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 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
- 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) and Bender et al. (2021)). 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 presented in Lacoste et al. (2019).
- 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:
@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.
Click to expand
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nreimers/MiniLM-L6-H384-uncased")
model = AutoModel.from_pretrained("nreimers/MiniLM-L6-H384-uncased")