Thanks for reaching out to us, since you already have a detailed scenario, I am happy to enable you a support ticket for evaluating the best solution for your project.
To answer your question generally, training a custom neural extraction model on a specific language, such as Czech, would ideally require a training dataset primarily composed of documents in that language. The model needs to understand the semantics and structure of the Czech language to accurately extract the required information. If your primary target is Czech invoices, then the majority of your training data should be in Czech.
However, incorporating a mix of languages in your dataset can potentially increase the model's robustness, especially if you expect to process invoices in multiple languages. The key is to ensure that the training data represents the distribution of languages you expect in your actual data. For example, if 70% of your invoices are in Czech, 20% are in English, and 10% are in German, your training data should ideally reflect this distribution. Remember, Neural Network models learn from the data they are provided with. Therefore, the more representative the training data is of the actual data the model will encounter, the better the model is likely to perform.
Please be aware that it's also important to note that training a multilingual model can be more challenging and may require additional steps, such as language detection and handling different date formats, etc.
Secondly, the composition of your training data should ideally reflect the real-world distribution of languages you expect your model to process. If your invoices are primarily in Czech and English, then these two languages should constitute the bulk of your training data.
Adding more languages to the mix could potentially improve the model's generalization capability, but only if you expect to process invoices in those languages in the real-world application. Training the model on languages that it won't encounter can increase complexity without providing tangible benefits and could even negatively impact the model's performance on your target languages.
Then, overfitting is a common concern when training neural networks. It occurs when a model learns to perform very well on its training data but struggles to generalize to unseen data. In other words, an overfitted model has learned the training data too well, to the point of memorizing it, including its noise and outliers, rather than learning the underlying patterns. Whether a model will overfit or not depends on several factors:
- Dataset Size: Smaller datasets are more prone to overfitting since the model can easily memorize them. Larger datasets typically provide more variability and help the model generalize better.
- Model Complexity: More complex models (those with more parameters) are more likely to overfit than simpler ones, especially when the amount of training data is limited.
- Training Duration: Overfitting can occur if the model is trained for too many epochs. After a certain point, the model starts to fit the noise in the training data rather than the signal.
- Diversity of Data: If your data is not diverse and representative of the real-world situations the model will encounter, the model will likely overfit to the specific cases it has seen in training. The approach of "more of the same" can potentially lead to overfitting if the additional data is not adding new information or variability. For example, simply duplicating existing training examples won't help the model generalize better.
To prevent overfitting, you can:
- Use a validation set to monitor the model's performance during training. If the performance on the validation set starts to degrade while the training performance continues improving, it's a sign of overfitting.
- Implement regularization techniques such as dropout, weight decay, or early stopping.
- Collect more diverse and representative data.
- Reduce the complexity of your model if you have a small dataset.
- Use techniques like data augmentation to artificially increase the size and diversity of your dataset.Overfitting is a common concern when training neural networks. It occurs when a model learns to perform very well on its training data but struggles to generalize to unseen data. In other words, an overfitted model has learned the training data too well, to the point of memorizing it, including its noise and outliers, rather than learning the underlying patterns. Whether a model will overfit or not depends on several factors:
- Dataset Size: Smaller datasets are more prone to overfitting since the model can easily memorize them. Larger datasets typically provide more variability and help the model generalize better.
- Model Complexity: More complex models (those with more parameters) are more likely to overfit than simpler ones, especially when the amount of training data is limited.
- Training Duration: Overfitting can occur if the model is trained for too many epochs. After a certain point, the model starts to fit the noise in the training data rather than the signal.
- Diversity of Data: If your data is not diverse and representative of the real-world situations the model will encounter, the model will likely overfit to the specific cases it has seen in training.
The approach of "more of the same" can potentially lead to overfitting if the additional data is not adding new information or variability. For example, simply duplicating existing training examples won't help the model generalize better. To prevent overfitting, you can:
- Use a validation set to monitor the model's performance during training. If the performance on the validation set starts to degrade while the training performance continues improving, it's a sign of overfitting.
- Implement regularization techniques such as dropout, weight decay, or early stopping.
- Collect more diverse and representative data.
- Reduce the complexity of your model if you have a small dataset.
- Use techniques like data augmentation to artificially increase the size and diversity of your dataset.
Please let us know if you need more details, also, we are happy to enable you a free ticket to discuss more with support engineer.
I hope this helps!
Regards,
Yutong
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