Gy 1736

We are using prodigy to ner. I read ner.

Nineteen hens 13 dead, 6 culled had intussusceptions of the proventriculus into the ventriculus. Mean age of affected hens was wk range wk. None of the hens in the study had an intestinal intussusception, and none of the hens euthanized at the end of the study had a proventricular intussusception. Hens with proventricular intussusceptions were severely emaciated; mean body weights were and g for affected and cohort hens, respectively. Necropsy findings included prominent keel, marked muscle atrophy, generalized serous atrophy of fat, no visible proventriculus, esophagus directly entering the ventriculus, and an enlarged, spherical, firm ventriculus, which contained an invaginated, swollen, diffusely ulcerated proventriculus. Eighteen affected hens were anovulatory Severe, diffuse necrosis and ulceration of the proventricular mucosa was confirmed microscopically, but no etiologic agent was identified.

Gy 1736

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Is that a best practice for the future, to split the docs to smaller chunks when annotations start to pile gy 1736 Is there a way to tell prodigy to skip some examples or we can only do by passing documents that are not annotated. Interface is slow when running textcat recipe, gy 1736.

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Gy 1736

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Also, if you want to really optimize for processing performance, another option could be to do the pre-processing separately, e. Necropsy findings included prominent keel, marked muscle atrophy, generalized serous atrophy of fat, no visible proventriculus, esophagus directly entering the ventriculus, and an enlarged, spherical, firm ventriculus, which contained an invaginated, swollen, diffusely ulcerated proventriculus. Eighteen affected hens were anovulatory Hi Ines, Thanks for the quick response. Once you're done with file 1, you can start at file 2, and if you ever restart the server, it'll only have to go through the examples in that file again. It is worth saying that the documents are quite long but since we know that they contain the relevant entities, should it take that much time? If you're working with one huge JSONL file and a large number of examples is already annotated, this could potentially lead to startup taking longer over time, because each example is processed and then skipped. Prodigy pretty much always uses nlp. From looking at the logs, it seems like Prodigy spends the 40 minutes going through your source data trying to find the next batch to send out. But I think the absolute fastest solution would be the pre-processing approach I described above. We are using prodigy to ner. Is that a best practice for the future, to split the docs to smaller chunks when annotations start to pile up? We have docs in the JSON with a mean sentence length of and a std of so it varies a lot. Is there a way we can speed up the loading?

The digital motion processor can be used to process complex algorithms directly on the board.

You can run prodigy stats to find the location of your local installation and then just hack it into the ner. Also, if you want to really optimize for processing performance, another option could be to do the pre-processing separately, e. It is worth saying that the documents are quite long but since we know that they contain the relevant entities, should it take that much time? Once you're done with file 1, you can start at file 2, and if you ever restart the server, it'll only have to go through the examples in that file again. Let me know if you need more information on our environment, data or anything. From looking at the logs, it seems like Prodigy spends the 40 minutes going through your source data trying to find the next batch to send out. Prodigy is slow at loading annotations usage. Mean age of affected hens was wk range wk. Necropsy findings included prominent keel, marked muscle atrophy, generalized serous atrophy of fat, no visible proventriculus, esophagus directly entering the ventriculus, and an enlarged, spherical, firm ventriculus, which contained an invaginated, swollen, diffusely ulcerated proventriculus. Running nlp text to each document is slow as well but not that slow, it takes approx 2s per document We assume prodigy does not use pipe. In that case, you could split your file up into smaller portions, so if you've already gone through examples, you can start at example instead of at the beginning. The hackier version of this would be to just remove all lines from the top of your JSONL that you know are already annotated in the data. Las gallinas con intususcepciones proventriculares estaban severamente emaciadas; los pesos corporales medios fueron g y g para las gallinas afectadas y para las gallinas cohorte, respectivamente. None of the hens in the study had an intestinal intussusception, and none of the hens euthanized at the end of the study had a proventricular intussusception. La edad media de las gallinas afectadas fue de semanas con un rango de a semanas.