finetuning-twitter-sentiment-distilbert

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finetuning-twitter-sentiment-distilbert

This model is a fine-tuned version of distilbert-base-uncased on the zeroshot/twitter-financial-news-sentiment dataset. It achieves the following results on the evaluation set:

• Loss: 0.6031
• Accuracy: 0.8591
• F1: 0.8578

Model description

Finetuned Distilbert on Twitter Finance Sentiment Dataset (zeroshot/twitter-financial-news-sentiment)

Intended uses & limitations

Predict sentiment in financial reporting

Training and evaluation data

Training procedure

Trained on colab using a custom notebook and dataset modifications (multi-class and f1 score adjusted)

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2