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This model is a fine-tuned version of roberta-base on this dataset sourced from Reddit. It achieves the following results on the evaluation/validation set: - Loss: 0.1543 - Accuracy: 0.9652972367116438 - Recall: 0.966571403827834 - Precision: 0.9638169257340242 - F1: 0.9651921995935487 It achieves the following result on validation partition of this updated dataset - Loss: 0.08761 - Accuracy: 0.97065 - Recall: 0.96652 - Precision: 0.97732 - F1: 0.97189 This model is a finetune of roberta-base to detect suicidal tendencies in a given text.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Code vs Natural language classification using bert-small from prajwall, below are the metrics achieved - Github repo for installable python package: https://github.com/Vishnunkumar - Space on the extraction of code blocks from screenshots: https://huggingface.co/spaces/vishnun/SnapCode

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This model is fine-tuned to detect offensive language in Greek texts (Twitter & Fiction). - Pretrained on `nlpaueb/bert-base-greek-uncased-v1` - Fine-tuned on a custom offensive language detection dataset, namely the AIKIA corpus. @inproceedings{markantonatou-etal-2024-corpus, title = {The Corpus {AIKIA}: Using Ranking Annotation for Offensive Language Detection in {M}odern {G}reek}, author = {Markantonatou, Stella and Stamou, Vivian and Christodoulou, Christina and Apostolopoulou, Georgia and Balas, Antonis and Ioannakis, George}, editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen}, booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, month = {may}, year = {2024}, address = {Torino, Italia}, publisher = {ELRA and ICCL}, url = {https://aclanthology.org/2024.lrec-main.1378}, pages = {15861--15871}

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