Hugging Face's logo

DimasikKurd
/
MedRuRobertaLarge_neg

Token Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
MedRuRobertaLarge_neg / README.md
DimasikKurd's picture
DimasikKurd
Training complete
33813e1 verified
preview code
|
raw
history blame contribute delete
No virus
4.71 kB
metadata
license: apache-2.0
base_model: DmitryPogrebnoy/MedRuRobertaLarge
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: MedRuRobertaLarge_neg
    results: []

MedRuRobertaLarge_neg

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

  • Loss: 0.6996
  • Precision: 0.5225
  • Recall: 0.5788
  • F1: 0.5492
  • Accuracy: 0.8955

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 50 0.6906 0.0175 0.0019 0.0035 0.7724
No log 2.0 100 0.7240 0.0526 0.0019 0.0037 0.7756
No log 3.0 150 0.5668 0.0407 0.0289 0.0338 0.7668
No log 4.0 200 0.4358 0.1326 0.1522 0.1417 0.8236
No log 5.0 250 0.3509 0.1932 0.2177 0.2047 0.8573
No log 6.0 300 0.2961 0.3339 0.3699 0.3510 0.8862
No log 7.0 350 0.3715 0.4073 0.3642 0.3845 0.8820
No log 8.0 400 0.2778 0.4511 0.4528 0.4519 0.9040
No log 9.0 450 0.3318 0.4576 0.4778 0.4675 0.8997
0.4025 10.0 500 0.3198 0.5278 0.5299 0.5288 0.9049
0.4025 11.0 550 0.3157 0.4297 0.6358 0.5128 0.8909
0.4025 12.0 600 0.3024 0.5548 0.5954 0.5743 0.9188
0.4025 13.0 650 0.3670 0.6091 0.6185 0.6138 0.9149
0.4025 14.0 700 0.4036 0.5088 0.6127 0.5559 0.8998
0.4025 15.0 750 0.4116 0.5542 0.6012 0.5767 0.9085
0.4025 16.0 800 0.3971 0.5301 0.6455 0.5821 0.9095
0.4025 17.0 850 0.4887 0.5535 0.5183 0.5353 0.8977
0.4025 18.0 900 0.4385 0.5563 0.6474 0.5984 0.9106
0.4025 19.0 950 0.4007 0.6316 0.6012 0.6160 0.9219
0.0841 20.0 1000 0.3720 0.5709 0.5896 0.5801 0.9165
0.0841 21.0 1050 0.5100 0.6393 0.6012 0.6197 0.9150
0.0841 22.0 1100 0.5028 0.5319 0.6590 0.5886 0.8972
0.0841 23.0 1150 0.4347 0.5656 0.5896 0.5774 0.9149
0.0841 24.0 1200 0.4721 0.5861 0.6031 0.5945 0.9122
0.0841 25.0 1250 0.5677 0.6457 0.5549 0.5969 0.9116
0.0841 26.0 1300 0.4095 0.6278 0.6435 0.6356 0.9189
0.0841 27.0 1350 0.4633 0.5088 0.6686 0.5779 0.8989
0.0841 28.0 1400 0.3649 0.5617 0.6493 0.6023 0.9105
0.0841 29.0 1450 0.4653 0.5633 0.6262 0.5931 0.9111
0.0464 30.0 1500 0.5159 0.5581 0.6474 0.5995 0.9119
0.0464 31.0 1550 0.4562 0.5248 0.6513 0.5813 0.9090
0.0464 32.0 1600 0.4424 0.5665 0.5742 0.5703 0.9173
0.0464 33.0 1650 0.4866 0.5617 0.5703 0.5660 0.9164
0.0464 34.0 1700 0.4313 0.3760 0.4586 0.4132 0.8986
0.0464 35.0 1750 0.3786 0.5218 0.5761 0.5476 0.9093

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1