Fill-Mask
Transformers
PyTorch
TensorFlow
JAX
albert
pretraining
multilingual
masked-language-modeling
sentence-order-prediction
xlmindic
nlp
indoaryan
indicnlp
iso15919
transliteration
Instructions to use ibraheemmoosa/xlmindic-base-uniscript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/xlmindic-base-uniscript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ibraheemmoosa/xlmindic-base-uniscript", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("ibraheemmoosa/xlmindic-base-uniscript") model = AutoModelForPreTraining.from_pretrained("ibraheemmoosa/xlmindic-base-uniscript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7a5ff261fcfa3fbdc8d65df9119b685eff5642cb1576526ad90bd681ea88544
- Size of remote file:
- 57.6 MB
- SHA256:
- 2b9a7d58a444b5d8b256a2dc9923e9d2ce3918adf389a6b8ee8ba7669bfd2030
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