5828 0.3449 0.3352
0.0232 14.1304 3250 5.0265 0.3565 0.3543
0.0224 15.2174 3500 5.2273 0.3627 0.3580
0.0224 16.3043 3750 5.2708 0.3611 0.3516
0.0156 17.3913 4000 5.6845 0.3511 0.3469
0.0156 18.4783 4250 5.5643 0.3603 0.3537
0.0081 19.5652 4500 5.9288 0.3519 0.3372
0.0081 20.6522 4750 5.9406 0.3611 0.3564
0.0034 21.7391 5000 5.9909 0.3534 0.3519
0.0034 22.8261 5250 6.1283 0.3611 0.3562
0.0017 23.9130 5500 6.1721 0.3688 0.3668
0.0017 25.0 5750 6.2167 0.3596 0.3581
0.0019 26.0870 6000 6.2126 0.3627 0.3596
0.0019 27.1739 6250 6.2446 0.3634 0.3616
0.0014 28.2609 6500 6.2484 0.3650 0.3624
0.0014 29.3478 6750 6.2603 0.3657 0.3633

Framework versions

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