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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-large
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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: x5-ner-with-augmentation-in-flight
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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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# x5-ner-with-augmentation-in-flight
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3224
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- Precision: 0.9316
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- Recall: 0.9553
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- F1: 0.9433
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- Accuracy: 0.9453
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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: 8
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- eval_batch_size: 8
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
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|:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
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| 0.3995 | 1.0 | 3631 | 0.9135 | 0.8831 | 0.3011 | 0.8761 | 0.8903 |
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| 0.3472 | 2.0 | 7262 | 0.9312 | 0.9173 | 0.2802 | 0.9080 | 0.9267 |
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| 0.318 | 3.0 | 10893 | 0.9385 | 0.9325 | 0.2597 | 0.9174 | 0.9480 |
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| 0.2956 | 4.0 | 14524 | 0.9401 | 0.9387 | 0.2870 | 0.9262 | 0.9515 |
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| 0.2837 | 5.0 | 18155 | 0.9401 | 0.9374 | 0.2796 | 0.9271 | 0.9480 |
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| 0.2601 | 6.0 | 21786 | 0.9423 | 0.9393 | 0.2723 | 0.9266 | 0.9524 |
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| 0.2482 | 7.0 | 25417 | 0.9458 | 0.9464 | 0.2683 | 0.9361 | 0.9569 |
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| 0.2364 | 8.0 | 29048 | 0.3210 | 0.9310 | 0.9550 | 0.9429 | 0.9432 |
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| 0.2077 | 9.0 | 32679 | 0.3160 | 0.9349 | 0.9572 | 0.9459 | 0.9455 |
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| 0.1616 | 10.0 | 36310 | 0.3224 | 0.9316 | 0.9553 | 0.9433 | 0.9453 |
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### Framework versions
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- Transformers 4.53.3
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- Pytorch 2.7.1+cu118
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- Datasets 3.6.0
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- Tokenizers 0.21.4
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