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End of training
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README.md
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---
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language:
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- mn
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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-ner-demo
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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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# xlm-roberta-base-ner-demo
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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: 0.1272
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- Precision: 0.9267
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- Recall: 0.9350
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- F1: 0.9309
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- Accuracy: 0.9786
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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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| 0.2011 | 1.0 | 477 | 0.0950 | 0.8951 | 0.9101 | 0.9025 | 0.9717 |
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| 0.0809 | 2.0 | 954 | 0.1010 | 0.8992 | 0.9135 | 0.9063 | 0.9720 |
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| 0.0588 | 3.0 | 1431 | 0.0937 | 0.9143 | 0.9274 | 0.9208 | 0.9765 |
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| 0.0438 | 4.0 | 1908 | 0.0949 | 0.9192 | 0.9291 | 0.9241 | 0.9771 |
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| 0.0316 | 5.0 | 2385 | 0.1000 | 0.9220 | 0.9300 | 0.9260 | 0.9771 |
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| 0.0238 | 6.0 | 2862 | 0.1099 | 0.9266 | 0.9333 | 0.9299 | 0.9783 |
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| 0.0181 | 7.0 | 3339 | 0.1125 | 0.9262 | 0.9344 | 0.9303 | 0.9783 |
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| 0.0135 | 8.0 | 3816 | 0.1201 | 0.9220 | 0.9333 | 0.9276 | 0.9781 |
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| 0.0106 | 9.0 | 4293 | 0.1244 | 0.9263 | 0.9343 | 0.9303 | 0.9784 |
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| 0.0089 | 10.0 | 4770 | 0.1272 | 0.9267 | 0.9350 | 0.9309 | 0.9786 |
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### Framework versions
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- Transformers 4.35.1
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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