nemuwn's picture
update model card README.md
e3f025b
metadata
language:
  - mn
license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-base-mongolian-ner
    results: []

xlm-roberta-base-mongolian-ner

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

  • Loss: 0.1166
  • Precision: 0.9251
  • Recall: 0.9335
  • F1: 0.9293
  • Accuracy: 0.9787

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: 32
  • 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
0.2013 1.0 477 0.0958 0.8951 0.9124 0.9037 0.9731
0.0846 2.0 954 0.0825 0.9155 0.9240 0.9197 0.9774
0.0622 3.0 1431 0.0844 0.9109 0.9235 0.9172 0.9766
0.0456 4.0 1908 0.0940 0.9174 0.9266 0.9220 0.9767
0.0347 5.0 2385 0.1015 0.9184 0.9284 0.9234 0.9770
0.0253 6.0 2862 0.1117 0.9174 0.9254 0.9214 0.9764
0.0203 7.0 3339 0.1147 0.9225 0.9310 0.9267 0.9780
0.0152 8.0 3816 0.1129 0.9229 0.9316 0.9272 0.9779
0.0129 9.0 4293 0.1150 0.9245 0.9324 0.9285 0.9784
0.0102 10.0 4770 0.1166 0.9251 0.9335 0.9293 0.9787

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3