Fill-Mask
Transformers
PyTorch
TensorFlow
JAX
albert
pretraining
multilingual
masked-language-modeling
sentence-order-prediction
xlmindic
nlp
indoaryan
indicnlp
iso15919
Instructions to use ibraheemmoosa/xlmindic-base-multiscript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/xlmindic-base-multiscript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ibraheemmoosa/xlmindic-base-multiscript")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("ibraheemmoosa/xlmindic-base-multiscript") model = AutoModelForPreTraining.from_pretrained("ibraheemmoosa/xlmindic-base-multiscript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ibraheemmoosa/xlmindic-base-multiscript: direct link, hf CLI and curl.
- Browser
- Download file 57.6 MB
-
https://huggingface.co/ibraheemmoosa/xlmindic-base-multiscript/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ibraheemmoosa/xlmindic-base-multiscript/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ibraheemmoosa/xlmindic-base-multiscript/resolve/main/flax_model.msgpack
57.6 MB
- Xet hash:
- 9cef0a385b6e566ccd129fa76d9556123894f61f89335d5db4da52cf403aba96
- Size of remote file:
- 57.6 MB
- SHA256:
- b2eccf3d75be315786c970ea4989f5f88474472a086c334c5ea74a8212567a1f
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