End of training
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README.md
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---
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library_name: transformers
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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: xlmr-multi-ru-uk
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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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# xlmr-multi-ru-uk
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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: 0.1275
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- Precision: 0.9537
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- Recall: 0.9586
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- F1: 0.9561
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- Accuracy: 0.9681
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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: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 2
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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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| 0.3835 | 1.0 | 540 | 0.1460 | 0.9447 | 0.9502 | 0.9474 | 0.9621 |
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| 0.1134 | 2.0 | 1080 | 0.1275 | 0.9537 | 0.9586 | 0.9561 | 0.9681 |
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### Framework versions
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- Transformers 4.48.3
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- Pytorch 2.5.1+cu124
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- Datasets 3.3.1
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- Tokenizers 0.21.0
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runs/Feb17_16-19-40_edc51e893a16/events.out.tfevents.1739809187.edc51e893a16.278.1
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version https://git-lfs.github.com/spec/v1
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size
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version https://git-lfs.github.com/spec/v1
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size 7315
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