Instructions to use muhtasham/olm-bert-tiny-december-2022 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhtasham/olm-bert-tiny-december-2022 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="muhtasham/olm-bert-tiny-december-2022")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("muhtasham/olm-bert-tiny-december-2022") model = AutoModelForMaskedLM.from_pretrained("muhtasham/olm-bert-tiny-december-2022", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c55635adac18887b1d8eb51af8958b90e38edfb199d87143ec6e5fe619de2846
- Size of remote file:
- 27.9 MB
- SHA256:
- 50f26a7c55a180c7d21e069f15e90085e313fe2ce422ae2e413b4aebf6e587f7
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