Instructions to use maveriq/lingbert-mini-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maveriq/lingbert-mini-1M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="maveriq/lingbert-mini-1M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("maveriq/lingbert-mini-1M") model = AutoModelForMaskedLM.from_pretrained("maveriq/lingbert-mini-1M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56bb13a6a1f86962325a62370a565ba629000ce5090bee607d2f1f1c39b341b4
- Size of remote file:
- 44.9 MB
- SHA256:
- 4464dc12705cc7f889a4f7d63f811ed43fada729386785cb754b59b8ff381813
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