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End of training

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README.md ADDED
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+ ---
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+ language:
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+ - mn
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+ base_model: bayartsogt/mongolian-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: roberta-base-ner-demo
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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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+ # roberta-base-ner-demo
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+
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+ This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1566
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+ - Precision: 0.6857
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+ - Recall: 0.7725
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+ - F1: 0.7265
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+ - Accuracy: 0.9453
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 32
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+ - seed: 42
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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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+ - 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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+ | 0.9745 | 1.0 | 477 | 0.5080 | 0.2164 | 0.1205 | 0.1548 | 0.8187 |
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+ | 0.425 | 2.0 | 954 | 0.3128 | 0.5213 | 0.5929 | 0.5548 | 0.9038 |
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+ | 0.2943 | 3.0 | 1431 | 0.2337 | 0.5905 | 0.6781 | 0.6313 | 0.9237 |
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+ | 0.2393 | 4.0 | 1908 | 0.2000 | 0.6303 | 0.7224 | 0.6732 | 0.9333 |
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+ | 0.2134 | 5.0 | 2385 | 0.1813 | 0.6526 | 0.7434 | 0.6951 | 0.9384 |
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+ | 0.1978 | 6.0 | 2862 | 0.1704 | 0.6629 | 0.7527 | 0.7050 | 0.9412 |
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+ | 0.1885 | 7.0 | 3339 | 0.1647 | 0.6737 | 0.7625 | 0.7154 | 0.9429 |
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+ | 0.1823 | 8.0 | 3816 | 0.1595 | 0.6816 | 0.7680 | 0.7222 | 0.9443 |
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+ | 0.1792 | 9.0 | 4293 | 0.1576 | 0.6843 | 0.7713 | 0.7252 | 0.9451 |
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+ | 0.1778 | 10.0 | 4770 | 0.1566 | 0.6857 | 0.7725 | 0.7265 | 0.9453 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.1
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.0
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+ - Tokenizers 0.19.1
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