Instructions to use l3cube-pune/hing-mbert-mixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hing-mbert-mixed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hing-mbert-mixed")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hing-mbert-mixed") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hing-mbert-mixed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d9c8ceb9d7fb773ada055c963baac91a92fbf890db3517dfa98ce2a2c0ac863
- Size of remote file:
- 712 MB
- SHA256:
- 02aa98ea10b87d4384561813032b1d986dc9cdef14ab76782e287593fd1c2ba6
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