NumPy

🐛 Bug

--- Logging error ---
Traceback (most recent call last):
  File "/scratch/rzou/pt/workspace-env/lib/python3.7/logging/__init__.py", line 1025, in emit
    msg = self.format(record)
  File "/scratch/rzou/pt/workspace-env/lib/python3.7/logging/__init__.py", line 869, in format
    return fmt.format(record)
  File "/scratch/rzou/pt/workspace-env/lib/python3.7/site-packages/_pytes

.coveragerc is present in git but not release tarballs.

Hi
Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq

What happened:

If a negative value for drop_axis is passed into either map_blocks or map_overlap a non-informative exception is raised.

What you expected to happen:

I would expect this would work as in NumPy for negative axis arguments where axis becomes axis = axis % array.ndim. If it is not intended to work, then it should raise a user-friendly AxisError. This came up

Reporting a bug

• [X]

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

What's wrong?

hi,
if possible, please add these indicators as well:
TDI (Traders Dynamic Index)
chandelier exit
pivot points
BOP (balance of power)
CTM (Chande trend meter)
Coppock Curve
Correlation Coefficient
PMO (DecisionPoint Price Momentum Oscillator)
Ulcer Index
most of them except TDI are available on stockcharts.com
thanks

Description

While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file

Error Message

ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.

To Reproduce

from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p

Support Series.between

Is your feature request related to a problem? Please describe.
Sometimes you want to check that data values are present in another array, but only up to a certain tolerance.

Describe the solution you'd like
da.isin(test_values, tolerance=1e-6), where the tolerance argument is optional.

Not sure what the implementation should be but there are two vectorized [suggestions here](http