Instructions to use nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large") model = AutoModelForMaskedLM.from_pretrained("nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large") - Inference
- Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
MiniLMv2
This is a MiniLMv2 model from: https://github.com/microsoft/unilm
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