metadata
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Mongolian-xlm-roberta-base-ner-hrl
results: []
Mongolian-xlm-roberta-base-ner-hrl
This model is a fine-tuned version of Davlan/xlm-roberta-base-ner-hrl on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1224
- Precision: 0.9303
- Recall: 0.9375
- F1: 0.9339
- Accuracy: 0.9794
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 |
|---|---|---|---|---|---|---|---|
| 0.1449 | 1.0 | 477 | 0.0884 | 0.8968 | 0.9156 | 0.9061 | 0.9730 |
| 0.0737 | 2.0 | 954 | 0.0840 | 0.9205 | 0.9283 | 0.9244 | 0.9771 |
| 0.0503 | 3.0 | 1431 | 0.0843 | 0.9229 | 0.9312 | 0.9270 | 0.9788 |
| 0.0367 | 4.0 | 1908 | 0.0959 | 0.9232 | 0.9326 | 0.9279 | 0.9781 |
| 0.0268 | 5.0 | 2385 | 0.0991 | 0.9297 | 0.9357 | 0.9327 | 0.9797 |
| 0.02 | 6.0 | 2862 | 0.1067 | 0.9246 | 0.9316 | 0.9281 | 0.9783 |
| 0.0149 | 7.0 | 3339 | 0.1147 | 0.9265 | 0.9345 | 0.9305 | 0.9786 |
| 0.0115 | 8.0 | 3816 | 0.1193 | 0.9289 | 0.9362 | 0.9325 | 0.9795 |
| 0.0095 | 9.0 | 4293 | 0.1208 | 0.9304 | 0.9369 | 0.9336 | 0.9794 |
| 0.008 | 10.0 | 4770 | 0.1224 | 0.9303 | 0.9375 | 0.9339 | 0.9794 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3