Instructions to use l3cube-pune/hindi-marathi-dev-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hindi-marathi-dev-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hindi-marathi-dev-bert-scratch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-marathi-dev-bert-scratch") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hindi-marathi-dev-bert-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
6fc3b8b
-
Update tokenizer_config.json
d3fb440
-
Add multilingual to the language tag (#1)
- Add multilingual to the language tag (f3fa3cb4d5276bdf83c8dc780ab94412fa2f523d) Co-authored-by: Loïck BOURDOIS <lbourdois@users.noreply.huggingface.co>111a2cf
-
Update README.md
e65bba2 -
Update README.md
fbe1c0c
-
Update model files
c68aaab
-
Update README.md
0dd089f -
Update README.md
dee7951 -
Create README.md
d274260
-
Update model files
79d7b2a -
initial commit
ed78c99