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README.md
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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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metrics:
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- accuracy
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- f1
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model-index:
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- name: EconoDetect
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results: []
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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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# 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
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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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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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
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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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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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# 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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## Model description
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This model is designed to detect whether a text discusses topics related to economics.
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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 related to economics.
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The dataset used for the training is strongly focused on US economy, hence a bias might occur as other regions are under represented.
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## Training and evaluation data
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## Training procedure
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### Training hyperparameters
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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
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