metadata
library_name: transformers
language:
- uz
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- risqaliyevds/uzbek_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Uzbek NER model
results: []
Uzbek NER model
This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the Uzbek Ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.1542
- Precision: 0.5799
- Recall: 0.6318
- F1: 0.6047
- Accuracy: 0.9456
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: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.5172 | 1.0 | 246 | 0.1644 | 0.5574 | 0.5631 | 0.5602 | 0.9434 |
| 0.1532 | 2.0 | 492 | 0.1551 | 0.5790 | 0.6188 | 0.5982 | 0.9453 |
| 0.143 | 2.9913 | 735 | 0.1542 | 0.5799 | 0.6318 | 0.6047 | 0.9456 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0