SentimentAnalysis | 基于深度学习的情感分析模型 , 经过外卖评论和酒店评论的训练，准确率在90 % 左右

I believe your goal as follows.

• You want to put the values of magnitude and score to the vertical direction.
• You are using the function of SentimentAnalysis as the custom function.
• magnitude and score are the correct values you expect.

In this case, how about the following modification?

First of all. you are saying port redirections - which is more like port mapping in docker compose.

Secondly - attempt to hep you:

Assuming no magic in portfolio-network and since your client in the same network as both of your servers you should communicate to the through their names but not localhost. i.e.

The problem is that pipelines by default load an English model. In the case of sentiment analysis, this is distilbert-base-uncased-finetuned-sst-2-english, see here.

Fortunately, you can just specify the exact model that you want to load, as described in the docs for pipeline:

The timeout is why it’s happening repeatedly. If the lambda times out or otherwise errs it will cause the batch to be reprocessed. You need to handle this because the delivery is “at least once”. You also need to figure out the cause of the timeout. It might be as simple as smaller batches, or a more complex solution using step functions. You might just be able to increase the timeout on the lambda.

You might use re module for that following way:

The issue is that your {recognizerResult} is what gets saved to const recognizerResult, but is not what gets returned by that API call.

It takes a lot of digging to find it all, but a V2 LUIS client gets the API response, then converts it into recognizerResult.

You've got a few options for "fixing" this:

1. Set a breakpoint in that node_modules\botbuilder-ai\src\luisRecognizerOptionsV2 file on that const result = line and grab luisResult.
2. Use something like Fiddler to record the actual API response and use that
3. Write it manually

For reference, you can see how we do this in our tests:

• nock()
• Recorded response

You can see that our nock() returns response.v2, which does not contain .topScoringIntent, which is what it's looking for, which is why the error is throwing.

Specifically, the mock response needs to be just the v2/luisResults attributes. In other words, when using the luisRecognizer, the response set in nock needs to be

.reply(200,{ "query": "Sample query", "topScoringIntent": { "intent": "desiredIntent", "score":1}, "entities":[]});

If you look at the test data linked above, there are other attributes in the actual response. But this is the minimum required response if you are just trying to get topIntent to test routing. If you needed other attributes you could add them, e.g. you could add everything within v2 as in this file or some of the more involved files with things like multiple intents.

I was finally able to figure it out, not the neatest conversion but it works. If anyone is interested in the full code see below.

I just came across this issue. I'm assuming you have solved it by now, but just for the public forum...

According to one of the contributors (https://github.com/aws/aws-sdk-js/issues/2417#issuecomment-446001911) Comprehend and Comprehend Medical aren't exported in the primary sdk bundle. You have to import it directly like so:

The debugger is showing only a preview of the data - the first 10 rows. The goal here is to show a few example rows and how each transform is operating on them to make debugging easier.

Reading in the entire training data and running all the transformations on it is expensive and only happens when you reach .Train(). As the transformations are only operating on a few rows, their effect might be different when operating on the entire dataset (e.g. the text dictionary will likely be bigger), but hopefully the preview of data shown before running through the full training process is helpful for debugging and making sure transforms are applied to the correct columns.

If you have any ideas on how to make this clearer or more useful, it would be great if you can create an issue on GitHub!