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Models by this creator
The dipterv-finbert model is a text classification model trained on financial news articles. It is designed to predict the sentiment (positive, negative, or neutral) of financial news based on the text. The model can help investors and financial professionals in making investment decisions by providing insights into market sentiment.
dipterv6 This model is a fine-tuned version of ahmedrachid/FinancialBERT-Sentiment-Analysis on the None dataset. It achieves the following results on the evaluation set: Loss: 0.0300 Accuracy: 0.9907 F1: 0.9907 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning_rate: 2e-05 train_batch_size: 16 eval_batch_size: 16 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 5 Training results Framework versions Transformers 4.28.1 Pytorch 2.0.0+cu118 Datasets 2.11.0 Tokenizers 0.13.3