Hey,
I wonder if it is possible to use one of the flair model (for example provided over huggingface) in such a way that it generates keywords from a text that is passed to the data model. Preferably in German.
I tried ner-english-ontonotes-large which works very well on German text for specific entities.
But what I need is model, that returns not specific entities, like the name of the person or the name of the city. I need a model, that returns a couple of keywords, that describes the context of the input text, like input: "I like to have pizza for breakfast."output: "breakfast", "pizza", "favorite meal"` ..which should also work for longer texts.
There is no model in the flair-oniverse, that fits exactly that usecase, right?
But is it possible, to train or fine-tune a flair-model for that? A have a huge set of data, that provides a text and related keywords, which would be ideal for finetuning a model. Is there a way (and a tutorial), how to feed the trainig-data to make the model fit for the usecase?
Question
Hey, I wonder if it is possible to use one of the flair model (for example provided over huggingface) in such a way that it generates keywords from a text that is passed to the data model. Preferably in German.
I tried
ner-english-ontonotes-largewhich works very well on German text for specific entities.But what I need is model, that returns not specific entities, like the name of the person or the name of the city. I need a model, that returns a couple of keywords, that describes the context of the input text, like
input: "I like to have pizza for breakfast."output: "breakfast", "pizza", "favorite meal"` ..which should also work for longer texts.There is no model in the flair-oniverse, that fits exactly that usecase, right?
But is it possible, to train or fine-tune a flair-model for that? A have a huge set of data, that provides a text and related keywords, which would be ideal for finetuning a model. Is there a way (and a tutorial), how to feed the trainig-data to make the model fit for the usecase?