finbervis:用于(多标签)序列分类任务的微调BERT模型的可视化调试工具

时间:2024-05-01 13:51:53
【文件属性】:
文件名称:finbervis:用于(多标签)序列分类任务的微调BERT模型的可视化调试工具
文件大小:31.69MB
文件格式:ZIP
更新时间:2024-05-01 13:51:53
sentiment-analysis visual-analytics bert sequence-classification Python FinBerVis 使用手动标记的文本微调微调预训练的BERT变压器模型,以进行情感分析。 针对此模型或更普通的模型开发可视化调试工具: 可视化调试工具,可针对(多标签)序列分类任务微调BERT模型
【文件预览】:
finbervis-main
----logs()
--------training-23-11-2020.log(5KB)
--------training-24-11-2020.log(4KB)
--------training-08-04-2021.log(4KB)
----current_version_zoom.png(272KB)
----version_add.sh(665B)
----test.py(777B)
----app.py(5KB)
----src()
--------train_helper.py(3KB)
--------train.py(9KB)
--------config.py(3KB)
--------fix_embs.py(830B)
--------explore.py(3KB)
--------dist.py(2KB)
--------predict.py(6KB)
--------neighbourhood.py(950B)
--------add_props.py(1KB)
--------cluster.py(5KB)
--------prep_new_files.py(819B)
--------bert_preprocess.py(7KB)
--------evaluate.py(586B)
--------interface.py(14KB)
--------structure_analysis.py(3KB)
----model()
--------config.json(676B)
----current_version_unzoom.png(388KB)
----requirements.txt(113B)
----Screenshot_prototype_cut.png(239KB)
----plots()
--------training_loss_24-11-2020.png(71KB)
--------training_loss2.png(86KB)
--------cluster_embeddings_eps0.4.png(38KB)
--------most_frequent_3-grams_of_pos_tags_for_sentiment_values.png(45KB)
--------dependencies_for_sent_1-gram.png(37KB)
--------cluster_embeddings.png(47KB)
--------cluster_embeddings_eps1.3.png(31KB)
--------cluster_embeddings_eps0.6_3_clust.png(32KB)
--------most_frequent_3-grams_of_pos_tags.png(40KB)
--------cluster_embeddings_eps0.7.png(33KB)
--------cluster_embeddings_eps0.55.png(32KB)
--------training_loss.png(76KB)
----tsconfig.json(315B)
----README.md(299B)
----makefile(397B)
----data()
--------COLLECTED_LABELD_DATA.csv(18B)
--------str_embeddings_text_segments.csv(1.25MB)
--------text_segments_cleaned_processed.csv(1.03MB)
--------data.csv(61.07MB)
--------projection_with_full_sents_BAK.csv(1.57MB)
--------projection_with_full_sents_SENT_PROPS.csv(1.82MB)
--------embeddings_text_segments.csv(1.29MB)
--------hand_coded_text_segments.csv(3.06MB)
--------text_segments_cleaned.csv(1.09MB)
--------new_training_data.csv(1.3MB)
--------cluster_segments.csv(1.54MB)
--------projection_with_full_sents.csv(1.57MB)
----templates()
--------index.html(9KB)
--------includes()
--------layout.html(908B)
----.gitignore(68B)
----static()
--------js()
--------css()

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