Improve model card: add pipeline_tag, library_name, license, and paper details

This PR significantly improves the model card for the model presented in [Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection](https://huggingface.co/papers/2502.13061).

Key changes include:
- **Updated `library_name` to `transformers`**: This ensures the "How to use" widget properly displays a `transformers` code snippet, as the model is compatible with `transformers` for loading and inference of its PEFT adapters.
- **Added `pipeline_tag: image-text-to-text`**: This makes the model discoverable under the appropriate task category on the Hugging Face Hub, reflecting its multimodal input and text output capabilities.
- **Added `license: cc-by-4.0`**: Provides clear licensing information.
- **Added `language: en` and relevant `tags`**: Enhances model discoverability.
- **Populated model card content**: The model card now includes the paper title, Hugging Face paper link, the abstract as a detailed model description, and filled-out sections for model details, uses, biases, training, and evaluation, providing comprehensive information about the model.
- **Added BibTeX citations**: The provided BibTeX entries for the associated papers are now correctly placed in the "Citation" section.

Please review and merge this PR if everything looks good.

README.md

@@ -1,207 +1,97 @@
 ---
 base_model: QWen/QWen2-VL-7B-Instruct
-library_name: peft
+library_name: transformers
+license: cc-by-4.0
+pipeline_tag: image-text-to-text
+language: en
+tags:
+  - multimodal
+  - hateful-speech-detection
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection
 
+This repository contains a PEFT fine-tuned Large Multimodal Model (LMM) for hateful meme detection, as presented in the paper [Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection](https://huggingface.co/papers/2502.13061).
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
+Hateful memes have become a significant concern on the Internet, necessitating robust automated detection systems. While Large Multimodal Models (LMMs) have shown promise in hateful meme detection, they face notable challenges like sub-optimal performance and limited out-of-domain generalization capabilities. Recent studies further reveal the limitations of both supervised fine-tuning (SFT) and in-context learning when applied to LMMs in this setting. To address these issues, this work proposes a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. Analysis reveals that this approach achieves improved robustness under adversarial attacks compared to SFT models. Experiments on six meme classification datasets show that this approach achieves state-of-the-art performance, outperforming larger agentic systems. Moreover, the method generates higher-quality rationales for explaining hateful content compared to standard SFT, enhancing model interpretability.
 
-### Model Sources [optional]
+-   **Developed by:** Jingbiao Mei, Jinghong Chen, Guangyu Yang, Weizhe Lin, Bill Byrne
+-   **Model type:** Large Multimodal Model (LMM), fine-tuned using PEFT (LoRA) for hateful meme detection.
+-   **Language(s) (NLP):** English
+-   **License:** cc-by-4.0
+-   **Finetuned from model:** QWen/QWen2-VL-7B-Instruct
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+-   **Paper:** [https://huggingface.co/papers/2502.13061](https://huggingface.co/papers/2502.13061)
 
 ## 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. -->
-
-[More Information Needed]
-
-### 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 is intended for the robust detection of hateful memes. It can be used to classify multimodal content (image and text) for hate speech, offering improved accuracy and cross-domain generalization. It also provides rationales for its classifications, aiding interpretability.
 
 ### 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 should not be used for generating hateful content, propagating misinformation, or any other malicious purposes. It is a detection tool and its application should align with ethical AI principles for combating harmful online content.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+All AI models, especially those dealing with sensitive content like hate speech, may exhibit biases from their training data or limitations in understanding complex nuances, sarcasm, or evolving slang. This could lead to misclassifications or biased explanations.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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 (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. Continuous monitoring, human oversight in critical applications, and further evaluation on diverse and evolving datasets are recommended.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+This model is a PEFT (Parameter-Efficient Fine-Tuning) adapter built on top of `QWen/QWen2-VL-7B-Instruct`. To use it, you typically load the base model and its tokenizer using the Hugging Face Transformers library, and then load this model as a PEFT adapter.
 
 ## 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 and evaluated on six meme classification datasets, as mentioned in the paper.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+The paper proposes a robust adaptation framework that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. The training involved fine-tuning using PEFT (LoRA).
 
 #### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+-   **Training regime:** Based on the base model, Qwen2-VL-7B-Instruct often uses mixed precision (e.g., bfloat16).
+-   **Epochs:** 3
+-   **Train Batch Size:** 16
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The model was evaluated on six meme classification datasets.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The evaluation metrics recorded during training include:
+-   `accuracy`
+-   `auroc` (Area Under the Receiver Operating Characteristic curve)
+-   `f1` (F1 Score)
+-   `precision`
+-   `recall`
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
+The proposed approach achieved state-of-the-art performance across six meme classification datasets, outperforming larger agentic systems. It also demonstrated improved robustness under adversarial attacks and generated higher-quality rationales compared to standard SFT models.
 
-[More Information Needed]
+## Citation
 
-## Environmental Impact
+If you find this work helpful, please consider citing the following papers:
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.12.0
-
-```
+```bibtex
 @inproceedings{RGCL2024Mei,
     title = "Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning",
     author = "Mei, Jingbiao  and
@@ -231,4 +121,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
           author={Mei, Jingbiao and Chen, Jinghong and Yang, Guangyu and Lin, Weizhe and Byrne, Bill},
           year={2025},
           month=may }
-```
+```
+### Framework versions
+
+- PEFT 0.12.0