---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Uzbek NER model

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the Uzbek Ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1421
- Precision: 0.6071
- Recall: 0.6482
- F1: 0.6270
- Accuracy: 0.9486

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- 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: cosine
- 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1671        | 0.5758 | 150  | 0.1632          | 0.5260    | 0.6425 | 0.5785 | 0.9402   |
| 0.1453        | 1.1497 | 300  | 0.1481          | 0.5935    | 0.6191 | 0.6061 | 0.9467   |
| 0.134         | 1.7255 | 450  | 0.1449          | 0.5936    | 0.6216 | 0.6073 | 0.9480   |
| 0.1273        | 2.2994 | 600  | 0.1413          | 0.6217    | 0.6262 | 0.6239 | 0.9493   |
| 0.1258        | 2.8752 | 750  | 0.1421          | 0.6071    | 0.6482 | 0.6270 | 0.9486   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.1.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0