Nuxt custom error page doesn't load for uncaught errors

I'm trying to use a custom error page in Nuxt (see: https://nuxtjs.org/docs/concepts/views/#error-page).

I've created layouts/error.vue, but this page doesn't load for uncaught exceptions, for example:

<script>
export default {
  computed: {
    component() {
      return () => import(`./non-existent-component.vue`);
    },
  }
}
</script>

When this error occurs server-side, I see the standard production error page. Is there a way to catch this in a generic way so that my custom error page loads in that instance?

from Nuxt custom error page doesn't load for uncaught errors

HuggingFace Evaluate a Fine-tuned Zero-Shot Model

I am finetuning the HuggingFace facebook/bart-large-mnli model to suit my need, I use the following parameters:

training_args = TrainingArguments(
    output_dir=model_directory,      # output directory
    num_train_epochs=30,              # total number of training epochs
    per_device_train_batch_size=1,  # batch size per device during training - 16 - Don't go over 1, it's out of memory
    per_device_eval_batch_size=2,   # batch size for evaluation - 64 - Don't go over 2, it's out of memory
    warmup_steps=500,                 # number of warmup steps for learning rate scheduler - 500
    weight_decay=0.01,               # strength of weight decay
)

model = BartForSequenceClassification.from_pretrained("facebook/bart-large-mnli")

trainer = Trainer(
    model=model,                          # the instantiated 🤗 Transformers model to be trained
    args=training_args,                   # training arguments, defined above
    compute_metrics=compute_metrics,      # a function to compute the metrics
    train_dataset=train_dataset,          # training dataset
    eval_dataset=test_dataset             # evaluation dataset
)

# Train the trainer
trainer.train()

The compute_metrics I use is:

import numpy as np
from datasets import Dataset, load_metric
from transformers import EvalPrediction

def compute_metrics(p: EvalPrediction):
  metric_acc = load_metric("accuracy")
  preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
  preds = np.argmax(preds, axis=1)
  result = {}
  result["accuracy"] = metric_acc.compute(predictions=preds, references=p.label_ids)["accuracy"]
  return result

But no matter how much train or test data I use, or how many epochs, when I use trainer.evaluate() I get an accuracy of 0.5.

My questions are:

1. How do I improve it?
2. How do I implement other metrics for the evaluation? for example F1 score.

I tried changing (adding) the metrics to this:

def compute_metrics(p: EvalPrediction):
  load_accuracy = load_metric("accuracy")
  load_f1 = load_metric("f1")
  preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
  preds = np.argmax(preds, axis=1)
  result = {}
  result["accuracy"] = load_accuracy.compute(predictions=preds, references=p.label_ids)["accuracy"]
  result["f1"] = load_f1.compute(predictions=preds, references=p.label_ids)["f1"]
  return result

But then I got this error while running trainer.evaluate():

ValueError: pos_label=1 is not a valid label. It should be one of [0, 2]

---

You can refer to my previous question for more details about my finetuning here

from HuggingFace Evaluate a Fine-tuned Zero-Shot Model

Custom sort function in BigQuery

Is there a way to pass a comparison function to sort by a column in BigQuery? I am fine, if required, using a SQL or JS udf. This would be similar to something like Javascript's localeCompare function, returning a -number, 0, or +number depending on if the first value is less than or equal to the second value.

This would allow doing something like:

const comparisonFunction = (val1, val2) => ...
MyValues.sort(comparisonFunction)

For example, if I have the following data:

with tbl as (
  select "date" val union all 
  select "time" union all 
  select "number"
)
select * from tbl order by comparisonFunction(tbl.val)

And for arguments sake, let's say the comparison function is something like:

const RANK_MAP = {"number": 1, "time": 2, "date": 3}
function comparisonFunction(val1, val2) {
    return RANK_MAP[val1] - RANK_MAP[val2];
}

Note that I want this signature (or something like it). I'm not looking for 'converting this into an inline sql function' with something like:

with tbl as (
  select "date" val union all 
  select "time" union all 
  select "number"
)
select
  tbl.val,
  case tbl.val
    when 'number' then 1
    when 'time' then 2
    when 'date' then 3
  end rank
from tbl
order by rank

How could this be done with a udf function similar to the js approach above?

from Custom sort function in BigQuery