Instructions to use nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large") model = AutoModelForMaskedLM.from_pretrained("nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a5408e970bccc3824c6676d26b6bab8781ab9381dba36d82ce4eabf9f7d5a0e
- Size of remote file:
- 165 MB
- SHA256:
- fecdfc2dd7e85f0aba3d6442ecf2b2abbbbf459fe7d51adcef5e9e767f391712
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