Text Classification
Transformers
Safetensors
Moroccan Arabic
Arabic
bert
toxicity-detection
content-moderation
offensive-language
moroccan-darija
darija
low-resource-languages
Eval Results (legacy)
text-embeddings-inference
Instructions to use TypicaAI/DarijaToxicityDetector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TypicaAI/DarijaToxicityDetector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TypicaAI/DarijaToxicityDetector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TypicaAI/DarijaToxicityDetector") model = AutoModelForSequenceClassification.from_pretrained("TypicaAI/DarijaToxicityDetector") - Notebooks
- Google Colab
- Kaggle
File size: 579 Bytes
9b6d56a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": false,
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"is_local": true,
"local_files_only": false,
"mask_token": "[MASK]",
"max_length": 512,
"model_max_length": 1000000000000000019884624838656,
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"stride": 0,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
|