Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:39122
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use hreyulog/embedinggemma_arkts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hreyulog/embedinggemma_arkts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hreyulog/embedinggemma_arkts") sentences = [ "组件即将出现时加载收藏商家数据", "static async delete(key: string, preferenceName: string = defaultPreferenceName) {\n let preferences = await this.getPreferences(preferenceName)\n return await preferences.delete(key)\n }", "async aboutToAppear(): Promise<void> {\n await this.loadFavoriteMerchants();\n }", "Copyright (c) 2022 Huawei Device Co., Ltd.\nLicensed under the Apache License,Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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