umn-cyber/indobert-hoax-detection
Browse files- README.md +71 -0
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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license: mit
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base_model: indobenchmark/indobert-base-p1
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: indobert-hoax-detection
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results: []
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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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# indobert-hoax-detection
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This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0862
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- Accuracy: 0.9854
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- F1: 0.9847
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- Precision: 0.9851
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- Recall: 0.9844
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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: 2e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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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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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.0898 | 1.0 | 739 | 0.0585 | 0.9875 | 0.9869 | 0.9858 | 0.9879 |
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| 0.0464 | 2.0 | 1478 | 0.0493 | 0.9861 | 0.9854 | 0.9858 | 0.9851 |
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| 0.0247 | 3.0 | 2217 | 0.0629 | 0.9868 | 0.9862 | 0.9830 | 0.9893 |
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| 0.0097 | 4.0 | 2956 | 0.0773 | 0.9871 | 0.9865 | 0.9858 | 0.9872 |
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| 0.0031 | 5.0 | 3695 | 0.0862 | 0.9854 | 0.9847 | 0.9851 | 0.9844 |
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### Framework versions
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- Transformers 4.45.2
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- Pytorch 2.4.1
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- Datasets 2.19.2
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- Tokenizers 0.20.1
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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-
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size 497804300
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version https://git-lfs.github.com/spec/v1
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size 497804300
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