Update README.md

README.md

@@ -5,6 +5,7 @@ tags:
 metrics:
 - accuracy
 - perplexity
+license: mit
 ---
 
 # Model Card for Model ID
@@ -17,12 +18,10 @@ This is a 🤗 transformers model card that was pre-trained on a dataset built f
 
 The model is a language model pre-trained on Kotlin code from the JetBrains Kotlin repository on GitHub. It is designed to complete and understand code in these programming languages.
 
-- **Developed by:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
+- **Developed by:** https://huggingface.co/belka13
 - **Model type:** Language model
 - **Language(s) (NLP):** Kotlin, Python
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** microsoft/phi-1_5
+- **Finetuned from model:** microsoft/phi-1_5
 
 ### Model Sources [optional]
 
@@ -30,25 +29,13 @@ The model is a language model pre-trained on Kotlin code from the JetBrains Kotl
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be used to augment Kotlin and Python code, finding bugs and style inconsistencies in existing code. It can also help in understanding and documenting code bases.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is not intended to generate arbitrary text or code in programming languages other than Kotlin and Python. It should also not be used to automatically make changes to production code without human review.
 
 ## Bias, Risks, and Limitations
 
@@ -58,133 +45,21 @@ The model may have biases and limitations based on code in the JetBrains Kotlin
 
 Users should be aware of the risks, biases and limitations of the model. Model-generated code should always be tested by experienced developers before use in production. The model is best suited for auxiliary purposes, such as learning Kotlin/Python syntax and style, rather than for writing production code yourself.
 
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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 [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
+The following metrics were used to evaluate the model in Kotlin and Python:
 
-[More Information Needed]
+| Model          | Dataset | Accuracy | Perplexity |
+|----------------|---------|----------|------------|
+| Default        | Kotlin  | 0.0031   | 94.78      |
+|                | Python  | 0.0067   | 6.67       |
+| Fine-tuned     | Kotlin  | 0.0096   | 62.82      |
+|                | Python  | 0.0180   | 5.71       |
 
-## Model Card Authors [optional]
 
-[More Information Needed]
 
-## Model Card Contact
 
-[More Information Needed]