xddModel / README.md
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metadata
license: mit
base_model: ukr-models/xlm-roberta-base-uk
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xddModel
    results: []

xddModel

This model is a fine-tuned version of ukr-models/xlm-roberta-base-uk on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1495
  • Precision: 0.8533
  • Recall: 0.8819
  • F1: 0.8674
  • Accuracy: 0.9625

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • 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
No log 1.0 175 0.1730 0.7640 0.8340 0.7975 0.9463
No log 2.0 350 0.1552 0.8131 0.8585 0.8352 0.9527
0.2473 3.0 525 0.1334 0.8433 0.8718 0.8573 0.9611
0.2473 4.0 700 0.1305 0.8429 0.8784 0.8603 0.9615
0.2473 5.0 875 0.1293 0.8541 0.8788 0.8663 0.9626
0.0833 6.0 1050 0.1346 0.8449 0.8828 0.8634 0.9621
0.0833 7.0 1225 0.1386 0.8449 0.8827 0.8634 0.9624
0.0833 8.0 1400 0.1474 0.8548 0.8851 0.8697 0.9632
0.0558 9.0 1575 0.1496 0.8485 0.8830 0.8654 0.9622
0.0558 10.0 1750 0.1495 0.8533 0.8819 0.8674 0.9625

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.0
  • Tokenizers 0.15.2