Instructions to use ibraheemmoosa/xlmindic-rembert-multiscript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/xlmindic-rembert-multiscript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ibraheemmoosa/xlmindic-rembert-multiscript")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ibraheemmoosa/xlmindic-rembert-multiscript") model = AutoModelForMaskedLM.from_pretrained("ibraheemmoosa/xlmindic-rembert-multiscript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2242de473064229cfc8bf705bfd092f9cb051ef535a334eed9a58dbe156ac7a6
- Size of remote file:
- 578 MB
- SHA256:
- b53f67acc739bede67dc0fdbc3d5a9e4b4cc838df95c671e7f3f33dbf8b1de0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.