samchain/EconoSentiment

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EconoSentiment

This model is a fine-tuned version of samchain/econo-sentence-v2 on the Financial Phrase Bank dataset from FinanceMTEB. The full model is trained using a small learning rate isntead of freezing the encoder. Hence, you should not use the encoder of this model for a task other than sentiment analysis.

It achieves the following results on the evaluation set:

• —Loss: 0.1293
• —Accuracy: 0.962
• —F1: 0.9619
• —Precision: 0.9619
• —Recall: 0.962

Model description

The base model is a sentence-transformers model built from EconoBert.

Intended uses & limitations

This model is trained to provide a useful tool for sentiment analysis in finance.

Training and evaluation data

The dataset is directly downloaded from the huggingface repo of the FinanceMTEB. The preprocessing consisted of tokenizing to a fixed sequence length of 512 tokens.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 3e-05
• —trainbatchsize: 8
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 100
• —num_epochs: 2

Training results

Framework versions

• —Transformers 4.50.0
• —Pytorch 2.1.0+cu118
• —Datasets 3.4.1
• —Tokenizers 0.21.1