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--- |
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library_name: transformers |
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language: |
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- uz |
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license: mit |
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base_model: FacebookAI/xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- risqaliyevds/uzbek_ner |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: Uzbek NER model |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Uzbek NER model |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the Uzbek Ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1421 |
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- Precision: 0.6071 |
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- Recall: 0.6482 |
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- F1: 0.6270 |
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- Accuracy: 0.9486 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1671 | 0.5758 | 150 | 0.1632 | 0.5260 | 0.6425 | 0.5785 | 0.9402 | |
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| 0.1453 | 1.1497 | 300 | 0.1481 | 0.5935 | 0.6191 | 0.6061 | 0.9467 | |
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| 0.134 | 1.7255 | 450 | 0.1449 | 0.5936 | 0.6216 | 0.6073 | 0.9480 | |
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| 0.1273 | 2.2994 | 600 | 0.1413 | 0.6217 | 0.6262 | 0.6239 | 0.9493 | |
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| 0.1258 | 2.8752 | 750 | 0.1421 | 0.6071 | 0.6482 | 0.6270 | 0.9486 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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