|
--- |
|
license: mit |
|
|
|
--- |
|
|
|
|
|
# Model Card for MiniLM: 6 Layer Version |
|
|
|
# Model Details |
|
|
|
## Model Description |
|
|
|
This is a 6 layer version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased/) by keeping only every second layer. |
|
|
|
- **Developed by:** Nils Reimers |
|
- **Shared by [Optional]:** Nils Reimers |
|
- **Model type:** Feature Extraction |
|
- **Language(s) (NLP):** More information needed |
|
- **License:** MIT |
|
- **Parent Model:** [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased/) |
|
- **Resources for more information:** More information needed. |
|
|
|
|
|
|
|
# Uses |
|
|
|
|
|
## Direct Use |
|
This model can be used for the task of feature extraction. |
|
|
|
## 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 |
|
More information needed |
|
|
|
### 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 |
|
|
|
More information needed. |
|
|
|
# Citation |
|
|
|
|
|
**BibTeX:** |
|
|
|
|
|
```bibtex |
|
@misc{wang2020minilm, |
|
title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers}, |
|
author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou}, |
|
year={2020}, |
|
eprint={2002.10957}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
|
|
|
|
|
|
# Glossary [optional] |
|
More information needed |
|
|
|
# More Information [optional] |
|
More information needed |
|
|
|
|
|
# Model Card Authors [optional] |
|
|
|
Nils Reimers 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 AutoTokenizer, AutoModel |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("nreimers/MiniLM-L6-H384-uncased") |
|
|
|
model = AutoModel.from_pretrained("nreimers/MiniLM-L6-H384-uncased") |
|
``` |
|
</details> |
|
|
