Instructions to use nlpie/miniALBERT-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpie/miniALBERT-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpie/miniALBERT-128")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlpie/miniALBERT-128") model = AutoModelForMaskedLM.from_pretrained("nlpie/miniALBERT-128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: mit
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# Model
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MiniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency.
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pages={1161--1173},
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year={2023}
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}
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```
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---
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license: mit
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tags:
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- oxford-legacy
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---
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# Model
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MiniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency.
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pages={1161--1173},
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year={2023}
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}
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```
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