---
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
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# 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