Upload README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+<h1 align="center">KaLM-Embedding-V2</h1>
+
+
+**KaLM-Embedding-V2** is a versatile and compact embedding model, which achieves impressive performance in general-purpose text embedding tasks by leveraging superior training techniques and data.
+
+KaLM-embedding-multilingual-mini-instruct-v2 is trained from [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) with massive weakly-supervised pre-training and high-quality supervised fine-tuning data.
+
+The model incorporates several innovative designs:
+- Architectural Design: integration of bidirectional attention, enhancing representation learning.
+- Training Recipe: multi-stage training strategy, progressively improving the generalization and performance.
+- Training Objective: focal-style reweighting mechanism and online hard-negative mixing strategy to improve the efficiency and continuity of embedding training.
+- Training Data: 20 categories of data for pre-training and 100 categories of data for fine-tuning, as well as comprehensive recipes for curating training datasets.
+
+## Model Information
+- Model Size: 0.5B
+- Embedding Dimension: 896
+- Max Input Tokens: 32k
+- MLR: 896 512 256 128 64
+
+## 📑 Open-source Plan
+
+- [x] Model Checkpoint 
+    - [x] [KaLM-embedding-multilingual-mini-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1)
+    - [x] [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1)
+    - [x] [KaLM-embedding-multilingual-mini-instruct-v1.5](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5)
+    - [x] [KaLM-embedding-multilingual-mini-instruct-v2](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2)
+- [x] Training and Evaluation Code: [HITsz-TMG/KaLM-Embedding](https://github.com/HITsz-TMG/KaLM-Embedding)
+- [x] Technical Report: [KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model](https://arxiv.org/abs/2501.01028)
+- [ ] Training Data
+
+
+## Evaluation
+### Overall results on MTEB (cmn, v1) and MTEB (eng, v1).
+![overall](./imgs/overall.jpg)
+
+### Detailed model performance on MTEB (cmn, v1).
+![mteb_cmn](./imgs/mteb_cmn.jpg)
+
+### Detailed model performance on MTEB (eng, v1).
+![mteb_cmn](./imgs/mteb_eng.jpg)
+
+## Requirements
+Since we have used the Qwen2 model, we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
+```
+KeyError: 'qwen2'
+```
+
+## Usage
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer("{MODEL_NAME_OR_PATH}", trust_remote_code=True, model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "flash_attention_2"})
+model.max_seq_length = 512
+
+embeddings = model.encode(
+    sentences, 
+    normalize_embeddings=True,
+    batch_size=256, 
+    show_progress_bar=True
+    )
+print(embeddings)
+```
+
+We add task instructions for asymmetric tasks: retrieval, reranking, classification, and clustering.
+And, we add task instructions for both queries and passages in symmetric tasks, including STS and pair classification.
+If you want to add task instructions to the query, you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer("{MODEL_NAME_OR_PATH}", trust_remote_code=True, model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "flash_attention_2"})
+model.max_seq_length = 512
+
+prompt = "Instruct: Classifying the category of french news. \n Query: "
+embeddings = model.encode(
+    sentences, 
+    prompt=prompt,
+    normalize_embeddings=True,
+    batch_size=256, 
+    show_progress_bar=True
+    )
+print(embeddings)
+```
+
+
+## Citation
+If you find this model useful, please consider giving a star and citation.
+```
+@article{zhao2025kalmv2,
+  title={KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model},
+  author={},
+  journal={},
+  year={2025}
+}
+
+@article{hu2025kalm,
+  title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model},
+  author={Hu, Xinshuo and Shan, Zifei and Zhao, Xinping and Sun, Zetian and Liu, Zhenyu and Li, Dongfang and Ye, Shaolin and Wei, Xinyuan and Chen, Qian and Hu, Baotian and others},
+  journal={arXiv preprint arXiv:2501.01028},
+  year={2025}
+}
+```
+
+
+## Contact
+If you encounter any issue, feel free to contact us via the email: <zhaoxinping@stu.hit.edu.cn>, <yanshek.woo@gmail.com>