Instructions to use ishathombre/monolingual-hindi-from-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ishathombre/monolingual-hindi-from-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ishathombre/monolingual-hindi-from-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ishathombre/monolingual-hindi-from-scratch") model = AutoModelForMaskedLM.from_pretrained("ishathombre/monolingual-hindi-from-scratch") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f76b55b1f2c722fc1c3d6051fd689d3b2ef2fd20a025eb7917e3109c23e34f36
- Size of remote file:
- 5.37 kB
- SHA256:
- c790402a35122c7a82291a2e6247264b479fe9628e489d72bdb3b32f66aff45d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.