# combine\_models - the effect of batch\_size? **URL:** \ **Category:** Uncategorized\ **Tags:** usage, solved\ **Created:** [November 10, 2019, 1:34pm UTC](https://support.prodi.gy/t/combine-models-the-effect-of-batch-size/2205 "2019-11-10T13:34:44Z")\ **Posts on this page:** 1\ **Showing post:** 2
**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\ **Post date:** [November 11, 2019, 12:23pm UTC](https://support.prodi.gy/t/combine-models-the-effect-of-batch-size/2205/2 "2019-11-11T12:23:02Z")
Hi! There are two batch sizes here: first, the batch size used to partition the two generators and interleave them, and second, the batch size that Prodigy uses to divide the final stream into batches. The `batch_size` on `combine_models` is less relevant here and mostly used for efficiency. In the end, the `predict` function still just yields `(score, example)` tuples. The `batch_size` setting in Prodigy is what decides about how many examples are fetched from the stream at once, how many are sent to the web app and how many are sent back to the server. See my reply here for more ideas on how you could solve your problem: > [@prefer\_high\_scores(stream) not prioritising the bootstrapped texts during cold start](https://support.prodi.gy/t/prefer-high-scores-stream-not-prioritising-the-bootstrapped-texts-during-cold-start/2177/4): > > Yeah, I think what you're observing here is that there are no pattern matches in the stream, only some initial suggestions from the model. Because the sorters operate over a (potentially infinite) stream and only get to see batches, they use an exponential moving average to decide what to send out. The main objectives here are: make sure there's always something to annotate and make sure the stream never gets stuck because the scores are too high/low. One option would be to use a larger batch\_… --- _[View the full topic](https://support.prodi.gy/t/combine-models-the-effect-of-batch-size/2205)._