Text Classification
Transformers
PyTorch
TensorFlow
JAX
albert
multilingual
xlmindic
nlp
indoaryan
indicnlp
iso15919
transliteration
Instructions to use ibraheemmoosa/xlmindic-base-uniscript-soham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/xlmindic-base-uniscript-soham with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ibraheemmoosa/xlmindic-base-uniscript-soham")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ibraheemmoosa/xlmindic-base-uniscript-soham") model = AutoModelForSequenceClassification.from_pretrained("ibraheemmoosa/xlmindic-base-uniscript-soham", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ibraheemmoosa/xlmindic-base-uniscript-soham: direct link, hf CLI and curl.
- Browser
- Download file 57 MB
-
https://huggingface.co/ibraheemmoosa/xlmindic-base-uniscript-soham/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ibraheemmoosa/xlmindic-base-uniscript-soham/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ibraheemmoosa/xlmindic-base-uniscript-soham/resolve/main/pytorch_model.bin
57 MB
- Xet hash:
- 52faf79963ea5e9bf5f9075f6598ef321bd6c42758a9a1b1155d934510831222
- Size of remote file:
- 57 MB
- SHA256:
- c7b9819708b78ed7c174d1ef0133abc7435f27d5f39f7cda26aac608eecfc942
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