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fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

afaji / fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

Run on huggingface.co
huggingface.co
Total runs: 7
24-hour runs: 0
7-day runs: 1
30-day runs: -16
Model's Last Updated: February 25 2023
text-classification

Introduction of fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

Model Details of fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

This model is a fine-tuned version of indolem/indobert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7758
  • Accuracy: 0.7761
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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.06
  • num_epochs: 16
Training results
Training Loss Epoch Step Validation Loss Accuracy
1.0858 1.0 161 0.9459 0.5230
0.8345 2.0 322 0.7373 0.6978
0.714 3.0 483 0.6561 0.7401
0.6385 4.0 644 0.6023 0.7679
0.541 5.0 805 0.5944 0.7756
0.4812 6.0 966 0.6186 0.7729
0.4285 7.0 1127 0.6241 0.7806
0.3847 8.0 1288 0.6587 0.7820
0.3295 9.0 1449 0.6775 0.7770
0.2998 10.0 1610 0.7086 0.7806
0.2585 11.0 1771 0.7179 0.7779
0.2409 12.0 1932 0.7355 0.7797
0.218 13.0 2093 0.7490 0.7729
0.204 14.0 2254 0.7683 0.7751
0.1981 15.0 2415 0.7748 0.7770
0.1956 16.0 2576 0.7758 0.7761
Framework versions
  • Transformers 4.26.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.2.0
  • Tokenizers 0.13.2

Runs of afaji fine-tuned-IndoNLI-Basic-with-indobert-base-uncased on huggingface.co

7
Total runs
0
24-hour runs
0
3-day runs
1
7-day runs
-16
30-day runs

More Information About fine-tuned-IndoNLI-Basic-with-indobert-base-uncased huggingface.co Model

More fine-tuned-IndoNLI-Basic-with-indobert-base-uncased license Visit here:

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fine-tuned-IndoNLI-Basic-with-indobert-base-uncased huggingface.co

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fine-tuned-IndoNLI-Basic-with-indobert-base-uncased huggingface.co Url

https://huggingface.co/afaji/fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

afaji fine-tuned-IndoNLI-Basic-with-indobert-base-uncased online free

fine-tuned-IndoNLI-Basic-with-indobert-base-uncased huggingface.co is an online trial and call api platform, which integrates fine-tuned-IndoNLI-Basic-with-indobert-base-uncased's modeling effects, including api services, and provides a free online trial of fine-tuned-IndoNLI-Basic-with-indobert-base-uncased, you can try fine-tuned-IndoNLI-Basic-with-indobert-base-uncased online for free by clicking the link below.

afaji fine-tuned-IndoNLI-Basic-with-indobert-base-uncased online free url in huggingface.co:

https://huggingface.co/afaji/fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

fine-tuned-IndoNLI-Basic-with-indobert-base-uncased install

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

fine-tuned-IndoNLI-Basic-with-indobert-base-uncased install url in huggingface.co:

https://huggingface.co/afaji/fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

Url of fine-tuned-IndoNLI-Basic-with-indobert-base-uncased

fine-tuned-IndoNLI-Basic-with-indobert-base-uncased huggingface.co Url

Provider of fine-tuned-IndoNLI-Basic-with-indobert-base-uncased huggingface.co

afaji
ORGANIZATIONS
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