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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Thanks for the suggestions, super helpful.

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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. In particular, it is shown how to retrieve the raw sensor values. If you plan to use the full range of features or require reliable and stable position data, then I recommend to have also a look at ready-to-use libraries. Next, we have to set up the I2C connection between the module and the Arduino.

Gy 1736

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Hens with proventricular intussusceptions were severely emaciated; mean body weights were and g for affected and cohort hens, respectively. Is that a best practice for the future, to split the docs to smaller chunks when annotations start to pile up? Thanks for the quick response. Dieciocho gallinas afectadas fueron anovulatorias Severe, diffuse necrosis and ulceration of the proventricular mucosa was confirmed microscopically, but no etiologic agent was identified. We have docs in the JSON with a mean sentence length of and a std of so it varies a lot. Also in case it makes any difference we are running prodigy and the database inside a docker container. 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. Are we doing anything wrong? Prodigy is slow at loading annotations usage. But given that we use a JSONL and that we have filtered it so that we only keep the documents that contain entities, 40 minutes sound like a lot. Thanks for the suggestions, super helpful.

Applications: Molecular works of ethanol precipitation, phenol extraction, NA preparation, cell collection, spin-down of temperature-sensitive reaction mixtures, etc. GZR-1 v, Hz.

You can run prodigy stats to find the location of your local installation and then just hack it into the ner. Publication types Case Reports. 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? But I think the absolute fastest solution would be the pre-processing approach I described above. You can then run that on a remote machine, potentially even with a GPU, parallelize it etc. Prodigy is slow at loading annotations usage. Managing long annotation sessions usage , streams. Prodigy pretty much always uses nlp. Let me know if you need more information on our environment, data or anything. 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. 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.