metadata
language:
- mn
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-ner-demo
results: []
xlm-roberta-large-ner-demo
This model is a fine-tuned version of xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0976
- Precision: 0.9340
- Recall: 0.9404
- F1: 0.9372
- Accuracy: 0.9816
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1657 | 1.0 | 477 | 0.0866 | 0.8655 | 0.8978 | 0.8814 | 0.9752 |
| 0.0716 | 2.0 | 954 | 0.0801 | 0.9135 | 0.9283 | 0.9208 | 0.9796 |
| 0.0448 | 3.0 | 1431 | 0.0814 | 0.9244 | 0.9374 | 0.9309 | 0.9805 |
| 0.0283 | 4.0 | 1908 | 0.0870 | 0.9256 | 0.9367 | 0.9311 | 0.9808 |
| 0.017 | 5.0 | 2385 | 0.0976 | 0.9340 | 0.9404 | 0.9372 | 0.9816 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1