Improve model card: Add model description, code link, and paper link

README.md

@@ -33,172 +33,72 @@ model-index:
       name: Accuracy
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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+# Model Card for Qwen2-VL 7B RSLORA Offensive Meme Singapore
 
+This model is a fine-tuned version of Qwen2-VL-7B-Instruct for offensive meme classification in the Singapore context.  It was trained on the [multimodal_meme_classification_singapore](https://huggingface.co/datasets/aliencaocao/multimodal_meme_classification_singapore) dataset.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
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+This model classifies memes as offensive or not, taking into account Singaporean social context. It leverages the visual and textual understanding capabilities of Qwen2-VL-7B-Instruct.
 
-- **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]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Cao Yuxuan, Wu Jiayang, Alistair Cheong Liang Chuen, Bryan Shan Guanrong, Theodore Lee Chong Jen, and Sherman Chann Zhi Shen
+- **Model type:** Vision-Language Model (VLM)
+- **Language(s) (NLP):** en
+- **License:** MIT
+- **Finetuned from model:** Qwen/Qwen2-VL-7B-Instruct
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** [https://github.com/aliencaocao/vlm-for-memes-aisg](https://github.com/aliencaocao/vlm-for-memes-aisg)
+- **Paper:** [Detecting Offensive Memes with Social Biases in Singapore Context Using Multimodal Large Language Models](https://arxiv.org/abs/2502.18101)
 
-- **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. -->
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 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model can be used directly to classify memes.  See the code example in the "How to Get Started" section.
 
 ### Downstream Use [optional]
 
-<!-- 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 further fine-tuned for other related tasks or incorporated into a larger content moderation system.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is specifically trained for the Singaporean context and may not generalize well to other cultures or languages.  It should not be used to make definitive judgments about individuals or groups.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Like any machine learning model, this model may exhibit biases present in the training data.  It is important to be aware of these limitations and use the model responsibly.  Further research is needed to assess and mitigate potential biases.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should be aware of the potential for bias and limitations in the model's performance.  It is recommended to use this model as a tool to assist human moderators rather than a replacement for human judgment.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+See the model repository's README for usage examples: [https://github.com/aliencaocao/vlm-for-memes-aisg](https://github.com/aliencaocao/vlm-for-memes-aisg)
 
-[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+The model was trained on the [multimodal_meme_classification_singapore](https://huggingface.co/datasets/aliencaocao/multimodal_meme_classification_singapore) dataset. This dataset contains memes labeled as offensive or not within the Singaporean context.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
+More details about the training procedure can be found in the paper.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
+The model achieved an AUROC of 0.8192 and an accuracy of 0.8043 on a held-out test set.  See the paper for more details on the evaluation methodology.
 
-### Results
+## Citation
 
-[More Information Needed]
-
-#### Summary
-
-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
-
-```
+```bibtex
 @misc{yuxuan2025detectingoffensivememessocial,
       title={Detecting Offensive Memes with Social Biases in Singapore Context Using Multimodal Large Language Models}, 
       author={Cao Yuxuan and Wu Jiayang and Alistair Cheong Liang Chuen and Bryan Shan Guanrong and Theodore Lee Chong Jen and Sherman Chann Zhi Shen},
@@ -208,22 +108,4 @@ Use the code below to get started with the model.
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2502.18101}, 
 }
-```
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
-
-[More Information Needed]
+```