|
--- |
|
tags: |
|
- bert |
|
|
|
--- |
|
# Model Card for simbert-chinese-base |
|
|
|
|
|
|
|
# Model Details |
|
|
|
## Model Description |
|
|
|
|
|
- **Developed by:** peterchou |
|
- **Shared by [Optional]:** More information needed |
|
- **Model type:** More information needed |
|
- **Language(s) (NLP):** More information needed |
|
- **License:** More information needed |
|
- **Related Models:** |
|
- **Parent Model:** BERT |
|
- **Resources for more information:** |
|
- [GitHub Repo](https://github.com/zejunwang1/bert4vec) |
|
|
|
|
|
# Uses |
|
|
|
|
|
## Direct Use |
|
|
|
This model can be used for the task of sentence vector pre-training models |
|
|
|
|
|
|
|
## Downstream Use [Optional] |
|
|
|
More information needed |
|
|
|
## 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 |
|
|
|
More information needed |
|
|
|
## Training Procedure |
|
|
|
|
|
### Preprocessing |
|
|
|
More information needed |
|
|
|
### Speeds, Sizes, Times |
|
|
|
More information needed |
|
|
|
# Evaluation |
|
|
|
|
|
## Testing Data, Factors & Metrics |
|
|
|
### Testing Data |
|
|
|
More information needed |
|
|
|
### Factors |
|
|
|
|
|
### Metrics |
|
|
|
More information needed |
|
## Results |
|
|
|
More information needed |
|
|
|
# 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 |
|
* transformers>=4.6.0,<5.0.0 |
|
* torch>=1.6.0 |
|
* numpy |
|
* huggingface-hub |
|
* faiss (optional) |
|
|
|
|
|
# Citation |
|
|
|
|
|
**BibTeX:** |
|
``` |
|
More information needed |
|
``` |
|
|
|
**APA:** |
|
``` |
|
More information needed |
|
``` |
|
|
|
# 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/simbert-chinese-base") |
|
|
|
``` |
|
</details> |
|
|