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Add text2text example (e.g., text summarisation) #419
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I'd like to work on this, however, it seems the URL provided is outdated/broken. |
Hi @iakhil ! You are right! I think the best for this issue is to add a text summarization example here: https://rubrix.readthedocs.io/en/stable/guides/task_examples.html#Text2Text-(Experimental) The idea is use text summarization pretrained pipeline from Huggingface (https://huggingface.co/transformers/main_classes/pipelines.html#summarizationpipeline) and add the example in this notebook (after the machine translation one): https://github.com/recognai/rubrix/blob/master/docs/guides/task_examples.ipynb For example: summarizer = pipeline("summarization")
# return three predictions
predictions = summarizer("A LONG TEXT....", num_return_sequences=3)
# log three predictions in rubrix
# follow the machine translation example |
Hi @iakhil , did you manage to take a look at this? Let us know if you need support |
Hey! In order to log predictions in Rubrix, the Docker-contained web app should be running, right? When I try to run it using the first method, I get the following error:
How should I go about it? |
Hi @iakhil There are some troubles with SSL certificates downloading the docker-compose.yml file. You can just copy the file content and create it locally. Then launch the I hope this helps you |
Thanks! I got it running. Are we supposed to have three predictions (as denote by the value of |
Hi @iakhil, sorry for the late reply. In principle you should be able to view the 3 predictions using the arrows on both the annotation and explore mode. Would you mind to share a screenshot with the records for which you don't see the predictions? |
Earlier, I had connected to the server locally using |
This issue is stale because it has been open for 30 days with no activity. |
This issue was closed because it has been inactive for 14 days since being marked as stale. |
Hey! Can I pick this issue under Hacktoberfest ? if its still pending? |
Absolutely! It's still pending |
Add the text summarisation fine-tuning tutorial similar to sentiment classifier fine-tuning tutorial:
https://rubrix.readthedocs.io/en/stable/tutorials/06-labeling-finetuning.html#3.-Fine-tune-the-pre-trained-mode
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