Hugging Face's logo

haryoaw
/
scenario-KD-SCR-PO-CDF-EN-FROM-EN-D2_data-en-cardiff_eng_only66

Transformers
PyTorch
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
Model card Files Community
scenario-KD-SCR-PO-CDF-EN-FROM-EN-D2_data-en-cardiff_eng_only66 / README.md
haryoaw's picture
haryoaw
Upload tokenizer
3168896 verified
preview code
|
raw
history blame contribute delete
2.85 kB
metadata
base_model: haryoaw/scenario-MDBT-TCR_data-en-cardiff_eng_only
library_name: transformers
license: mit
metrics:
  - accuracy
  - f1
tags:
  - generated_from_trainer
model-index:
  - name: scenario-KD-SCR-PO-CDF-EN-FROM-EN-D2_data-en-cardiff_eng_only66
    results: []

scenario-KD-SCR-PO-CDF-EN-FROM-EN-D2_data-en-cardiff_eng_only66

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

  • Loss: 533.0406
  • Accuracy: 0.3444
  • F1: 0.2711

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: 8
  • eval_batch_size: 32
  • seed: 66
  • 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: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 1.7391 100 631.4247 0.3302 0.2694
No log 3.4783 200 605.5723 0.3413 0.2651
No log 5.2174 300 589.1194 0.3369 0.2521
No log 6.9565 400 580.2668 0.3501 0.2633
574.1509 8.6957 500 571.8079 0.3254 0.2189
574.1509 10.4348 600 564.8586 0.3360 0.2072
574.1509 12.1739 700 559.2232 0.3417 0.2567
574.1509 13.9130 800 552.7770 0.3391 0.2289
574.1509 15.6522 900 549.6864 0.3333 0.2295
468.9151 17.3913 1000 545.1831 0.3338 0.2429
468.9151 19.1304 1100 541.3506 0.3466 0.2794
468.9151 20.8696 1200 538.9130 0.3355 0.2612
468.9151 22.6087 1300 537.2876 0.3470 0.2807
468.9151 24.3478 1400 535.7286 0.3426 0.2346
433.9469 26.0870 1500 533.7326 0.3492 0.2730
433.9469 27.8261 1600 533.2730 0.3338 0.2553
433.9469 29.5652 1700 533.0406 0.3444 0.2711

Framework versions

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