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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLM-RoBERTa-Base-Conll2003-English-NER-Finetune-FP16-BinaryClass-WeightedLoss
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9526306589757035
- name: Recall
type: recall
value: 0.964943342776204
- name: F1
type: f1
value: 0.9587474711935965
- name: Accuracy
type: accuracy
value: 0.9901367502961128
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLM-RoBERTa-Base-Conll2003-English-NER-Finetune-FP16-BinaryClass-WeightedLoss
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1188
- Precision: 0.9526
- Recall: 0.9649
- F1: 0.9587
- Accuracy: 0.9901
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2739 | 0.3333 | 1441 | 0.0632 | 0.9412 | 0.9373 | 0.9392 | 0.9863 |
| 0.0329 | 0.6667 | 2882 | 0.0572 | 0.9435 | 0.9347 | 0.9391 | 0.9865 |
| 0.024 | 1.0 | 4323 | 0.0679 | 0.9433 | 0.9536 | 0.9484 | 0.9882 |
| 0.0181 | 1.3333 | 5764 | 0.0652 | 0.9458 | 0.9618 | 0.9537 | 0.9897 |
| 0.0187 | 1.6667 | 7205 | 0.0625 | 0.9531 | 0.9492 | 0.9511 | 0.9895 |
| 0.0176 | 2.0 | 8646 | 0.0685 | 0.9488 | 0.9573 | 0.9530 | 0.9896 |
| 0.0108 | 2.3333 | 10087 | 0.0931 | 0.9470 | 0.9625 | 0.9547 | 0.9897 |
| 0.0117 | 2.6667 | 11528 | 0.0808 | 0.9489 | 0.9632 | 0.9560 | 0.9900 |
| 0.0107 | 3.0 | 12969 | 0.0672 | 0.9531 | 0.9602 | 0.9566 | 0.9908 |
| 0.0076 | 3.3333 | 14410 | 0.0973 | 0.9470 | 0.9587 | 0.9528 | 0.9897 |
| 0.0085 | 3.6667 | 15851 | 0.0741 | 0.9574 | 0.9549 | 0.9561 | 0.9906 |
| 0.0092 | 4.0 | 17292 | 0.0807 | 0.9492 | 0.9621 | 0.9556 | 0.9901 |
| 0.0049 | 4.3333 | 18733 | 0.0886 | 0.9527 | 0.9623 | 0.9575 | 0.9906 |
| 0.0058 | 4.6667 | 20174 | 0.0871 | 0.9516 | 0.9639 | 0.9577 | 0.9904 |
| 0.0047 | 5.0 | 21615 | 0.0928 | 0.9541 | 0.9610 | 0.9576 | 0.9903 |
| 0.0041 | 5.3333 | 23056 | 0.1145 | 0.9491 | 0.9667 | 0.9578 | 0.9899 |
| 0.0048 | 5.6667 | 24497 | 0.0854 | 0.9554 | 0.9623 | 0.9588 | 0.9907 |
| 0.0032 | 6.0 | 25938 | 0.1107 | 0.9488 | 0.9651 | 0.9569 | 0.9899 |
| 0.003 | 6.3333 | 27379 | 0.1038 | 0.9524 | 0.9674 | 0.9599 | 0.9907 |
| 0.0032 | 6.6667 | 28820 | 0.1038 | 0.9533 | 0.9651 | 0.9592 | 0.9904 |
| 0.0034 | 7.0 | 30261 | 0.1038 | 0.9534 | 0.9667 | 0.9600 | 0.9906 |
| 0.0025 | 7.3333 | 31702 | 0.1103 | 0.9528 | 0.9619 | 0.9574 | 0.9899 |
| 0.003 | 7.6667 | 33143 | 0.1177 | 0.9506 | 0.9644 | 0.9575 | 0.9899 |
| 0.0022 | 8.0 | 34584 | 0.1151 | 0.9511 | 0.9633 | 0.9572 | 0.9900 |
| 0.0016 | 8.3333 | 36025 | 0.1141 | 0.9528 | 0.9651 | 0.9589 | 0.9904 |
| 0.0025 | 8.6667 | 37466 | 0.1090 | 0.9550 | 0.9626 | 0.9588 | 0.9905 |
| 0.0024 | 9.0 | 38907 | 0.1115 | 0.9546 | 0.9653 | 0.9599 | 0.9906 |
| 0.002 | 9.3333 | 40348 | 0.1148 | 0.9536 | 0.9639 | 0.9587 | 0.9903 |
| 0.0014 | 9.6667 | 41789 | 0.1201 | 0.9522 | 0.9655 | 0.9588 | 0.9902 |
| 0.0015 | 10.0 | 43230 | 0.1188 | 0.9526 | 0.9649 | 0.9587 | 0.9901 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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