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Category/text-classification
Text classification AI models are an essential tool for anyone looking to analyze and categorize large volumes of text data. These models can be used for a variety of applications, including sentiment analysis, spam filtering, topic modeling, and more.
To train a text classification AI model, you will need labeled data, meaning that the text has already been categorized. The model learns from this data and then applies what it has learned to classify new, unseen text. The accuracy of the model depends on a variety of factors, including the quality and quantity of the training data, the complexity of the text, and the choice of model.
To ensure that your text classification AI model remains accurate and effective, it's important to continually evaluate and improve the model over time. This may involve adding new labeled data, tweaking the model's hyperparameters, or trying out different algorithms.
text-classification
FinBERT is a pre-trained NLP model for financial sentiment analysis, built by fine-tuning the BERT language model on a large financial corpus. The model provides softmax outputs for three labels: positive, negative, or neutral.
text-classification
A model trained on approximately 58 million tweets and fine-tuned for emotion recognition using the TweetEval benchmark. The model achieves high accuracy on various emotion classification tasks, including emojii, emotion, hate, irony, offensive, sentiment, stance/abortion, stance/atheism, stance/climate, stance/feminist, and stance/hillary.
text-classification
A RoBERTa-base trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English.
text-classification
DistilBERT-base-uncased-finetuned-sst-2-english achieved an accuracy of 0.91 on the glue dataset, with a loss of 0.39 and an F1 score of 0.91. The model was trained on the sst2 dataset with a configuration of default and a split of train.
