项目描述： 1-训练一个基于新闻内容或标题进行文本分类的模型 2-训练样本1440条，验证集360条，做18分类，如家居、房产、财经，各类样本分布基本均衡 关键步骤： 1-同时使用多种模型的多种参数进行训练，目标筛选出最佳模型及参数 2-模型涵盖传统方法及神经网络，包括FastText，RNN、LSTM、GRU、CNN、Gated CNN、BERT、Bert+LSTM等。调整参数包括：学习率，hidden_size，batch_size，优化�
active 2023-11-07 → 2026-01-04 (UTC)
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Line chart, 790 days from 2023-11-07 to 2026-01-04. Pushes: 0 total, peak 0 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 4 total, peak 1 in a day.
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Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
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