Edit model card

roberta-base-ner-demo-turshilt2

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

  • Loss: 0.1242
  • Precision: 0.9296
  • Recall: 0.9365
  • F1: 0.9330
  • Accuracy: 0.9802

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: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.6386 0.9958 119 0.1340 0.7472 0.8012 0.7732 0.9536
0.1096 2.0 239 0.0939 0.8249 0.8791 0.8511 0.9686
0.0647 2.9958 358 0.0893 0.8356 0.8889 0.8614 0.9715
0.0455 4.0 478 0.0963 0.8452 0.8912 0.8676 0.9712
0.0306 4.9958 597 0.0909 0.9234 0.9314 0.9274 0.9795
0.0146 6.0 717 0.1046 0.9235 0.9302 0.9268 0.9789
0.0108 6.9958 836 0.1040 0.9204 0.9311 0.9257 0.9794
0.0079 8.0 956 0.1168 0.9245 0.9309 0.9277 0.9792
0.0063 8.9958 1075 0.1138 0.9258 0.9337 0.9297 0.9800
0.0051 10.0 1195 0.1165 0.9268 0.9330 0.9299 0.9800
0.0047 10.9958 1314 0.1199 0.9261 0.9359 0.9309 0.9803
0.0034 12.0 1434 0.1238 0.9284 0.9358 0.9321 0.9800
0.0027 12.9958 1553 0.1242 0.9267 0.9355 0.9311 0.9800
0.0026 14.0 1673 0.1232 0.9294 0.9365 0.9329 0.9804
0.0023 14.9372 1785 0.1242 0.9296 0.9365 0.9330 0.9802

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
Downloads last month
2
Safetensors
Model size
124M params
Tensor type
F32
·

Finetuned from