Instructions to use Harshatheeswar/Ltg_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harshatheeswar/Ltg_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Harshatheeswar/Ltg_bert", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Harshatheeswar/Ltg_bert", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fab4f589c0b0806548747189fb4b5795fdb9230111a04d17192ae87745918d3
- Size of remote file:
- 5.3 kB
- SHA256:
- a48b72f3695e3f7b78b0481ffa1659d9c3b6ac4e72f876c2390d68f47082ddb0
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