---
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: 0.1754
- Precision: 0.5848
- Recall: 0.6313
- F1: 0.6071
- Accuracy: 0.9386

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.08
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2474        | 0.4662 | 100  | 0.2283          | 0.4911    | 0.5164 | 0.5035 | 0.9284   |
| 0.2039        | 0.9324 | 200  | 0.1942          | 0.5495    | 0.5836 | 0.5661 | 0.9345   |
| 0.1949        | 1.3963 | 300  | 0.1855          | 0.5591    | 0.6348 | 0.5945 | 0.9359   |
| 0.19          | 1.8625 | 400  | 0.1800          | 0.5604    | 0.6279 | 0.5922 | 0.9361   |
| 0.1769        | 2.3263 | 500  | 0.1761          | 0.5806    | 0.6262 | 0.6025 | 0.9381   |
| 0.1765        | 2.7925 | 600  | 0.1754          | 0.5849    | 0.6311 | 0.6071 | 0.9386   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0






### Usage


- from transformers import AutoTokenizer, AutoModelForTokenClassification
- import torch

- custom_id2label = {
    0: "O", 1: "B-CARDINAL", 2: "I-CARDINAL", 3: "B-DATE", 4: "I-DATE",
    5: "B-EVENT", 6: "I-EVENT", 7: "B-GPE", 8: "I-GPE", 9: "B-LOC", 10: "I-LOC",
    11: "B-MONEY", 12: "I-MONEY", 13: "B-ORDINAL", 14: "B-ORG", 15: "I-ORG",
    16: "B-PERCENT", 17: "I-PERCENT", 18: "B-PERSON", 19: "I-PERSON",
    20: "B-TIME", 21: "I-TIME"
}
- custom_label2id = {v: k for k, v in custom_id2label.items()}

- model_name = "mustafoyev202/roberta-uz"
- tokenizer = AutoTokenizer.from_pretrained(model_name)
- model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=23)

- model.config.id2label = custom_id2label
- model.config.label2id = custom_label2id

- text = "Tesla kompaniyasi AQSHda joylashgan."

- tokens = tokenizer(text.split(), return_tensors="pt", is_split_into_words=True)

- with torch.no_grad():
    logits = model(**tokens).logits

- predicted_token_class_ids = logits.argmax(-1).squeeze().tolist()

- word_ids = tokens.word_ids()
- previous_word_id = None
- word_predictions = {}

- for i, word_id in enumerate(word_ids):
    if word_id is not None:
        label = custom_id2label[predicted_token_class_ids[i]]
        if word_id != previous_word_id:  # New word
            word_predictions[word_id] = label
        previous_word_id = word_id

- words = text.split()  # Splitting for simplicity
- final_predictions = [(word, word_predictions.get(i, "O")) for i, word in enumerate(words)]

- print("Predictions:")
- for word, label in final_predictions:
    print(f"{word}: {label}")

- labels = torch.tensor([predicted_token_class_ids]).unsqueeze(0)  # Adjust dimensions
- loss = model(**tokens, labels=labels).loss
- print("\nLoss:", round(loss.item(), 2))