Distilroberta Finetuned Financial News Sentiment Analysis

🚀 DistilRoberta-financial-sentiment

This model is a fine - tuned version of distilroberta - base on the financial_phrasebank dataset, used for financial sentiment analysis with high accuracy.

🚀 Quick Start

This model is a fine - tuned version of [distilroberta - base](https://huggingface.co/distilroberta - base) on the financial_phrasebank dataset. It achieves the following results on the evaluation set:

• Loss: 0.1116
• Accuracy: 0.9823

✨ Features

• High Accuracy: Achieves an accuracy of 0.9823 on the financial_phrasebank dataset.
• Fast Inference: On average, DistilRoBERTa is twice as fast as Roberta - base.

📚 Documentation

Base Model description

This model is a distilled version of the [RoBERTa - base model](https://huggingface.co/roberta - base). It follows the same training procedure as [DistilBERT](https://huggingface.co/distilbert - base - uncased). 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

Framework versions

• Transformers 4.10.2
• Pytorch 1.9.0+cu102
• Datasets 1.12.1
• Tokenizers 0.10.3

📄 License

This model is licensed under the Apache - 2.0 license.