Model Card for Model ID
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
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: Pranav Keshav
- Model type: Text generation
- Language(s) (NLP): English
- License: [More Information Needed]
- Finetuned from model : google/codegemma-2b
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Uses
Direct Use
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.
Downstream Use
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
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
Recommendations
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.
How to Get Started with the Model
Use the code below to generate react component code from the model:
from transformers import GemmaTokenizer, AutoModelForCausalLM
tokenizer = GemmaTokenizer.from_pretrained("PranavKeshav/reactgpt-1.2")
model = AutoModelForCausalLM.from_pretrained("PranavKeshav/reactgpt-1.2")
input_text = "PageNotFound component"
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
Training Details
Training Data
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Training Procedure
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Evaluation
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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