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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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<!-- 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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# canine-s-mongolian-ner |
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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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## 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: 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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.6287 | 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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### Framework versions |
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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 |
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