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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: test-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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# test-ner |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2327 |
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- Precision: 0.9133 |
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- Recall: 0.9225 |
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- F1: 0.9179 |
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- Accuracy: 0.9687 |
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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: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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| 0.3488 | 1.0 | 625 | 0.1874 | 0.8414 | 0.8560 | 0.8487 | 0.9486 | |
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| 0.1914 | 2.0 | 1250 | 0.1857 | 0.8674 | 0.8794 | 0.8734 | 0.9552 | |
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| 0.1418 | 3.0 | 1875 | 0.1618 | 0.8752 | 0.8906 | 0.8828 | 0.9596 | |
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| 0.0883 | 4.0 | 2500 | 0.1701 | 0.8952 | 0.9011 | 0.8982 | 0.9631 | |
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| 0.0582 | 5.0 | 3125 | 0.1873 | 0.8774 | 0.9149 | 0.8958 | 0.9620 | |
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| 0.0453 | 6.0 | 3750 | 0.1902 | 0.9008 | 0.9131 | 0.9069 | 0.9641 | |
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| 0.0353 | 7.0 | 4375 | 0.2059 | 0.8992 | 0.9067 | 0.9029 | 0.9654 | |
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| 0.015 | 8.0 | 5000 | 0.2231 | 0.9031 | 0.9183 | 0.9106 | 0.9659 | |
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| 0.0114 | 9.0 | 5625 | 0.2234 | 0.9120 | 0.9198 | 0.9159 | 0.9677 | |
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| 0.0066 | 10.0 | 6250 | 0.2327 | 0.9133 | 0.9225 | 0.9179 | 0.9687 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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