Update README.md

README.md

@@ -5,12 +5,17 @@ tags:
 model-index:
 - name: santacoder-finetuned-the-stack-swift
   results: []
+datasets:
+- bigcode/the-stack-dedup
+language:
+- code
+pipeline_tag: text-generation
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# santacoder-finetuned-the-stack-swift
+# SantaCoder 🎅 fine-tuned on Swift 🍏
 
 This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset.
 It achieves the following results on the evaluation set:
@@ -18,7 +23,9 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+The [SantaCoder](https://huggingface.co/bigcode/santacoder) models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests). 
+The main model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255).
+In addition, there are several models that were trained on datasets with different filter parameters and with architecture and objective variations. 
 
 ## Intended uses & limitations
 
@@ -26,7 +33,7 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.**
 
 ## Training procedure