model documentation

README.md

@@ -1,7 +1,178 @@
----
-license: mit
----
-
-## MiniLM: 6 Layer Version
-
-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.
+---
+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>