Instructions to use google-bert/bert-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-chinese")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-chinese") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-chinese") - Inference
- Notebooks
- Google Colab
- Kaggle
Add link to Neuron-optimized version
Browse files🤖 Neuron Export Bot: Adding link to Neuron-optimized version.
A Neuron-optimized version of this model has been created at [badaoui/google-bert-bert-base-chinese-neuron](https://huggingface.co/badaoui/google-bert-bert-base-chinese-neuron).
The optimized version provides improved performance on AWS Inferentia/Trainium instances with pre-compiled artifacts.
Generated by: [badaoui](https://huggingface.co/badaoui)
Generated using: [Optimum Neuron Compiler Space](https://huggingface.co/spaces/optimum/neuron-export)
README.md
CHANGED
|
@@ -70,4 +70,16 @@ tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
|
|
| 70 |
|
| 71 |
model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
|
| 72 |
|
| 73 |
-
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 70 |
|
| 71 |
model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
|
| 72 |
|
| 73 |
+
```
|
| 74 |
+
|
| 75 |
+
---
|
| 76 |
+
## 🚀 AWS Neuron Optimized Version Available
|
| 77 |
+
|
| 78 |
+
A Neuron-optimized version of this model is available for improved performance on AWS Inferentia/Trainium instances:
|
| 79 |
+
|
| 80 |
+
**[badaoui/google-bert-bert-base-chinese-neuron](https://huggingface.co/badaoui/google-bert-bert-base-chinese-neuron)**
|
| 81 |
+
|
| 82 |
+
The Neuron-optimized version provides:
|
| 83 |
+
- Pre-compiled artifacts for faster loading
|
| 84 |
+
- Optimized performance on AWS Neuron devices
|
| 85 |
+
- Same model capabilities with improved inference speed
|