Instructions to use hardiksr/function-classifier-bert-base-arabic-camelbert-msa-data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hardiksr/function-classifier-bert-base-arabic-camelbert-msa-data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hardiksr/function-classifier-bert-base-arabic-camelbert-msa-data")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hardiksr/function-classifier-bert-base-arabic-camelbert-msa-data") model = AutoModelForSequenceClassification.from_pretrained("hardiksr/function-classifier-bert-base-arabic-camelbert-msa-data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2706b008dbde35c4b138a80d439ac83fe9cfe2ba63eb481b926a59d37cccc1ed
- Size of remote file:
- 436 MB
- SHA256:
- 94118ff4da3f80bee2c70364e6bf5640d10dcbc79cc77c78b002cadaaf21c351
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