transformer | PyTorch Implementation of `` Attention Is All | Machine Learning library

Nobody uses this (invisible dependent quantification) for this purpose (where the dependency is not used) but it is the same as giving a Type -> .. parameter, implicitly.

They are the same data type to share as much code as possible between Reader and ReaderT. As it stands, only runReader, mapReader, and withReader have any special cases. And withReader doesn't have any unique code, it's just a type specialization, so only two functions actually do anything special for Reader as opposed to ReaderT.

You might look at the module exports and think that isn't buying much, but it actually is. There are a lot of instances defined for ReaderT that Reader automatically has as well, because it's the same type. So it's actually a fair bit less code to have only one underlying type for the two.

Given that, your question boils down to asking why Reader is implemented on top of ReaderT, and not the other way around. And for that, well, it's just the only way that works.

Let's try to go the other direction and see what goes wrong.

Typescript types only exist at compile time. They do not exist in the compiled javascript. Thus you cannot populate an array (a runtime entity) with compile-time data (such as the RequestObject type alias), unless you do something complicated like the library you found.

1. code something yourself that works like the library you found.
2. find a different library that works with type aliases such as RequestObject.
3. create an interface equivalent to your type alias and pass that to the library you found, e.g.:

The problem is that you have Node 17.2.0. locally but in Netlify's environment, you are running a lower version (by default it's not set as 17.2.0). So the local environment is OK, Netlify environment is KO because of this mismatch of Node versions.

When Netlify deploys your site it installs and builds again your site so you should ensure that both environments work under the same conditions. Otherwise, both node_modules will differ so your application will have different behavior or eventually won't even build because of dependency errors.

You can easily play with the Node version in multiple ways but I'd recommend using the .nvmrc file. Just run the following command in the root of your project:

I found a partial answer. Both StandardScaler and SelectKBest have .get_feature_names_out methods. I did not find the time to investigate further.

using OneHotEncoder is not the way to go here, it's better to extract the features from the column time as separate features like year, month, day, hour, minutes etc... and give these columns as input to your model.

The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model

What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.

The ZeroShotClassificationPipeline is currently not supported by shap, but you can use a workaround. The workaround is required because:

1. The shap Explainer forwards only one parameter to the model (a pipeline in this case), but the ZeroShotClassificationPipeline requires two parameters, namely text, and labels.
2. The shap Explainer will access the config of your model and use its label2id and id2label properties. They do not match the labels returned from the ZeroShotClassificationPipeline and will result in an error.

Below is a suggestion for one possible workaround. I recommend opening an issue at shap and requesting official support for huggingface's ZeroShotClassificationPipeline.