从开源角度来说,huggingface的transformers会更好,因为contributors更多,社区更活跃,所以算是入坑了😓 Text-Classification 代码传送门:bert4pl Text-Classification的算法实现比较简单,首先经过bert的encoder之后取output第一维度的值也就是[CLS]的向量,[CLS]代表着这句话的句向量,然后接一个dropout层和一个全...
在https://huggingface.co/spaces/mteb/leaderboard上可以看到,acge模型已经在目前业界最全面、最权威的中文语义向量评测基准C-MTEB(Chinese Massive Text Embedding Benchmark)的榜单中获得了第一名的成绩。 由上表可以看到,acge_text_embedding模型在“Classification Average (9 datasets)”这一列中,acge_text_embeddi...
HuggingFace already did most of the work for us and added a classification layer to the GPT2 model. In creating the model I usedGPT2ForSequenceClassification. Since we have a custom padding token we need to initialize it for the model usingmodel.config.pad_token_id. Finally we will need t...
参数化为 之后,所有损失分别按各自的重要性 进行适当缩放后,做最终聚合MRL optimizes the multi-class classification loss for each of the nested dimension m ∈M using standard empirical risk minimization using a separatelinear classifier, parameterized by W(m) ∈RL×m ....
准备数据阶段主要需要用到的是datasets.Dataset 和transformers.AutoTokenizer。1，数据加载 HuggingFace的...
tasks like text classification, sentiment analysis, domain/intent detection for dialogue systems, etc. The model takes a text input and predicts a label/class for the whole sequence. Megatron-LM and most of the BERT-based encoders supported by HuggingFace including BERT, RoBERTa, and DistilBERT....
Repository files navigation README MIT license Huggingface text classification TorchServe Architecture Diagram Working DemoAbout No description, website, or topics provided. Resources Readme License MIT license Activity Stars 0 stars Watchers 1 watching Forks 0 forks Report repository Releases ...
CPUghcr.io/huggingface/text-embeddings-inference:cpu-1.5 VoltaNOT SUPPORTED Turing (T4, RTX 2000 series, ...)ghcr.io/huggingface/text-embeddings-inference:turing-1.5 (experimental) Ampere 80 (A100, A30)ghcr.io/huggingface/text-embeddings-inference:1.5 ...
兼容huggingface/transformers 文本二分类,多分类,多标签分类 多GPU并行 目录结构 .├── base │ ├── base_dataset.py │ ├── base_model.py │ ├── base_trainer.py │ ├── __init__.py ├── configs │ ├── binary_classification │ │ ├── active_learning_word_embedding_tex...
4. Dataset Gathering and Processing TheDBPedia Topic Classification datasetconsists of 342K+ Wikipedia page abstracts. Each abstract is assigned a class from 3 different levels of hierarchical categories with 9, 71 and 219 classes respectively, and the names of the columns for each level are l1,...