Instructions to use subhasisj/MiniLMv2-qa-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/MiniLMv2-qa-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/MiniLMv2-qa-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/MiniLMv2-qa-encoder") model = AutoModelForMaskedLM.from_pretrained("subhasisj/MiniLMv2-qa-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 134 Bytes
13a00c2 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:05aee358dfb4b2d5abb94b45d3f8b28962800438c13437e9a97ad64af652e6b4
size 855744041
|