FDT_based_workload_orchestration | workload orchestration algorithm | Artificial Intelligence library

The none dimension is present because some of the images might have three channels and the others have four, so dynamic dimensions are shown as None.

Seems like when you were uploading the dataset the runtime got interrupted which led to the corruption of the data you were trying to upload. Using force=True while deleting should allow you to delete it.

For more information feel free to check out the Hub API basics docs for details on how to delete datasets in Hub.

If you stop uploading a Hub dataset midway through your dataset will be only partially uploaded to Hub. So, you will need to restart the upload. If you would like to re-create the dataset, you can use the overwrite = True flag in hub.empty(overwrite = True). If you are making updates to an existing dataset, you should use version control to checkpoint the states that are in good shape.

It depends! Especially given your relatively vague setup description, it is not clear what loss will be used. But to start from the beginning, let's first check how the default compute_loss() function in the Trainer class looks like.

You can find the corresponding function here, if you want to have a look for yourself (current version at time of writing is 4.17). The actual loss that will be returned with default parameters is taken from the model's output values:

loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

which means that the model itself is (by default) responsible for computing some sort of loss and returning it in outputs.

Following this, we can then look into the actual model definitions for BERT (source: here, and in particular check out the model that will be used in your Sentiment Analysis task (I assume a BertForSequenceClassification model.

The code relevant for defining a loss function looks like this:

Specify dim=1 in torch.tensor_split(x,2) .

The x comes from two tensors with the shape [100,1] stacked at dim 1, so its shape is [100, 2]. After applying tensor_split, you get two tensors both with shape [50, 2].

It's critical but easy...!

Use requestAnimationFrame for your webpage visual changes.

If run as a requestAnimationFrame callback, this will be run at the start of the frame.

const Alan = () => {

You only use the .wv property to fetch the KeyedVectors object from another more complete algorithmic model, like a full Word2Vec model (which contains a KeyedVectors in its .wv attribute).

If you're already working with just-the-vectors, there's no need to request the word-vectors subcomponent. Whatever you were going to do, you just do to the KeyedVectors directly.

However, you're also using the .vocab attribute, which has been replaced. See the migration FAQ for more details:

https://github.com/RaRe-Technologies/gensim/wiki/Migrating-from-Gensim-3.x-to-4#4-vocab-dict-became-key_to_index-for-looking-up-a-keys-integer-index-or-get_vecattr-and-set_vecattr-for-other-per-key-attributes

(Mainly: instead of doing an in w2v_model.wv.vocab, you may only need to do in kv_model or in kv_model.key_to_index.)

It looks like one of your files was corrupted.

You can try to solve the problem by opening the directory C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\ and deleting the file ResMaskNet_Z_resmasking_dropout1_rot30.pth.

Then run again the code and it should redownload the deleted file.

The warning in the first two lines is just a warning, it's saying that some of the code in the library nilearn is deprecated. Most of the times you would just ignore this line, this will be probably fixed by the coders of nilearn in a future patch.

This will load Resnet 50 model

Well, Neural Network Models are basically a black box, so there is no way to know this for sure.

I could imagine that the grammar in last sentence is a bit too "fancy"/literature-like if the model was trained on news or web data and might be throwing the model off. This difficulty of seeing the sentence context as something that would be followed up by a name as well as the fact that "Hagrid" is a kind of unusual name could be the reason.

You can try some other models such as the one integrated in Flair:

https://huggingface.co/flair/ner-english-large?text=Hermione+bought+a+car%2C+then+both+Hermione+and+Hagrid+raced+it+on+the+track.+Tom+Brady+was+very+happy+with+Hagrid+this+year.

or this fine-tuned BERT model:

https://huggingface.co/dslim/bert-large-NER?text=Hermione+bought+a+car%2C+then+both+Hermione+and+Hagrid+raced+it+on+the+track.+Tom+Brady+was+very+happy+with+Hagrid+this+year.

They are more powerful and get it right, from my experience SpaCy is a nice tool and quite fast, but not the most precise for NER.