This paper describes approaches and results for shared Task 1 and 4 of
SMMH4-23 by Team Shayona. Shared Task-1 was binary classification of english
tweets self-reporting a COVID-19 diagnosis, and Shared Task-4 was Binary
classification of English Reddit posts self-reporting a social anxiety disorder
diagnosis. Our team has achieved the highest f1-score 0.94 in Task-1 among all
participants. We have leveraged the Transformer model (BERT) in combination
with the LightGBM model for both tasks.

我们在 SMMH4-23 的共享任务 1 和 4 中通过使用 Transformer 模型（BERT）与 LightGBM 模型结合取得了最高的 f1 分数为 0.94，对英文推文进行 COVID-19 诊断的二元分类以及对英文 Reddit 帖子进行社交焦虑症诊断的二元分类。

COVID-19 自诊断分类：BERT 和 LightGBM 模型

Shayona@SMM4H23: COVID-19 Self diagnosis classification using BERT and  LightGBM models

The aim of the CASE 2021 Shared Task 1 (H\"urriyeto\u{g}lu et al., 2021) was
to detect and classify socio-political and crisis event information at
document, sentence, cross-sentence, and token levels in a multilingual setting,
with each of these subtasks being evaluated separately in each test language.
Our submission contained entries in all of the subtasks, and the scores
obtained validated our research finding: That the multilingual aspect of the
tasks should be embraced, so that modeling and training regimes use the
multilingual nature of the tasks to their mutual benefit, rather than trying to
tackle the different languages separately. Our code is available at
this https URL

通过 CASE 2021 中的共同任务 1，在多语言环境下检测和分类社会政治和危机事件信息，证实了多语言任务的意义和模型训练方式。