Trained model not working on new dataset

[ryanwesslen]

@WKamptner

To move the model over, we just ssh'd from the Datascience machine to the Analyst machine and recursively copied over specific folders. Is there any chance that, say, a hidden folder may contain important information?

Not that I'm aware.

In theory moving the full folder for the spaCy pipeline should work on any machine. What's weird though is if some file was missed when loading the model, you'd get more of an error than just Killed.

For example, this shows what happens if there's a problem with the meta.json file in a spaCy pipeline:

Issue with exported spacy models

Hi, Following the ‘First steps’, I created the model, and created the spacy package from it, then install the model and linked to custom name. However, when trying to load with spacy.load(), or use it as a basis for ner.make-gold, I receive following error message (it depends on the language model used for training): OSError: [E050] Can’t find model ‘en_core_web_lg.vectors’. It doesn’t seem to be a shortcut link, a Python package or a valid path to a data directory. However, after loading th…

Can you check if your "Datascientist" machine is running into any memory problems?

Prodigy NER train recipe getting killed for no apparent reason

Hello @koaning , I got to realize a couple of things: data-to-spacy managed to build the .spacy files required for training in spaCy. When training the model however, I faced up again that Killed message. Having the .spacy files already generated however, I decided to move to another cloud-based VM, with more computational resources in regards of memory this time... And the training completed successfully. That indirectly explains the root cause of my issue. Still, it would be awesome to h…

If not, on the "Datascientist" machine, let's forget Prodigy for a second and see if you can load/run a sample sentence:

import spacy
nlp = spacy.load("path/to/pipeline")
doc = nlp("This is a sample sentence")

# confirm you can run your model on the Datascientist machine
for entity in doc.ents:
    print(entity.text, entity.label_)

@WKamptner

The only other thing I can imagine is that the Datascience Machine has a dedicated GPU on it that was used for training, though I was told that the gpu was not necessary for the model they were using.

GPU could be another factor! Were they used to train the model and/or did you use spacy-transformers anywhere in your workflow?

GPU's can yield more accurate models but can also be trickier to handle. Here's a FAQ on GPU's in spaCy.

GPU Support Troubleshooting FAQ · explosion/spaCy · Discussion #11436