# High F1 score

This is an experimental Arabic topic classification model based on ModernBERT-base, demonstrating how to adapt ModernBERT for Arabic language tasks.

A few-shot text classification model trained with AutoTrain, suitable for news article classification tasks

Russian spelling correction model trained on FRED-T5-1.7B, capable of correcting spelling, punctuation, and capitalization errors

A speech processing model fine-tuned based on microsoft/wavlm-large, achieving 75% accuracy on the evaluation set

A cross-lingual model fine-tuned on German and French datasets based on XLM-RoBERTa-base, primarily used for named entity recognition tasks.

French token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base

Cross-lingual model fine-tuned on German and French datasets based on XLM-RoBERTa-base

German text classification model fine-tuned based on bert-base-german-cased, performs excellently on the evaluation set

Flair's built-in English fast phrase chunking model for identifying grammatical structures such as noun phrases and verb phrases in sentences.

A sentiment analysis model fine-tuned based on distilbert-base-uncased, trained on the tweet_eval dataset for tweet sentiment classification

A sentiment analysis model fine-tuned on distilbert-base-uncased, trained on the tweet_eval dataset for tweet sentiment classification

A Twitter sentiment analysis model fine-tuned on distilbert-base-uncased, trained on the tweet_eval dataset

A Portuguese question-answering model fine-tuned on the Portuguese SQUAD v1.1 dataset, based on BERTimbau base, suitable for Portuguese QA tasks.

A RoBERTa base model with domain-adaptive pretraining on movie corpora, fine-tuned for SQuAD QA tasks to answer movie-related questions.

A text sentiment classification model fine-tuned on distilbert-base-uncased, trained on the tweet_eval dataset

Text classification model fine-tuned on GLUE MRPC dataset based on bert-base-uncased

A hate speech detection model fine-tuned on distilbert-base-uncased, trained on the tweet_eval dataset

A French Q&A model based on CamemBERT, fine-tuned on three French Q&A datasets: PIAF, FQuAD, and SQuAD-FR

A deep learning-based text classification model that demonstrates high F1 score and ROC AUC values on the validation set.

A lightweight question-answering model based on MobileBERT, fine-tuned on the SQuAD v1.1 dataset, suitable for English Q&A tasks.

A text classification model fine-tuned on the GLUE dataset based on distilbert-base-uncased, used to determine whether sentence pairs are semantically equivalent

This model is a BERT-Base fine-tuned for QA tasks, using 80% 1x4 block-sparse pre-training combined with knowledge distillation.

Spanish Q&A model, fine-tuned from roberta-base-bne, pre-trained on 570GB of text data from the Spanish National Library