| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: samchain/econo-sentence-v2 |
| tags: |
| - generated_from_trainer |
| - economics |
| - finance |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: EconoDetect |
| results: [] |
| datasets: |
| - samchain/economics-relevance |
| 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. --> |
|
|
| # EconoDetect |
|
|
| This model is a fine-tuned version of [samchain/econo-sentence-v2](https://huggingface.co/samchain/econo-sentence-v2) on the economics-relevance dataset. |
| The base model is kept frozen during training, only the classification head is updated. |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.3973 |
| - Accuracy: 0.8211 |
| - F1: 0.7991 |
| - Precision: 0.7895 |
| - Recall: 0.8211 |
|
|
| ## Model description |
|
|
| This model is designed to detect whether a text discusses topics related to the US economy. |
|
|
| ## Intended uses & limitations |
|
|
| The model can be used as a screening tool to remove texts that are not discussing US economy. |
|
|
| ## Training and evaluation data |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0005 |
| - 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: 3 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.5381 | 1.0 | 700 | 0.4333 | 0.7844 | 0.7894 | 0.7952 | 0.7844 | |
| | 0.4613 | 2.0 | 1400 | 0.4044 | 0.8328 | 0.7679 | 0.7856 | 0.8328 | |
| | 0.3523 | 3.0 | 2100 | 0.3973 | 0.8211 | 0.7991 | 0.7895 | 0.8211 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.50.0 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 3.4.1 |
| - Tokenizers 0.21.1 |
