Update README.md

README.md

@@ -6,6 +6,9 @@ tags:
 model-index:
 - name: solidity-generator
   results: []
+datasets:
+- mwritescode/slither-audited-smart-contracts
+pipeline_tag: text-generation
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,17 +16,33 @@ should probably proofread and complete it, then remove this comment. -->
 
 # solidity-generator
 
-This model is a fine-tuned version of [codeparrot/codeparrot-small](https://huggingface.co/codeparrot/codeparrot-small) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2180
+This model is a model specialized in generating Solidity contract codes. Derived from the [codeparrot/codeparrot-small](https://huggingface.co/codeparrot/codeparrot-small) model by Hugging Face, it's been meticulously trained on an extensive set of Solidity contracts and patterns, making it apt for assisting in drafting or suggesting contract structures.
+
 
 ## Model description
 
-More information needed
+This model has been designed specifically for generating Solidity contracts. Being a derivative of the `codeparrot-small` model, it retains the broader capabilities of the parent model while demonstrating a keen proficiency in understanding and generating Solidity-centric texts.
 
-## Intended uses & limitations
+### Performance
+
+The model reported a loss of `0.2180` on the evaluation set.
+
+## Intended Uses & Limitations
+
+
+### Intended Uses:
+1. Assist developers by auto-generating contract code snippets based on prompts.
+2. Help in understanding and drafting complex contract structures.
+
+### Limitations:
+1. The generated code must be reviewed for security and functional correctness.
+2. The clarity of the generated code largely depends on the specificity of the prompt.
+
+## Training Details
+
+### Dataset
+The model was fine-tuned on an undisclosed dataset comprised of a range of Solidity contracts.
 
-More information needed
 
 ## Training and evaluation data
 
@@ -63,4 +82,4 @@ The following hyperparameters were used during training:
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.3
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3