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Although existing VLM exhibit high performance across various tasks, they are not trained on specific tasks such as visual inspection. Thus, we construct a dataset consisting of diverse images of non-defective and defective products collected from the web, along with unified formatted output text, and fine-tune VLM. For new products, our method employs In-Context Learning, which allows the model to perform inspections with an example of non-defective or defective image and the corresponding explanatory texts with visual prompts. This approach eliminates the need to collect a large number of training samples and re-train the model for each product. The experimental results show that our method achieves high performance, with MCC of 0.804 and F1-score of 0.950 on MVTec AD in a one-shot manner. 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Nowadays, online communication channels especially social media is becoming a way of expressing suicidal tendencies. However, the availability of properly tagged textual data is a major issue for researchers to perform identification or classification of such tendencies through texts. In light of the abovementioned facts, in the present work, we have proposed an approach to determine early detection with the goal of early diagnosis of suicidal behaviour through text posted on social media via supervised learning using few shot learning process. Therefore, to detect suicidal behaviour, we extract embeddings by finetuning a large pre-trained language model. The contextual embeddings have then been used for binary classification of the text for suicidal behaviour, in which a comparison of various classifiers has been performed. This includes traditional supervised classifiers and neural network models. A comparison of the model’s performance with and without an outlier detection and removal step has also been performed to highlight the importance of the outlier detection step in the pipeline. The feasibility and practicality of the approach have been demonstrated by generating results for user-generated content scraped from Reddit platform posts made on subreddits ‘SuicideWatch’ and ‘depression’. The results generated by the model are also seen to outperform various state-of-the-art models.</jats:p>", "DOI": "10.21203/rs.3.rs-2392230/v1", "type": "posted-content", "created": {"date-parts": [[2022, 12, 23]], "date-time": "2022-12-23T03:38:15Z", "timestamp": 1671766695000}, "source": "Crossref", "is-referenced-by-count": 3, "title": ["Few shot learning with fine-tuned language model for suicidal text detection"], "prefix": "10.21203", "author": [{"given": "Sandeep", "family": "Varma", "sequence": "first", "affiliation": [{"name": "ZS Associates"}], "role": [{"role": "author", "vocabulary": "crossref"}]}, {"given": "Shivam", "family": "Shivam", "sequence": "additional", "affiliation": [{"name": "ZS Associates"}], "role": [{"role": "author", "vocabulary": "crossref"}]}, {"given": "Biswarup", "family": "Ray", "sequence": "additional", "affiliation": [{"name": "ZS Associates"}], "role": [{"role": "author", "vocabulary": "crossref"}]}, {"given": "Ankita", "family": "Banerjee", "sequence": "additional", "affiliation": [{"name": "ZS Associates"}], "role": [{"role": "author", "vocabulary": "crossref"}]}], "member": "8761", "link": [{"URL": "https://www.researchsquare.com/article/rs-2392230/v1", "content-type": "text/html", "content-version": "vor", "intended-application": "text-mining"}, {"URL": "https://www.researchsquare.com/article/rs-2392230/v1.html", "content-type": "unspecified", "content-version": "vor", "intended-application": "similarity-checking"}], "deposited": {"date-parts": [[2023, 2, 4]], "date-time": "2023-02-04T11:14:36Z", "timestamp": 1675509276000}, "score": 36.654526, "resource": {"primary": {"URL": "https://www.researchsquare.com/article/rs-2392230/v1"}}, "issued": {"date-parts": [[2022, 12, 23]]}, "references-count": 0, "URL": "https://doi.org/10.21203/rs.3.rs-2392230/v1", "published": {"date-parts": [[2022, 12, 23]]}, "subtype": "preprint"}], "items-per-page": 6, "query": {"start-index": 0, "search-terms": null}}}