metadata

library_name: transformers
license: apache-2.0
base_model: samchain/econo-sentence-v2
tags:
  - generated_from_trainer
  - finance
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: EconoSentiment
    results: []
datasets:
  - FinanceMTEB/financial_phrasebank
language:
  - en
pipeline_tag: text-classification

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
train_batch_size: 8
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.5992	1.0	158	0.4854	0.805	0.7692	0.8108	0.805
0.0985	2.0	316	0.1293	0.962	0.9619	0.9619	0.962

Framework versions

Transformers 4.50.0
Pytorch 2.1.0+cu118
Datasets 3.4.1
Tokenizers 0.21.1