finetuned-phi2-financial-sentiment-analysis

This model is a fine-tuned version of microsoft/phi-2 on the FinancialPhraseBank dataset. The FinancialPhraseBank dataset is a comprehensive collection that captures the sentiments of financial news headlines from the viewpoint of a retail investor. Comprising two key columns, namely "Sentiment" and "News Headline," the dataset effectively classifies sentiments as either negative, neutral, or positive. This structured dataset serves as a valuable resource for analyzing and understanding the complex dynamics of sentiment in the domain of financial news. It achieves the following results on the evaluation set:

• Loss: 1.4052

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: 0.0002
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.8067	1.0	112	1.5200
1.5055	2.0	225	1.4345
1.5221	3.0	337	1.4083
1.4956	3.98	448	1.4052

Framework versions

• PEFT 0.7.1
• Transformers 4.38.0.dev0
• Pytorch 2.0.0+cu117
• Datasets 2.16.1
• Tokenizers 0.15.0