Instructions to use wonik-hi/hate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wonik-hi/hate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wonik-hi/hate")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wonik-hi/hate") model = AutoModelForSequenceClassification.from_pretrained("wonik-hi/hate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from wonik-hi/hate: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
-
https://huggingface.co/wonik-hi/hate/resolve/main/README.md
- Command line
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hf download hf://wonik-hi/hate/README.md
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curl -L -o README.md https://huggingface.co/wonik-hi/hate/resolve/main/README.md
1.44 kB
| library_name: transformers | |
| license: mit | |
| base_model: beomi/KcELECTRA-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: hate | |
| results: [] | |
| <!-- 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. --> | |
| # hate | |
| This model is a fine-tuned version of [beomi/KcELECTRA-base](https://huggingface.co/beomi/KcELECTRA-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4020 | |
| - Accuracy: 0.8309 | |
| ## 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: 8 | |
| - eval_batch_size: 8 | |
| - 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: linear | |
| - num_epochs: 0.2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| | |
| | 0.4859 | 0.2002 | 734 | 0.4020 | 0.8309 | | |
| ### Framework versions | |
| - Transformers 4.48.3 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.3.0 | |
| - Tokenizers 0.21.0 | |