Update README.md

README.md

@@ -3,77 +3,71 @@ library_name: transformers
 tags:
 - trl
 - sft
+datasets:
+- Hardik1234/reactjs_labelled
+language:
+- en
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
+This model generates React component code from natural language descriptions. It leverages the capabilities of the CodeGemma-2B model for text-to-code generation tasks.
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This is a text-to-React component code generation model fine-tuned on the `Hardik1234/reactjs_labelled` dataset with CodeGemma-2B as the base model. It aims to assist developers by generating React component code from textual descriptions, streamlining the development process.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Developed by:** Pranav Keshav
+- **Model type:** Text generation
+- **Language(s) (NLP):** English
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model :** google/codegemma-2b
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
 ## 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. -->
+The model can be used to generate React component code from textual descriptions, such as "NavBar component," which can be integrated directly into React applications.
 
-[More Information Needed]
-
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+This model can be fine-tuned further for specific use cases or integrated into development tools and platforms to enhance developer productivity by automating code generation.
 
 ### 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 quite suitable for generating code for non-React frameworks or languages. It may also produce incorrect or non-functional code if the input description is unclear or ambiguous.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Recommendations
 
-[More Information Needed]
+Users should be aware that the generated code may require manual verification and refinement. The model may also reflect biases present in the training data, and care should be taken to review and test the generated code thoroughly.
 
-### Recommendations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Use the code below to generate react component code from the model:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```python
+from transformers import GemmaTokenizer, AutoModelForCausalLM
 
-## How to Get Started with the Model
+tokenizer = GemmaTokenizer.from_pretrained("PranavKeshav/reactgpt-1.2")
+model = AutoModelForCausalLM.from_pretrained("PranavKeshav/reactgpt-1.2")
 
-Use the code below to get started with the model.
+input_text = "PageNotFound component"
+input_ids = tokenizer(input_text, return_tensors="pt")
 
-[More Information Needed]
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
 ## Training Details