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", device_map="auto")# 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,8 +1,8 @@
|
|
| 1 |
-
---
|
| 2 |
-
license: mit
|
| 3 |
-
tags:
|
| 4 |
-
- oxford-legacy
|
| 5 |
-
---
|
| 6 |
|
| 7 |
# Model
|
| 8 |
MiniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency.
|
|
@@ -45,4 +45,8 @@ If you use the model, please cite our paper:
|
|
| 45 |
pages={1161--1173},
|
| 46 |
year={2023}
|
| 47 |
}
|
| 48 |
-
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
tags:
|
| 4 |
+
- oxford-legacy
|
| 5 |
+
---
|
| 6 |
|
| 7 |
# Model
|
| 8 |
MiniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency.
|
|
|
|
| 45 |
pages={1161--1173},
|
| 46 |
year={2023}
|
| 47 |
}
|
| 48 |
+
```
|
| 49 |
+
|
| 50 |
+
# Support
|
| 51 |
+
If this model helps your work, you can keep the project running with a one-off or monthly contribution:
|
| 52 |
+
https://github.com/sponsors/nlpie-research
|