Hello TensorFlow Community,
I hope this message finds you well. I am currently working on a project that involves developing a text understanding model for entity extraction using TensorFlow. The goal is to extract specific entities such as team names and scores from textual data.
I’ve made progress in experimenting with transformer-based models, but I’m facing challenges in fine-tuning the model to accurately predict entities it hasn’t seen during training. The desired outcome is a model capable of intelligently predicting and extracting relevant information, even if the specific entity names are novel.
I would greatly appreciate your insights and recommendations on suitable models or algorithms that could be effective for this task. Additionally, any guidance on best practices for fine-tuning and handling novel entities would be immensely helpful.
Here are some specific questions I have:
- Are there specific pre-trained models within the TensorFlow ecosystem that are well-suited for entity extraction and text understanding?
- How can I enhance the model’s ability to predict and extract entities it hasn’t encountered during training?
- Are there recommended approaches or best practices for handling novel entity names in the context of transformer-based models?
Thank you in advance for your time and assistance. I am eager to learn from the collective expertise of the TensorFlow community and look forward to any insights you can provide.