Text Classification
Transformers
PyTorch
Arabic
bert
text classification
arabic
poetry
text-embeddings-inference
Instructions to use Ammar-alhaj-ali/arabic-MARBERT-poetry-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ammar-alhaj-ali/arabic-MARBERT-poetry-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ammar-alhaj-ali/arabic-MARBERT-poetry-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ammar-alhaj-ali/arabic-MARBERT-poetry-classification") model = AutoModelForSequenceClassification.from_pretrained("Ammar-alhaj-ali/arabic-MARBERT-poetry-classification") - Notebooks
- Google Colab
- Kaggle
Commit ·
8ccb17c
1
Parent(s): e780c4a
Upload tokenizer_config.json
Browse files- tokenizer_config.json +15 -0
tokenizer_config.json
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{
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"name_or_path": "UBC-NLP/MARBERT",
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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