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---
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library_name: transformers
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license: mit
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base_model: FacebookAI/xlm-roberta-large
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tags:
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- generated_from_trainer
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datasets:
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- conll2002
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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-large-finetuned-ner
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: conll2002
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type: conll2002
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config: es
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split: validation
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args: es
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metrics:
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- name: Precision
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type: precision
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value: 0.86443345323741
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- name: Recall
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type: recall
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value: 0.8835018382352942
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- name: F1
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type: f1
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value: 0.8738636363636364
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- name: Accuracy
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type: accuracy
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value: 0.9787686065955755
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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-large-finetuned-ner
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the conll2002 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0973
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- Precision: 0.8644
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- Recall: 0.8835
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- F1: 0.8739
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- Accuracy: 0.9788
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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 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: 3
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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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| 0.1382 | 1.0 | 521 | 0.0906 | 0.8502 | 0.8830 | 0.8663 | 0.9782 |
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| 0.048 | 2.0 | 1042 | 0.0861 | 0.8472 | 0.8729 | 0.8599 | 0.9780 |
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| 0.0294 | 3.0 | 1563 | 0.0973 | 0.8644 | 0.8835 | 0.8739 | 0.9788 |
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### Framework versions
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- Transformers 4.46.3
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- Pytorch 2.5.1
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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