Text Classification
Transformers
PyTorch
Arabic
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use MMars/marbertv2_flodusta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MMars/marbertv2_flodusta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MMars/marbertv2_flodusta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MMars/marbertv2_flodusta") model = AutoModelForSequenceClassification.from_pretrained("MMars/marbertv2_flodusta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from MMars/marbertv2_flodusta: direct link, hf CLI and curl.
- Browser
- Download file 2.69 MB
-
https://huggingface.co/MMars/marbertv2_flodusta/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://MMars/marbertv2_flodusta@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/MMars/marbertv2_flodusta/resolve/refs%2Fpr%2F1/tokenizer.json
2.69 MB
- Xet hash:
- 40d13137b5d893c584c4f8a96a39fd9ccb40d2f99c97e7f3e4246ca81ed1b4b7
- Size of remote file:
- 2.69 MB
- SHA256:
- 50a18ba4423179a9e5e18e90321f5393abfdf3fb5f53c4ec10e0747bf31fc139
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