DistilRoberta financial sentiment This model is a fine tuned version of distilroberta base on the financial phrasebank dataset. It achieves the following results on the evaluation set: Loss: 0.1116 Accuracy: 0.98 23 Base Model description This model is a distilled version of the RoBERTa base model. It follows the same training procedure as DistilBERT. The code for the distillation process can be found here. This model is case sensitive: it makes a difference between English and English. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa base). On average DistilRoBERTa is twice as fast as Roberta base. Training Data Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English language financial news categorised by sentiment. The dataset is divided by agreement rate of 5 8 annotators. Training procedure Training hyperparameters The following hyperparameters were used during training: learning rate: 2e 05 train batch size: 8 eval batch size: 8 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e 08 lr scheduler type: linear num epochs: 5 Training results Train…
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