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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-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
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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
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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: 0.4700
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- Precision: 0.9465
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- Recall: 0.9597
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- F1: 0.9531
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- Accuracy: 0.9525
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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 | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.1882 | 4.0 | 12264 | 0.2794 | 0.9282 | 0.9477 | 0.9379 | 0.9443 |
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| 0.1232 | 5.0 | 15330 | 0.2867 | 0.9391 | 0.9534 | 0.9462 | 0.9504 |
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| 0.0967 | 6.0 | 18396 | 0.3523 | 0.9400 | 0.9543 | 0.9471 | 0.9508 |
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| 0.0529 | 7.0 | 21462 | 0.3790 | 0.9397 | 0.9585 | 0.9490 | 0.9516 |
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| 0.0372 | 8.0 | 24528 | 0.4232 | 0.9454 | 0.9556 | 0.9505 | 0.9518 |
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| 0.0238 | 9.0 | 27594 | 0.4425 | 0.9472 | 0.9616 | 0.9544 | 0.9544 |
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| 0.0126 | 10.0 | 30660 | 0.4700 | 0.9465 | 0.9597 | 0.9531 | 0.9525 |
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### Framework versions
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- Transformers 4.56.2
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- Pytorch 2.7.1+cu118
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- Datasets 3.6.0
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- Tokenizers 0.22.0
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