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")# pip install -U transformers accelerate # 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
Download flax_model.msgpack from ibraheemmoosa/xlmindic-base-multiscript-soham: direct link, hf CLI and curl.
- Browser
- Download file 57 MB
-
https://huggingface.co/ibraheemmoosa/xlmindic-base-multiscript-soham/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ibraheemmoosa/xlmindic-base-multiscript-soham/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ibraheemmoosa/xlmindic-base-multiscript-soham/resolve/main/flax_model.msgpack
57 MB
- Xet hash:
- a7af39bc64b3513d772939dae064d8d767440c66959dc5f16785e0266e6e2437
- Size of remote file:
- 57 MB
- SHA256:
- 4f6b032ad9edccc67892c3bb4d0e2d98b196e0ff56c6f21ebdfc3ad6282b8338
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