Instructions to use deandrasetya/indobertweet-multilabel-abusive-language-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deandrasetya/indobertweet-multilabel-abusive-language-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deandrasetya/indobertweet-multilabel-abusive-language-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deandrasetya/indobertweet-multilabel-abusive-language-classifier") model = AutoModelForSequenceClassification.from_pretrained("deandrasetya/indobertweet-multilabel-abusive-language-classifier") - Notebooks
- Google Colab
- Kaggle
indobertweet-multilabel-abusive-language-classifier
This model is a fine-tuned version of indolem/indobertweet-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
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