Hugging Face's logo

haryoaw
/
scenario-KD-PO-CDF-CL-D2_data-cl-cardiff_cl_only_alpha-jason

Transformers
PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
Model card Files Community
scenario-KD-PO-CDF-CL-D2_data-cl-cardiff_cl_only_alpha-jason / README.md
haryoaw's picture
haryoaw
Initial Commit
1abb588 verified
preview code
|
raw
history blame
3.41 kB
metadata
license: mit
base_model: haryoaw/scenario-TCR_data-cl-cardiff_cl_only2
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: scenario-KD-PO-CDF-CL-D2_data-cl-cardiff_cl_only_alpha-jason
    results: []

scenario-KD-PO-CDF-CL-D2_data-cl-cardiff_cl_only_alpha-jason

This model is a fine-tuned version of haryoaw/scenario-TCR_data-cl-cardiff_cl_only2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 16.1676
  • Accuracy: 0.4005
  • F1: 0.3988

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: 2222
  • 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 1.09 250 11.7802 0.3727 0.3689
13.6353 2.17 500 10.9269 0.4120 0.4013
13.6353 3.26 750 10.7530 0.4066 0.4034
11.1498 4.35 1000 11.4698 0.4059 0.3994
11.1498 5.43 1250 11.1047 0.4182 0.4140
9.4007 6.52 1500 11.6114 0.4028 0.3911
9.4007 7.61 1750 12.1035 0.3935 0.3911
8.1024 8.7 2000 13.1654 0.4090 0.4035
8.1024 9.78 2250 12.8799 0.4020 0.4001
7.094 10.87 2500 12.5580 0.4082 0.3972
7.094 11.96 2750 12.7991 0.4228 0.4214
6.1021 13.04 3000 13.4827 0.3920 0.3906
6.1021 14.13 3250 14.8695 0.4159 0.4135
5.1725 15.22 3500 14.0881 0.4090 0.4081
5.1725 16.3 3750 14.4576 0.3866 0.3782
4.616 17.39 4000 14.4197 0.3819 0.3777
4.616 18.48 4250 15.2137 0.3997 0.3986
4.0484 19.57 4500 15.2744 0.3943 0.3939
4.0484 20.65 4750 15.3068 0.3858 0.3840
3.6271 21.74 5000 16.0691 0.4059 0.4030
3.6271 22.83 5250 15.7583 0.4120 0.4120
3.3297 23.91 5500 15.9934 0.4028 0.4002
3.3297 25.0 5750 16.3662 0.4097 0.4092
3.0762 26.09 6000 16.4914 0.4051 0.4040
3.0762 27.17 6250 16.4281 0.4051 0.4043
2.8363 28.26 6500 16.3025 0.4151 0.4149
2.8363 29.35 6750 16.1676 0.4005 0.3988

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.13.3