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README.md
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---
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library_name: transformers
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base_model: ai-forever/ruRoberta-large
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tags:
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- generated_from_trainer
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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: x5-ner-ru
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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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# x5-ner-ru
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This model is a fine-tuned version of [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5277
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- Precision: 0.9444
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- Recall: 0.9581
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- F1: 0.9512
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- Accuracy: 0.9510
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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: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Use OptimizerNames.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: linear
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
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|:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
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| 0.4099 | 1.0 | 3066 | 0.9327 | 0.9241 | 0.3133 | 0.9141 | 0.9343 |
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| 0.2574 | 2.0 | 6132 | 0.9380 | 0.9312 | 0.3137 | 0.9162 | 0.9467 |
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| 0.2085 | 3.0 | 9198 | 0.9434 | 0.9388 | 0.2655 | 0.9262 | 0.9518 |
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| 0.1484 | 4.0 | 12264 | 0.9454 | 0.9449 | 0.3075 | 0.9359 | 0.9540 |
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| 0.1094 | 5.0 | 15330 | 0.9491 | 0.9463 | 0.3448 | 0.9422 | 0.9505 |
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| 0.077 | 6.0 | 18396 | 0.9509 | 0.9483 | 0.3936 | 0.9393 | 0.9575 |
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| 0.0559 | 7.0 | 21462 | 0.9504 | 0.9505 | 0.3870 | 0.9421 | 0.9591 |
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| 0.0279 | 8.0 | 24528 | 0.5072 | 0.9426 | 0.9581 | 0.9503 | 0.9485 |
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| 0.0142 | 9.0 | 27594 | 0.4978 | 0.9449 | 0.9572 | 0.9510 | 0.9501 |
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| 0.0155 | 10.0 | 30660 | 0.5277 | 0.9444 | 0.9581 | 0.9512 | 0.9510 |
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### Framework versions
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- Transformers 4.56.2
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- Pytorch 2.7.1+cu118
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- Datasets 3.6.0
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- Tokenizers 0.22.0
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