nairaxo / swahili-ner

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Model's Last Updated: January 22 2025
token-classification

Introduction of swahili-ner

Model Details of swahili-ner

swahili-ner

This model is a fine-tuned version of nairaxo/toumbert on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1735
  • Precision: 0.9015
  • Recall: 0.9118
  • F1: 0.9066
  • Accuracy: 0.9805
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: 2e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • num_epochs: 20
Training results
Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.1531 1.0 678 0.0737 0.8544 0.9018 0.8775 0.9768
0.0729 2.0 1356 0.0882 0.8656 0.9161 0.8901 0.9754
0.0392 3.0 2034 0.0864 0.8987 0.8962 0.8974 0.9806
0.022 4.0 2712 0.0978 0.8940 0.9118 0.9028 0.9807
0.0167 5.0 3390 0.1191 0.8933 0.9047 0.8989 0.9775
0.0084 6.0 4068 0.1572 0.9136 0.8876 0.9004 0.9801
0.0059 7.0 4746 0.1391 0.8993 0.9018 0.9006 0.9795
0.004 8.0 5424 0.1290 0.8922 0.9303 0.9109 0.9808
0.0028 9.0 6102 0.1374 0.9048 0.9189 0.9118 0.9806
0.0021 10.0 6780 0.1303 0.9106 0.9275 0.9190 0.9832
0.0026 11.0 7458 0.1569 0.9193 0.9232 0.9212 0.9817
0.0012 12.0 8136 0.1593 0.9049 0.9203 0.9126 0.9806
0.0005 13.0 8814 0.1593 0.9110 0.9175 0.9142 0.9819
0.0008 14.0 9492 0.1665 0.9136 0.9175 0.9155 0.9799
0.0004 15.0 10170 0.1760 0.9038 0.9090 0.9064 0.9799
0.0002 16.0 10848 0.1730 0.9077 0.9090 0.9083 0.9805
0.0003 17.0 11526 0.1733 0.9004 0.9132 0.9068 0.9803
0.0 18.0 12204 0.1748 0.9041 0.9118 0.9079 0.9803
0.0001 19.0 12882 0.1734 0.9015 0.9118 0.9066 0.9805
0.0001 20.0 13560 0.1735 0.9015 0.9118 0.9066 0.9805
Framework versions
  • Transformers 4.47.1
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0

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More Information About swahili-ner huggingface.co Model

More swahili-ner license Visit here:

https://choosealicense.com/licenses/apache-2.0

swahili-ner huggingface.co

swahili-ner huggingface.co is an AI model on huggingface.co that provides swahili-ner's model effect (), which can be used instantly with this nairaxo swahili-ner model. huggingface.co supports a free trial of the swahili-ner model, and also provides paid use of the swahili-ner. Support call swahili-ner model through api, including Node.js, Python, http.

swahili-ner huggingface.co Url

https://huggingface.co/nairaxo/swahili-ner

nairaxo swahili-ner online free

swahili-ner huggingface.co is an online trial and call api platform, which integrates swahili-ner's modeling effects, including api services, and provides a free online trial of swahili-ner, you can try swahili-ner online for free by clicking the link below.

nairaxo swahili-ner online free url in huggingface.co:

https://huggingface.co/nairaxo/swahili-ner

swahili-ner install

swahili-ner is an open source model from GitHub that offers a free installation service, and any user can find swahili-ner on GitHub to install. At the same time, huggingface.co provides the effect of swahili-ner install, users can directly use swahili-ner installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

swahili-ner install url in huggingface.co:

https://huggingface.co/nairaxo/swahili-ner

Url of swahili-ner

swahili-ner huggingface.co Url

Provider of swahili-ner huggingface.co

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