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:
- d9caf37e7a914f867c0e65e1078f26657621c3f9b65640517407298a3ba7d3a6
- Size of remote file:
- 3.45 kB
- SHA256:
- 52125965e5057bb9a1336c6e4a8639f2f82d8017c55e21e6286d388e0bf4f241
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