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Would it be possible to retrieve the similarity score between keywords? Once a text is assigned to keywords, would there be a function to retrieve a value on how the keywords are similar to each other…
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I tired to aug the positive examples using back-translation with different prompt (the prompt used in unsupervised roberta) . Specifically, I got two views of the positive examples using back-translat…
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I have a question, is it possible to train model on NLI task with ktrain?
or just need to combine 2 sequence and add cls and sep token (BERT) and train like usually...
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Is it possible to get a representation of the topics using advanced models of OpenAI ,Cohere etc?
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If we want to fine tuning the model using our internal data, is it feasible?
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---> 15 z_intents = unknown_intents_set("en_core_web_trf", z_dataset['test']['text'])
16 berta = model.instantiate_model(z_banking.labels(), z_banking.output_dim(), device, model_name,
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if we have a German Umlaut in the attachments filename, the letterparser transforms the attachment like this:
=?iso-8859-1?Q?Pr=FCfbericht.pdf?=
it was a PDF File with the name Prüfbericht.pdf
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I have the following environment.
Python 3.8
txtai
Jupyter notebook
index backend is default FAISS
The basic example given was tried. Everything works except the step where I want to save the i…
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copying fairseq\examples\wav2vec\unsupervised\kaldi_self_train\st\local\prepare_lm.sh -> build\lib.win-amd64-cpython-310\fairseq\examples\wav2vec\unsupervised\kaldi_self_train\st\local
cop…