Instructions to use zeifar/mlm-miniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeifar/mlm-miniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zeifar/mlm-miniLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("zeifar/mlm-miniLM") model = AutoModel.from_pretrained("zeifar/mlm-miniLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from zeifar/mlm-miniLM: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/zeifar/mlm-miniLM/resolve/main/tokenizer.json
- Command line
-
hf download hf://zeifar/mlm-miniLM/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/zeifar/mlm-miniLM/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 3754dea70b09657834bcb673827ffe3411a959595ddc53f6939c0fe0a8284b81
- Size of remote file:
- 17.1 MB
- SHA256:
- 43ceb89e12bfb82c2c75398e3d8e9e2b378390c7c74121ad1a7c1d3321e3fdba
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