Gy 1736

We are using prodigy to ner. I read ner. But given that we use a JSONL and that we have filtered it so that we gy 1736 keep the documents that contain entities, 40 minutes sound like a lot. It is worth saying that the documents are quite long but since we know that they contain the relevant entities, gy 1736, should it take that much time?

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.

Gy 1736

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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 gy 1736 data.

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Gy 1736

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Also in case it makes any difference we are running prodigy and the database inside a docker container. Are we doing anything wrong? Let me know if you need more information on our environment, data or anything. Severe, diffuse necrosis and ulceration of the proventricular mucosa was confirmed microscopically, but no etiologic agent was identified. Is there a way we can speed up the loading? 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. It batches the examples up in batches of 10, which should be pretty fast. Thanks for the suggestions, super helpful. In conclusion, proventricular intussusception of undetermined etiology was identified as a cause of sporadic emaciation, culling, and mortality in older laying hens. We have docs in the JSON with a mean sentence length of and a std of so it varies a lot. But I think the absolute fastest solution would be the pre-processing approach I described above.

The digital motion processor can be used to process complex algorithms directly on the board. Usually, the DMP processes algorithms that turn the raw values from the sensors into stable position data.

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? Abstract in English, Spanish. 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. Is that a best practice for the future, to split the docs to smaller chunks when annotations start to pile up? Nineteen hens 13 dead, 6 culled had intussusceptions of the proventriculus into the ventriculus. 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. You can run prodigy stats to find the location of your local installation and then just hack it into the ner. Prodigy pretty much always uses nlp. Managing long annotation sessions usage , streams. Severe, diffuse necrosis and ulceration of the proventricular mucosa was confirmed microscopically, but no etiologic agent was identified. 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. Publication types Case Reports. Also, if you want to really optimize for processing performance, another option could be to do the pre-processing separately, e. You can then use that with ner.