model documentation (#2)

- model documentation (4e2edd99993fccdf5928d170acd3ff3d7eb44147)


Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>

README.md

@@ -1,21 +1,178 @@
+
 ---
 language:
-  - code
+- code
 license: apache-2.0
 widget:
-  - text: 'public [MASK] isOdd(Integer num) {if (num % 2 == 0) {return "even";} else {return "odd";}}'
+- text: public [MASK] isOdd(Integer num) {if (num % 2 == 0) {return "even";} else
+    {return "odd";}}
 ---
-## JavaBERT
+
+# Model Card for JavaBERT
+ 
 A BERT-like model pretrained on Java software code.
-### Training Data
+ 
+ 
+ 
+ 
+ 
+ 
+# Model Details
+ 
+## Model Description
+ 
+A BERT-like model pretrained on Java software code.
+ 
+- **Developed by:** Christian-Albrechts-University of Kiel (CAUKiel)
+- **Shared by [Optional]:** Hugging Face
+- **Model type:** Fill-Mask
+- **Language(s) (NLP):** en
+- **License:** Apache-2.0
+- **Related Models:** A version of this model using an uncased tokenizer is available at [CAUKiel/JavaBERT-uncased](https://huggingface.co/CAUKiel/JavaBERT-uncased).
+  - **Parent Model:** BERT
+- **Resources for more information:** 
+  - [Associated Paper](https://arxiv.org/pdf/2110.10404.pdf)
+ 
+ 
+# Uses
+ 
+## Direct Use
+ 
+Fill-Mask
+ 
+## 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.
+{ see paper= word something)
+ 
+# Training Details
+ 
+## Training Data
 The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A ```bert-base-cased``` tokenizer is used by this model.
+ 
+## Training Procedure
+ 
+ 
 ### Training Objective
 A MLM (Masked Language Model) objective was used to train this model.
-### Usage
-```python
+ 
+### 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
+ 
+More information needed.
+ 
+# Citation
+ 
+ 
+ 
+**BibTeX:**
+ 
+More information needed.
+ 
+**APA:**
+ 
+More information needed.
+ 
+# Glossary [optional]
+More information needed.
+ 
+# More Information [optional]
+ 
+More information needed.
+ 
+# Model Card Authors [optional]
+ 
+Christian-Albrechts-University of Kiel (CAUKiel)  in collaboration with Ezi Ozoani and the team at Hugging Face
+ 
+# 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 pipeline
 pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT')
 output = pipe(CODE) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code.
 ```
-#### Related Model
-A version of this model using an uncased tokenizer is available at [CAUKiel/JavaBERT-uncased](https://huggingface.co/CAUKiel/JavaBERT-uncased).
+ 
+</details>
+