Commits · 158c0b8cead806f718df8b98d618ef336647794b · 万朱浩 / Venue-Ops

10 Dec, 2024 1 commit

1,中间件代码逻辑可以优化，以减少重复的 return 语句，并提高可读性
2,为了更好地调试和监控，建议为应用添加日志记录，捕获用户请求和错误

authored 2024-12-10 20:52:22 +0800

18 Oct, 2024 10 commits
- app final update · e41334cd
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  juanboy authored 2024-10-18 22:22:49 +0800
- app rebuilt · 96af98f9
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  juanboy authored 2024-10-18 22:20:04 +0800
- predict.demo built · a4fba83c
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  juanboy authored 2024-10-18 22:15:21 +0800
- front end app update · 848da77e
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  juanboy authored 2024-10-18 22:09:37 +0800
- failure.html page built · 975890d6
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  juanboy authored 2024-10-18 22:05:44 +0800
- waiting.html page built · 24931366
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  juanboy authored 2024-10-18 22:02:52 +0800
- success.html · 9bfc6f36
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  juanboy authored 2024-10-18 22:01:09 +0800
- update app.py · 1417936c
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  juanboy authored 2024-10-18 16:08:11 +0800
- preliminary realize main.html · 440e3c3b
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  juanboy authored 2024-10-18 16:03:40 +0800
- front end built · 70b54530
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  juanboy authored 2024-10-18 16:00:16 +0800
16 Oct, 2024 2 commits
- Upload of the final complete model data · fd815a2d
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  戒酒的李白 authored 2024-10-16 10:40:19 +0800
- The integration process and a complete use example are given · f8b13ec7
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  戒酒的李白 authored 2024-10-16 09:46:37 +0800
15 Oct, 2024 1 commit
- The final classification layer is complete · af5e2265
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  戒酒的李白 authored 2024-10-15 08:13:08 +0800
13 Oct, 2024 1 commit
- The multi-head attention mechanism is basically completed. · 3efea929
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  戒酒的李白 authored 2024-10-13 10:04:18 +0800
07 Oct, 2024 1 commit
- Calculates the scaling dot product attention · 9af61e2a
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  戒酒的李白 authored 2024-10-07 09:51:29 +0800
06 Oct, 2024 2 commits
- Divide the input into long heads · 4500b271
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  戒酒的李白 authored 2024-10-06 11:54:32 +0800
- Define the linear transformation layer · f5e307d3
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  戒酒的李白 authored 2024-10-06 11:34:31 +0800
04 Oct, 2024 4 commits
- Multi-head attention mechanism infrastructure and input dimension settings. · ee739c3c
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  戒酒的李白 authored 2024-10-05 00:49:24 +0800
- A small change. · ba192296
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  戒酒的李白 authored 2024-10-04 23:16:45 +0800
- BCAT is basically completed. · 5adabea0
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  戒酒的李白 authored 2024-10-04 23:15:44 +0800
- BCAT Preliminary · b49d16ab
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  戒酒的李白 authored 2024-10-04 22:18:54 +0800
02 Oct, 2024 3 commits
- Implement the get_bert_ctm_embeddings function and embedding generation and loading logic · 80aa0cfa
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  戒酒的李白 authored 2024-10-03 00:48:10 +0800
- Bert's method of processing sentences · 4d91f30d
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  戒酒的李白 authored 2024-10-02 23:36:00 +0800
- Upload model parameters of CTM module · 6cf336a7
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  戒酒的李白 authored 2024-10-02 23:30:30 +0800
01 Oct, 2024 3 commits
- The BERT_CTM module is finally completed · 88981ad5
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  戒酒的李白 authored 2024-10-01 21:22:26 +0800
- Merge branch 'main' of https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem · 34405d78
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  戒酒的李白 authored 2024-10-01 15:51:03 +0800
- Added inference function for the model · f2ebb565
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  戒酒的李白 authored 2024-10-01 15:50:57 +0800
30 Sep, 2024 5 commits
- 更新 index.html · 8e1635e8
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  suki-un authored 2024-09-30 23:43:03 +0800
- readme-cn · de9d51c8
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  suki-un authored 2024-09-30 23:39:04 +0800
- readme-cn · c41028a6
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  suki-un authored 2024-09-30 23:36:51 +0800
- Add BERT-CTM model with save/load and error handling · 91a192e3
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  戒酒的李白 authored 2024-09-30 13:38:56 +0800
- Integrated training function of CTM model · f0f43c8e
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  戒酒的李白 authored 2024-09-30 09:24:08 +0800
29 Sep, 2024 3 commits
- Implement Chinese word segmentation · 5108ae12
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  戒酒的李白 authored 2024-09-30 01:06:03 +0800
- Batch processing text embedding tests · 48af69da
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  戒酒的李白 authored 2024-09-30 00:38:42 +0800
- Test the BERT model for Chinese simulation embedding · 8c0479a9
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  戒酒的李白 authored 2024-09-30 00:14:40 +0800
26 Sep, 2024 3 commits
- Import the initial BERT pre-trained model · 701926ba
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  戒酒的李白 authored 2024-09-27 00:24:30 +0800
- Input the new dataset for sentiment recognition · ce80b15e
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  戒酒的李白 authored 2024-09-27 00:16:47 +0800
- Initialization of the New Sentiment Recognition Model · e83639b4
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  戒酒的李白 authored 2024-09-27 00:10:35 +0800
10 Sep, 2024 1 commit
- readme updata · 413c8894
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  戒酒的李白 authored 2024-09-10 10:20:25 +0800