Ilya-Nazimov's picture
End of training
3fb3869 verified
metadata
license: mit
base_model: cointegrated/rubert-tiny2
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: rubert-tiny2-odonata-f3-ner
    results: []

rubert-tiny2-odonata-f3-ner

This model is a fine-tuned version of cointegrated/rubert-tiny2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0188
  • Precision: 0.6653
  • Recall: 0.6157
  • F1: 0.6395
  • Accuracy: 0.9944

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: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 32 0.1309 0.0 0.0 0.0 0.9903
No log 2.0 64 0.0672 0.0 0.0 0.0 0.9903
No log 3.0 96 0.0623 0.0 0.0 0.0 0.9903
No log 4.0 128 0.0576 0.0 0.0 0.0 0.9903
No log 5.0 160 0.0488 0.0 0.0 0.0 0.9903
No log 6.0 192 0.0353 0.0 0.0 0.0 0.9903
No log 7.0 224 0.0288 0.7921 0.5529 0.6513 0.9935
No log 8.0 256 0.0256 0.7987 0.4824 0.6015 0.9931
No log 9.0 288 0.0235 0.7975 0.5098 0.6220 0.9933
No log 10.0 320 0.0221 0.7310 0.5647 0.6372 0.9938
No log 11.0 352 0.0212 0.6912 0.5529 0.6144 0.9938
No log 12.0 384 0.0205 0.6746 0.5529 0.6078 0.9937
No log 13.0 416 0.0201 0.6774 0.5765 0.6229 0.9938
No log 14.0 448 0.0196 0.6712 0.5843 0.6247 0.9940
No log 15.0 480 0.0194 0.6581 0.6039 0.6299 0.9941
0.0722 16.0 512 0.0192 0.6681 0.6 0.6322 0.9942
0.0722 17.0 544 0.0190 0.6624 0.6078 0.6339 0.9943
0.0722 18.0 576 0.0189 0.6542 0.6157 0.6343 0.9943
0.0722 19.0 608 0.0188 0.6624 0.6157 0.6382 0.9944
0.0722 20.0 640 0.0188 0.6653 0.6157 0.6395 0.9944

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cpu
  • Datasets 2.19.2
  • Tokenizers 0.19.1