dcarpintero / pangolin-guard-base

huggingface.co
Total runs: 267
24-hour runs: 0
7-day runs: -28
30-day runs: -4
Model's Last Updated: April 08 2025
text-classification

Introduction of pangolin-guard-base

Model Details of pangolin-guard-base

pangolin-guard-base

This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0194
  • F1: 0.9906
  • Accuracy: 0.9941
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-05
  • train_batch_size: 64
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 2
Training results
Training Loss Epoch Step Validation Loss F1 Accuracy
0.1811 0.1042 100 0.0917 0.9524 0.9690
0.0781 0.2083 200 0.0568 0.9682 0.9802
0.0488 0.3125 300 0.0415 0.9786 0.9866
0.052 0.4167 400 0.0404 0.9801 0.9876
0.0506 0.5208 500 0.0331 0.9831 0.9892
0.0352 0.625 600 0.0355 0.9833 0.9894
0.0374 0.7292 700 0.0263 0.9867 0.9916
0.0281 0.8333 800 0.0284 0.9877 0.9922
0.0241 0.9375 900 0.0428 0.9834 0.9894
0.025 1.0417 1000 0.0266 0.9883 0.9926
0.0096 1.1458 1100 0.0300 0.9873 0.9920
0.0125 1.25 1200 0.0237 0.9892 0.9932
0.0075 1.3542 1300 0.0251 0.9894 0.9933
0.0118 1.4583 1400 0.0249 0.9883 0.9926
0.014 1.5625 1500 0.0199 0.9901 0.9937
0.0067 1.6667 1600 0.0215 0.9902 0.9938
0.0088 1.7708 1700 0.0201 0.9909 0.9943
0.0079 1.875 1800 0.0199 0.9906 0.9941
0.0118 1.9792 1900 0.0194 0.9906 0.9941
Framework versions
  • Transformers 4.49.0
  • Pytorch 2.6.0+cu124
  • Datasets 3.4.1
  • Tokenizers 0.21.1

Runs of dcarpintero pangolin-guard-base on huggingface.co

267
Total runs
0
24-hour runs
1
3-day runs
-28
7-day runs
-4
30-day runs

More Information About pangolin-guard-base huggingface.co Model

More pangolin-guard-base license Visit here:

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

pangolin-guard-base huggingface.co

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

pangolin-guard-base huggingface.co Url

https://huggingface.co/dcarpintero/pangolin-guard-base

dcarpintero pangolin-guard-base online free

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

dcarpintero pangolin-guard-base online free url in huggingface.co:

https://huggingface.co/dcarpintero/pangolin-guard-base

pangolin-guard-base install

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

pangolin-guard-base install url in huggingface.co:

https://huggingface.co/dcarpintero/pangolin-guard-base

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pangolin-guard-base huggingface.co Url

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