Update README.md

README.md

@@ -19,10 +19,9 @@ tags:
 
 # Model description
 
-CodeLlama-7B-KStack-clean model is a fine-tuned open-source generative text model fine-tuned on [JetBrains/KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) dataset. 
-This is a repository for fine-tuned CodeLlama-7b model in the Hugging Face Transformers format.
+This is a repository for the **CodeLlama-7b** model fine-tuned on the [KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) dataset with rule-based filtering, in the *Hugging Face Transformers* format. KStack-clean is a small subset of [KStack](https://huggingface.co/datasets/JetBrains/KStack), the largest collection of permissively licensed Kotlin code, automatically filtered to include files that have the highest "educational value for learning algorithms in Kotlin".
 
-# Model use
+# How to use
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -69,24 +68,23 @@ The model was trained on one A100 GPU with following hyperparameters:
 |        `total_batch_size`        |        32 (~30K tokens per step)          |
 |        `num_epochs`        |          2          |
 
-More details about finetuning can be found in the technical report
+More details about fine-tuning can be found in the technical report.
 
 # Fine-tuning data
 
-For this model we used 25K exmaples of [KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) selected according to educational value for learning algorithms. In total dataset contains about 23M tokens. 
-For more information about the dataset follow the link.
+For this model, we used 25K exmaples from the [KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) dataset, selected from the larger [KStack](https://huggingface.co/datasets/JetBrains/KStack) dataset according to educational value for learning algorithms. In total, the dataset contains about 23M tokens. 
 
 # Evaluation 
 
-To evaluate we used Kotlin Humaneval ([more infromation here](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval))
+For evaluation, we used the [Kotlin HumanEval](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval) dataset, which contains all 161 tasks from HumanEval translated into Kotlin by human experts. You can find more details about the pre-processing necessary to obtain our results, including the code for running, on the [datasets's page](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval).
 
-Fine-tuned model:
+Here are the results of our evaluation:
 
 |         **Model name**           |             **Kotlin HumanEval Pass Rate**              |
 |:---------------------------:|:----------------------------------------:|
-|           `base model`            |           26.89            |
-|        `fine-tuned model`        |          37.89        |
+|           `CodeLlama-7B`            |           26.89            |
+|        `CodeLlama-7B-KStack-clean`        |          **37.89**        |
 
 # Ethical Considerations and Limitations
 
-CodeLlama-7B-KStack-clean and its variants are a new technology that carries risks with use. The testing conducted to date could not cover all scenarios. For these reasons, as with all LLMs, CodeLlama-7B-KStack-clean potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of CodeLlama-7B-KStack-clean, developers should perform safety testing and tuning tailored to their specific applications of the model.
+CodeLlama-7B-KStack-clean is a new technology that carries risks with use. The testing conducted to date has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, CodeLlama-7B-KStack-clean's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of CodeLlama-7B-KStack-clean, developers should perform safety testing and tuning tailored to their specific applications of the model.