--- model-index: - name: ternary-weight-embedding results: - dataset: config: default name: MTEB AFQMC (default) revision: b44c3b011063adb25877c13823db83bb193913c4 split: validation type: C-MTEB/AFQMC metrics: - type: cosine_pearson value: 54.178784977880845 - type: cosine_spearman value: 58.24983603546464 - type: euclidean_pearson value: 56.12063302915049 - type: euclidean_spearman value: 57.55891180444177 - type: main_score value: 58.24983603546464 - type: manhattan_pearson value: 56.14231021933658 - type: manhattan_spearman value: 57.57073312857721 task: type: STS - dataset: config: default name: MTEB ATEC (default) revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 split: test type: C-MTEB/ATEC metrics: - type: cosine_pearson value: 54.15496422617569 - type: cosine_spearman value: 57.14812321292837 - type: euclidean_pearson value: 60.12883177451991 - type: euclidean_spearman value: 56.79823039497166 - type: main_score value: 57.14812321292837 - type: manhattan_pearson value: 60.12467197534511 - type: manhattan_spearman value: 56.79279245167105 task: type: STS - dataset: config: default name: MTEB ATEC (default) revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 split: validation type: C-MTEB/ATEC metrics: - type: cosine_pearson value: 54.24370907266313 - type: cosine_spearman value: 57.79884664537865 - type: euclidean_pearson value: 60.05093414384327 - type: euclidean_spearman value: 57.47352051181076 - type: main_score value: 57.79884664537865 - type: manhattan_pearson value: 60.03579846842669 - type: manhattan_spearman value: 57.46213161281322 task: type: STS - dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 37.554 - type: f1 value: 37.076043043891794 - type: f1_weighted value: 37.076043043891794 - type: main_score value: 37.554 task: type: Classification - dataset: config: de name: MTEB AmazonReviewsClassification (de) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 26.883999999999997 - type: f1 value: 26.766904450030015 - type: f1_weighted value: 26.766904450030015 - type: main_score value: 26.883999999999997 task: type: Classification - dataset: config: es name: MTEB AmazonReviewsClassification (es) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 29.028 - type: f1 value: 28.780140874309517 - type: f1_weighted value: 28.780140874309517 - type: main_score value: 29.028 task: type: Classification - dataset: config: fr name: MTEB AmazonReviewsClassification (fr) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 26.72 - type: f1 value: 26.480495303330265 - type: f1_weighted value: 26.480495303330265 - type: main_score value: 26.72 task: type: Classification - dataset: config: ja name: MTEB AmazonReviewsClassification (ja) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 28.518 - type: f1 value: 28.438382784401405 - type: f1_weighted value: 28.438382784401405 - type: main_score value: 28.518 task: type: Classification - dataset: config: zh name: MTEB AmazonReviewsClassification (zh) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 46.26200000000001 - type: f1 value: 45.27125595644993 - type: f1_weighted value: 45.271255956449934 - type: main_score value: 46.26200000000001 task: type: Classification - dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: validation type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 37.258 - type: f1 value: 36.810536650483364 - type: f1_weighted value: 36.810536650483364 - type: main_score value: 37.258 task: type: Classification - dataset: config: de name: MTEB AmazonReviewsClassification (de) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: validation type: mteb/amazon_reviews_multi metrics: - 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type: accuracy value: 86.19 - type: ap value: 69.16016629543033 - type: ap_weighted value: 69.16016629543033 - type: f1 value: 84.65946646369608 - type: f1_weighted value: 86.35113290203732 - type: main_score value: 86.19 task: type: Classification tags: - mteb --- # ternary-weight-embedding 基于xiaobu-embedding-v2[1]，在nli-zh[2]和t2ranking[3]数据集文本上微调得到的三元权重text embedding模型。模型中所有Linear层的权重取值为1，0或-1。模型中所有Linear层的权重取值为1，0或-1。推理时间和存储空间可以达到全精度模型的0.37x（在A800上的测试结果）的和0.13x。 使用请安装BITBLAS，支持的GPU见[4] ``` pip install bitblas ``` 初次运行可能会花一些时间 使用Sentence-Transformers进行测试 ``` pip install -U sentence-transformers ``` ``` import mteb from sentence_transformers import SentenceTransformer model = SentenceTransformer('malenia1/ternary-weight-embedding',trust_remote_code=True) print(model) tasks = mteb.get_tasks("OnlineShopping") evaluation = mteb.MTEB(tasks=tasks) results = evaluation.run(model, output_folder=f"results") ``` ## Reference 1. https://huggingface.co/lier007/xiaobu-embedding-v2 2. https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/sampled_data 3. https://huggingface.co/datasets/sentence-transformers/t2ranking/tree/main/triplet 4. https://github.com/microsoft/BitBLAS