---
tags:
- bert

---
# Model Card for ernie-gram 
 
 
# Model Details
 
## Model Description
 
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding.
 
- **Developed by:** Dongling Xiao, Yukun Li, Han Zhang, Yu Sun, Hao Tian, Hua Wu and Haifeng Wang
- **Shared by [Optional]:**  Peterchou 
- **Model type:** More information needed
- **Language(s) (NLP):** Chinese, English (more information needed)
- **License:** More information needed
- **Related Models:**
  - **Parent Model:** BERT
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/PaddlePaddle/ERNIE)
    - [Associated Paper](https://arxiv.org/abs/2010.12148)
 	
 
# Uses
 
 
## Direct Use
 
 More information needed
 
## Downstream Use [Optional]
 
This model could also be used for the task of question answering and text classification.
 
## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people.
 
# Bias, Risks, and Limitations
 
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 
 
## Recommendations
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 
# Training Details
 
## Training Data
 
The model creators note in the [associated paper](https://arxiv.org/abs/2010.12148):
 
>English Pre-training Data. We use two com- mon text corpora for English pre-training:
    • Base-scale corpora: 16GB uncompressed text from WIKIPEDIA and BOOKSCORPUS (Zhu et al., 2015), which is the original data for BERT. 
    • Large-scale corpora: 160GB uncompressed text from WIKIPEDIA, BOOKSCORPUS, OPEN- WEBTEXT3, CC-NEWS (Liu et al., 2019) and STORIES (Trinh and Le, 2018), which is the original data used in RoBERTa.

> Chinese Pre-training Data. We adopt the same Chinese text corpora used in ERNIE2.0 (Sun et al., 2020) to pre-train ERNIE-Gram. 
				
			
		
 
 
## Training Procedure
 
 
### Preprocessing
 
The model authors note in the [associated paper](https://arxiv.org/abs/2010.12148):
> For pre-training on base-scale English corpora, the batch size is set to 256 sequences, the peak learning rate is 1e-4 for 1M training steps, which are the same settings as BERTBASE. As for large-scale English corpora, the batch size is 5112 sequences, the peak learning rate is 4e-4 for 500K training steps. For pre-training on Chinese corpora, the batch size is 256 sequences, the peak learning rate is 1e-4 for 3M training steps. 

### Speeds, Sizes, Times
 
More information needed
 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
More information needed
 
### Factors
 
 
### Metrics
 
More information needed
## Results 
 
Classification and matching use the CLUE dataset.
CLUE evaluation results:
 
| 配置     | 模型             | CLUEWSC2020 | IFLYTEK | TNEWS | AFQMC | CMNLI | CSL   | OCNLI | 平均值 |
|----------|------------------|-------------|---------|-------|-------|-------|-------|-------|--------|
| 20L1024H | ERNIE 3.0-XBase  | 91.12       | 62.22   | 60.34 | 76.95 | 84.98 | 84.27 | 82.07 | 77.42  |
| 12L768H  | ERNIE 3.0-Base   | 88.18       | 60.72   | 58.73 | 76.53 | 83.65 | 83.30 | 80.31 | 75.63  |
| 6L768H   | ERNIE 3.0-Medium | 79.93       | 60.14   | 57.16 | 74.56 | 80.87 | 81.23 | 77.02 | 72.99  |
 
 
 
# Model Examination
 
More information needed
 
# Environmental Impact
 
 
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
 
# Technical Specifications [optional]
 
## Model Architecture and Objective
 
More information needed
 
## Compute Infrastructure
 
More information needed
 
### Hardware
 
More information needed
 
### Software
More information needed
 
# Citation
 
 
**BibTeX:**
 ```
@article{xiao2020ernie,
  title={ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding},
  author={Xiao, Dongling and Li, Yu-Kun and Zhang, Han and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},
  journal={arXiv preprint arXiv:2010.12148},
  year={2020}
}
```
 
 
 
# Glossary [optional]
More information needed
 
# More Information [optional]
 
More information needed
 
# Model Card Authors [optional]
 
 
Peterchou in collaboration with Ezi Ozoani and the Hugging Face team
 
# Model Card Contact
 
More information needed
 
# How to Get Started with the Model
 
Use the code below to get started with the model.
 
<details>
<summary> Click to expand </summary>

```python
from transformers import AutoModel
 
model = AutoModel.from_pretrained("peterchou/ernie-gram")
```
</details>