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End of training

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+ ---
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+ license: mit
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+ base_model: xlm-roberta-base
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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: xlm-roberta-base-kyrgyzNER
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+ results: []
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+ ---
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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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+
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+ # xlm-roberta-base-kyrgyzNER
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+
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3229
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+ - Precision: 0.7042
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+ - Recall: 0.6871
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+ - F1: 0.6956
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+ - Accuracy: 0.9119
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 1
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 800
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 88 | 3.8135 | 0.0001 | 0.0009 | 0.0002 | 0.0002 |
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+ | No log | 2.0 | 176 | 1.3867 | 0.0 | 0.0 | 0.0 | 0.7162 |
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+ | No log | 3.0 | 264 | 0.9276 | 0.1684 | 0.0357 | 0.0590 | 0.7678 |
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+ | No log | 4.0 | 352 | 0.6467 | 0.4470 | 0.2771 | 0.3421 | 0.8420 |
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+ | No log | 5.0 | 440 | 0.5200 | 0.6394 | 0.5282 | 0.5785 | 0.8792 |
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+ | 1.647 | 6.0 | 528 | 0.4383 | 0.6712 | 0.5918 | 0.6290 | 0.8927 |
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+ | 1.647 | 7.0 | 616 | 0.3847 | 0.6724 | 0.6439 | 0.6578 | 0.9028 |
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+ | 1.647 | 8.0 | 704 | 0.3586 | 0.6857 | 0.6575 | 0.6713 | 0.9061 |
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+ | 1.647 | 9.0 | 792 | 0.3422 | 0.6786 | 0.6717 | 0.6751 | 0.9070 |
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+ | 1.647 | 10.0 | 880 | 0.3229 | 0.7042 | 0.6871 | 0.6956 | 0.9119 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.17.0
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+ - Tokenizers 0.15.2
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