Text Classification
Transformers
PyTorch
TensorFlow
JAX
albert
multilingual
fill-mask
xlmindic
nlp
indoaryan
indicnlp
iso15919
Instructions to use ibraheemmoosa/xlmindic-base-multiscript-soham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/xlmindic-base-multiscript-soham with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ibraheemmoosa/xlmindic-base-multiscript-soham")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ibraheemmoosa/xlmindic-base-multiscript-soham") model = AutoModelForSequenceClassification.from_pretrained("ibraheemmoosa/xlmindic-base-multiscript-soham", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 768b0102e92b9348b018d06c2e7d35a287a071f5dc3a4666fc4ad101b1b76aad
- Size of remote file:
- 57 MB
- SHA256:
- 0f7b03c60678b0f3a58c5bebd28eff6fad66428fa2fa1898ef59a41318eecf18
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