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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: samchain/econo-sentence-v2 |
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tags: |
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- generated_from_trainer |
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- economics |
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- finance |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: EconoDetect |
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results: [] |
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datasets: |
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- samchain/economics-relevance |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# EconoDetect |
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This model is a fine-tuned version of [samchain/econo-sentence-v2](https://huggingface.co/samchain/econo-sentence-v2) on the economics-relevance dataset. |
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The base model is kept frozen during training, only the classification head is updated. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3973 |
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- Accuracy: 0.8211 |
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- F1: 0.7991 |
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- Precision: 0.7895 |
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- Recall: 0.8211 |
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## Model description |
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This model is designed to detect whether a text discusses topics related to the US economy. |
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## Intended uses & limitations |
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The model can be used as a screening tool to remove texts that are not discussing US economy. |
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## Training and evaluation data |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.5381 | 1.0 | 700 | 0.4333 | 0.7844 | 0.7894 | 0.7952 | 0.7844 | |
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| 0.4613 | 2.0 | 1400 | 0.4044 | 0.8328 | 0.7679 | 0.7856 | 0.8328 | |
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| 0.3523 | 3.0 | 2100 | 0.3973 | 0.8211 | 0.7991 | 0.7895 | 0.8211 | |
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### Framework versions |
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- Transformers 4.50.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.1 |
