# Text classification optimization

GIST-Embedding-v0 is a sentence embedding model based on sentence-transformers, mainly used for sentence similarity calculation and feature extraction tasks.

NorBERT 3 is a next-generation Norwegian language model based on the BERT architecture, supporting both Bokmål and Nynorsk written Norwegian.

RoBERTa model fine-tuned on culinary-related datasets based on bert-base-uncased, suitable for NLP tasks such as text classification

FERNET-C5 is a Czech monolingual BERT base model pretrained on a 93GB large-scale cleaned Czech web corpus (C5).

A model fine-tuned based on bert-base-uncased for identifying hateful meme text content

A text classification model fine-tuned on the GLUE dataset based on distilbert-base-uncased, designed for sentence acceptability judgment tasks

A fine-tuned model based on DistilBERT for the grammatical acceptability classification task, performing excellently on the evaluation set.

A BERT variant trained on large-scale Tagalog text using whole-word masking, suitable for Filipino natural language processing tasks

This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset, primarily used for multilingual text processing tasks.

A lightweight text classification model based on DistilBERT, fine-tuned on the GLUE CoLA task for judging grammatical correctness of sentences

SinBERT is a Sinhala pre-trained language model based on the RoBERTa architecture, trained on a large Sinhala monolingual corpus (sin-cc-15M).

SinBERT is a model pretrained on a large Sinhala monolingual corpus (sin-cc-15M) based on the RoBERTa architecture, suitable for Sinhala text processing tasks.

SGPT-125M is a sentence transformer model optimized with weighted mean and bitfit techniques, focusing on sentence similarity tasks.