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mteb --- -

KaLM-Embedding

**KaLM-Embedding** is a series of embedding models adapted from auto-regressive LLMs with superior training data. KaLM-embedding-multilingual-mini is trained from [Qwen/Qwen2-0.5B](https://huggingface.co./Qwen/Qwen2-0.5B) with massive weakly-supervised pre-training and supervised fine-tuning data. ## 📑 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) - [ ] KaLM-embedding-multilingual-max-v1 - [ ] Technical Report - [ ] Training and Evaluation Code - [ ] Training Data ## Evaluation | Model Name | Model Size | C-MTEB(35) | MTEB(56) | avg |:----:|:---:|:---:|:---:|:---:| | [multilingual-e5-large](https://huggingface.co./intfloat/multilingual-e5-large) | 560M | 58.81 | 61.5 | 60.16 | [bge-m3 (dense)](https://huggingface.co./BAAI/bge-m3) | 560M | 60.80 | 59.84 | 60.32 | [gte-multilingual-base (dense)](https://huggingface.co./Alibaba-NLP/gte-multilingual-base) | **305M** | 62.72 | 61.40 | 62.06 | [KaLM-embedding-multilingual-mini-v1](https://huggingface.co./HIT-TMG/KaLM-embedding-multilingual-mini-v1) | 494M | 62.31 | 61.87 | 62.09 | [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co./HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1) | 494M | **63.57** | **64.74** | **64.16** ## 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}') # Do NOT set trust_remote_code model.max_seq_length = 512 embeddings = model.encode( sentences, normalize_embeddings=True, batch_size=256, show_progress_bar=True ) print(embeddings) ``` We add instruction for asymmetric tasks: retrieval, reranking, classification and clustering. If you want to add instruction to the query (no instruction for the corpus), 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}') # Do NOT set trust_remote_code 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) ``` ## Contact If you encounter any issue, feel free to contact us via the email: yanshek.woo@gmail.com