Create README.md

README.md

@@ -0,0 +1,56 @@
+---
+license: apache-2.0
+datasets:
+- Lin-Chen/ShareGPT4V
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+- google/siglip-so400m-patch14-384
+pipeline_tag: text-generation
+---
+
+
+# MLM-Filter-Qwen2.5-1.5B-GPT4o Model Card
+
+## Model details
+
+**Model type:**
+MLM-Filter-Qwen2.5-1.5B-GPT4o is an open-source MLLM trained to assess the data quality of image-text paired data. It can generate 4 quality metrics for image-text data: Image Text Matching, Object Detail Fulfillment, Caption Text Quality, and Semantic Understanding.
+
+**Model date:**
+MLM-Filter-Qwen2.5-1.5B-GPT4o was trained in Dec 2024.
+
+**Paper or resources for more information:**
+https://mlm-filter.github.io/
+
+```
+@article{wang2024finetuned,
+  title={Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters},
+  author={Wang, Weizhi and Mrini, Khalil and Yang, Linjie and Kumar, Sateesh and Tian, Yu and Yan, Xifeng and Wang, Heng},
+  journal={arXiv preprint arXiv:2403.02677},
+  year={2024}
+}
+```
+
+## License
+Qwen LICENSE AGREEMENT
+
+**Where to send questions or comments about the model:**
+https://github.com/Victorwz/MLM_Filter/issues
+
+## Intended use
+**Primary intended uses:**
+MLM-Filter can be used as a drop-in replacement for CLIPScore in these tasks:
+
+1. Score image-text data in large-scale pre-training dataset and then filter high-quality subsets based on the scores (For training MLLMs or VLMs, please consider to jointly use the Image-Text Matching score and the Object Detail Fulfillment score);
+
+2. Evaluate the image-text alignment for image2text or text2image generation models;
+
+3. Any potential applications with the need to calculate the image-text alignment.
+
+
+## Training dataset (709K)
+- 665k ShareGPT4V data.
+- 44k instructions on image-text data quality assessment tasks ranging across 4 metrics.
+
+## Usage Sample
+Please follow the instructions in https://github.com/Victorwz/MLM_Filter.