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+ ---
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+ language:
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+ - mn
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+ license: mit
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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: canine-s-mongolian-ner
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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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+ # canine-s-mongolian-ner
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+
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+ This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3588
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+ - Precision: 0.5262
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+ - Recall: 0.5383
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+ - F1: 0.5322
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+ - Accuracy: 0.9073
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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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+ | 0.6287 | 1.0 | 477 | 0.5233 | 0.2908 | 0.1814 | 0.2234 | 0.8471 |
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+ | 0.4898 | 2.0 | 954 | 0.4404 | 0.3727 | 0.2993 | 0.3320 | 0.8677 |
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+ | 0.403 | 3.0 | 1431 | 0.3939 | 0.4498 | 0.3922 | 0.4191 | 0.8832 |
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+ | 0.3416 | 4.0 | 1908 | 0.3720 | 0.4734 | 0.4606 | 0.4669 | 0.8934 |
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+ | 0.2986 | 5.0 | 2385 | 0.3640 | 0.5093 | 0.4745 | 0.4913 | 0.8987 |
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+ | 0.2693 | 6.0 | 2862 | 0.3599 | 0.5126 | 0.5039 | 0.5082 | 0.9012 |
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+ | 0.243 | 7.0 | 3339 | 0.3534 | 0.5009 | 0.5241 | 0.5123 | 0.9026 |
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+ | 0.2262 | 8.0 | 3816 | 0.3582 | 0.5103 | 0.5315 | 0.5207 | 0.9048 |
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+ | 0.213 | 9.0 | 4293 | 0.3534 | 0.5195 | 0.5327 | 0.5260 | 0.9059 |
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+ | 0.2064 | 10.0 | 4770 | 0.3588 | 0.5262 | 0.5383 | 0.5322 | 0.9073 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3