Instructions to use l3cube-pune/marathi-tweets-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-tweets-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/marathi-tweets-bert-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-tweets-bert-scratch") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/marathi-tweets-bert-scratch") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02937b1b63975e6b9854e88c7b52e5caa0f6b6c3185dc901d28b51436409ce28
- Size of remote file:
- 504 MB
- SHA256:
- 55b621d09259551bc9213fff9e0bf6e8ced7585b9cc2d94c03aa36478c2a4a71
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.