Instructions to use tejaskamtam/testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tejaskamtam/testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tejaskamtam/testing")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tejaskamtam/testing") model = AutoModelForMaskedLM.from_pretrained("tejaskamtam/testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2821fe6e0ada7db165c2f7b7b71bac54408684825e6e2a0a225d175376410c49
- Size of remote file:
- 438 MB
- SHA256:
- 7702735369fd181b794db2f3ac6829731fdd8c096b1a41e51ecc6c7b14d13773
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