amgharhind
/

srd2plantUml_codet5base_V2

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 library_name: transformers
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 ---
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 ---
 library_name: transformers
+tags: [CodeT5, fine-tuning, UML, PlantUML, NLP]
 ---
+# Model Card for srd2plantUml_Salesforce_codet5-base
+## Summary
+This model is a fine-tuned version of Salesforce's CodeT5, specifically trained to generate PlantUML diagrams (use case, class, and sequence diagrams) from software requirements documents (SRD). The model is designed to aid in automating the generation of UML diagrams from textual descriptions, providing a valuable tool for software design and documentation.
 ## Model Details
 ### Model Description
+The `srd2plantUml_Salesforce_codet5-base` model was fine-tuned using a dataset of software requirement documents paired with corresponding PlantUML code. It is aimed at generating accurate UML diagrams based on SRD content, supporting developers and engineers in visualizing system designs from textual specifications.
+- **Developed by:** Hind Amghar
+- **Model type:** CodeT5 (Transformer-based)
+- **Language(s):** English (NLP for software requirements processing)
+- **Fine-tuned from model:** `Salesforce/codet5-base`
+### Model Sources
+- **Paper:** If there's no direct paper, consider linking to the CodeT5 paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/abs/2109.00859)
 ## Uses
 ### Direct Use
+The model can be directly used to generate PlantUML code for UML diagrams (use case, class, sequence) from SRD inputs. This is particularly useful for:
+- Software engineering teams looking to automate the creation of UML diagrams.
+- Documentation and design teams needing to visualize requirements.
+### Downstream Use
+The model can be fine-tuned further for specific domains or customized UML structures if more specialized diagram types are needed.
 ### Out-of-Scope Use
+This model is not suited for tasks unrelated to software requirement document analysis or UML diagram generation. Misuse may include attempts to generate non-UML visualizations or diagrams that the model was not trained on.
 ## Bias, Risks, and Limitations
+The model may have limitations in handling complex or highly technical SRDs outside of the dataset it was trained on. Additionally, since the dataset lacks domain diversity, it may perform less accurately on projects outside common software domains (e.g., management, e-commerce).
 ### Recommendations
+Users should be cautious of potential limitations in complex diagrams and may need to manually adjust or review the generated PlantUML code for accuracy, especially in domains not covered by the training data.
 ## How to Get Started with the Model
+To use the model:
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+# Load the model and tokenizer
+model = AutoModel.from_pretrained("amgharhind/srd2plantUml_codet5base_V2")
+tokenizer = AutoTokenizer.from_pretrained("amgharhind/srd2plantUml_codet5base_V2")
+# Example usage
+input_text = "Sample SRD input for a use case diagram"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+uml_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Generated PlantUML code:", uml_code)