| --- |
| 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 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # EconoSentiment |
|
|
| This model is a fine-tuned version of [samchain/econo-sentence-v2](https://huggingface.co/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](https://huggingface.co/samchain/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 |
