Upload README.md with huggingface_hub

README.md

@@ -66,7 +66,8 @@ library_name: transformers
 <br>
 
 # MTEB Leaderboard Chinese Reranking Results
-We have validated the performance of our model on the [mteb-chinese-reranking leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Currently, the open-source models on this leaderboard are primarily bidirectional discriminative models (BERT-like models). The only unidirectional generative model (GPT-like model) is gte-Qwen1.5-7B-instruct, which has an average score of 66.38, ranking 25th, with less than ideal results. Our self-developed unidirectional generative model, 360Zhinao-1.8B-reranking, achieved an average score of 70.13, currently ranking first overall and first among open-source models, opening up new possibilities for generative models to undertake discriminative tasks.
+We have validated the performance of our model on the [mteb-chinese-reranking leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Currently, the open-source models on this leaderboard are primarily bidirectional discriminative models (BERT-like models). The only unidirectional generative model (GPT-like model) is gte-Qwen1.5-7B-instruct, which has an average score of 66.38, ranking 25th, with less than ideal results. Our self-developed unidirectional generative model, 360Zhinao-1.8B-reranking, achieved an average score of 70.13, currently ranking first overall and first among open-source models, opening up new possibilities for generative models to undertake discriminative tasks. For more detail, please check [360zhinao](https://github.com/Qihoo360/360zhinao/tree/main). 
+
 
 | Model | T2Reranking | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |  
 |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|  
@@ -102,13 +103,13 @@ FLASH_ATTENTION_FORCE_BUILD=TRUE ./miniconda3/bin/python -m pip install flash-at
 
 # Model Introduction
 
-The 360Zhinao-1.8B-reranking model utilizes the self-developed zhinao_1-8b_base model as its foundation. Through iterative discovery and resolution of the following technical issues, it continuously stimulates the world knowledge inherent in the large model during the pre-training phase, better bridging the gap between generative models and discriminative tasks.
+The **360Zhinao-1.8B-Reranking** model utilizes the self-developed 360Zhinao-1.8b-Base model as its foundation. Through iterative discovery and resolution of the following technical issues, it continuously stimulates the world knowledge inherent in the large model during the pre-training phase, better bridging the gap between generative models and discriminative tasks.
 
 ## Data Processing
 
-The model training did not utilize world knowledge, meaning it neither continued pre-training with domain-specific data nor fine-tuned datasets outside of the four datasets on the leaderboard. It only used the four datasets within the leaderboard, carefully iterating through data perception, and targeting different datasets for data cleaning and mining to ensure that the ranking in individual tasks could reach the top three level.
+The model training did not utilize world knowledge, meaning it neither continue pre-training with domain-specific data nor fine-tuned datasets outside of the four datasets on the leaderboard. It only used the four datasets within the leaderboard, carefully iterating through data perception, data cleaning and data mining to ensure that the ranking in individual tasks could reach the top three level.
 
-## Resolving Task Conflicts
+## Resolving Task Conflicts and Catastrophic Forgetting
 
 When merging four tasks, due to different data domain distributions, answer patterns, training data volumes, convergence steps, and even sequence lengths, conflicts exist between different tasks. Deeply resolving these conflict issues is crucial to obtaining a universal model with the best comprehensive indicators across different tasks.