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update model card README.md
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README.md
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---
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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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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: ukraine-war-pov
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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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# ukraine-war-pov
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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: 0.2166
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- Accuracy: 0.9315
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- F1: 0.9315
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- Precision: 0.9315
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- Recall: 0.9315
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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: 5e-05
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- train_batch_size: 16
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- eval_batch_size: 64
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- seed: 123
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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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- lr_scheduler_warmup_steps: 500
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.284 | 1.0 | 1875 | 0.1850 | 0.9295 | 0.9295 | 0.9303 | 0.9295 |
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| 0.2271 | 2.0 | 3750 | 0.1551 | 0.9405 | 0.9405 | 0.9414 | 0.9405 |
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| 0.2064 | 3.0 | 5625 | 0.1734 | 0.9305 | 0.9305 | 0.9311 | 0.9305 |
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| 0.1842 | 4.0 | 7500 | 0.1694 | 0.9315 | 0.9315 | 0.9317 | 0.9315 |
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| 0.1628 | 5.0 | 9375 | 0.1838 | 0.9435 | 0.9435 | 0.9438 | 0.9435 |
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| 0.1309 | 6.0 | 11250 | 0.2074 | 0.9395 | 0.9395 | 0.9395 | 0.9395 |
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| 0.1017 | 7.0 | 13125 | 0.2659 | 0.9365 | 0.9365 | 0.9365 | 0.9365 |
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| 0.0778 | 8.0 | 15000 | 0.2851 | 0.94 | 0.9400 | 0.9400 | 0.94 |
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| 0.0664 | 9.0 | 16875 | 0.3238 | 0.9385 | 0.9385 | 0.9387 | 0.9385 |
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| 0.066 | 10.0 | 18750 | 0.3092 | 0.939 | 0.9390 | 0.9390 | 0.9390 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 2.0.0+cu118
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- Tokenizers 0.13.3
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