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--- |
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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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datasets: |
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- conll2003 |
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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-Base-Conll2003-English-NER-Finetune-BinaryClass-WeightedLoss |
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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: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: test |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.952513966480447 |
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- name: Recall |
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type: recall |
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value: 0.9660056657223796 |
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- name: F1 |
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type: f1 |
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value: 0.9592123769338959 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9906536018089803 |
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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-Base-Conll2003-English-NER-Finetune-BinaryClass-WeightedLoss |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1110 |
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- Precision: 0.9525 |
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- Recall: 0.9660 |
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- F1: 0.9592 |
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- Accuracy: 0.9907 |
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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-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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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_ratio: 0.05 |
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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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| 0.3351 | 0.3333 | 1441 | 0.1107 | 0.9131 | 0.9377 | 0.9252 | 0.9814 | |
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| 0.0357 | 0.6667 | 2882 | 0.0501 | 0.9500 | 0.9446 | 0.9473 | 0.9882 | |
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| 0.0239 | 1.0 | 4323 | 0.0662 | 0.9481 | 0.9479 | 0.9480 | 0.9883 | |
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| 0.0178 | 1.3333 | 5764 | 0.0667 | 0.9489 | 0.9603 | 0.9546 | 0.9899 | |
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| 0.0188 | 1.6667 | 7205 | 0.0712 | 0.9488 | 0.9575 | 0.9531 | 0.9895 | |
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| 0.018 | 2.0 | 8646 | 0.0605 | 0.9524 | 0.9559 | 0.9541 | 0.9902 | |
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| 0.0119 | 2.3333 | 10087 | 0.0840 | 0.9487 | 0.9662 | 0.9574 | 0.9901 | |
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| 0.0124 | 2.6667 | 11528 | 0.0758 | 0.9486 | 0.9641 | 0.9563 | 0.9901 | |
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| 0.0112 | 3.0 | 12969 | 0.0664 | 0.9559 | 0.9628 | 0.9593 | 0.9910 | |
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| 0.0082 | 3.3333 | 14410 | 0.0939 | 0.9483 | 0.9603 | 0.9543 | 0.9899 | |
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| 0.0083 | 3.6667 | 15851 | 0.0681 | 0.9555 | 0.9591 | 0.9573 | 0.9907 | |
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| 0.0077 | 4.0 | 17292 | 0.0686 | 0.9555 | 0.9572 | 0.9563 | 0.9902 | |
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| 0.0055 | 4.3333 | 18733 | 0.0852 | 0.9498 | 0.9642 | 0.9569 | 0.9905 | |
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| 0.005 | 4.6667 | 20174 | 0.0795 | 0.9530 | 0.9653 | 0.9591 | 0.9907 | |
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| 0.0049 | 5.0 | 21615 | 0.0871 | 0.9526 | 0.9614 | 0.9570 | 0.9900 | |
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| 0.0042 | 5.3333 | 23056 | 0.1054 | 0.9482 | 0.9658 | 0.9569 | 0.9898 | |
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| 0.0045 | 5.6667 | 24497 | 0.0764 | 0.9559 | 0.9598 | 0.9579 | 0.9905 | |
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| 0.0043 | 6.0 | 25938 | 0.0996 | 0.9510 | 0.9662 | 0.9585 | 0.9905 | |
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| 0.0037 | 6.3333 | 27379 | 0.0909 | 0.9539 | 0.9641 | 0.9590 | 0.9908 | |
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| 0.003 | 6.6667 | 28820 | 0.1010 | 0.9519 | 0.9639 | 0.9579 | 0.9905 | |
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| 0.003 | 7.0 | 30261 | 0.0944 | 0.9510 | 0.9632 | 0.9571 | 0.9905 | |
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| 0.0037 | 7.3333 | 31702 | 0.1041 | 0.9514 | 0.9642 | 0.9578 | 0.9903 | |
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| 0.0021 | 7.6667 | 33143 | 0.1048 | 0.9520 | 0.9658 | 0.9589 | 0.9907 | |
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| 0.0029 | 8.0 | 34584 | 0.1001 | 0.9526 | 0.9651 | 0.9588 | 0.9907 | |
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| 0.0019 | 8.3333 | 36025 | 0.1098 | 0.9525 | 0.9653 | 0.9588 | 0.9906 | |
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| 0.0019 | 8.6667 | 37466 | 0.1027 | 0.9538 | 0.9651 | 0.9594 | 0.9906 | |
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| 0.0019 | 9.0 | 38907 | 0.0990 | 0.9543 | 0.9653 | 0.9598 | 0.9908 | |
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| 0.0018 | 9.3333 | 40348 | 0.1086 | 0.9537 | 0.9655 | 0.9595 | 0.9907 | |
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| 0.0014 | 9.6667 | 41789 | 0.1090 | 0.9533 | 0.9658 | 0.9595 | 0.9907 | |
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| 0.0014 | 10.0 | 43230 | 0.1110 | 0.9525 | 0.9660 | 0.9592 | 0.9907 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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