ayulockin

lukas

word_to_id = imdb.get_word_index()
word_to_id = {k: (v+3) for k, v in word_to_id.items()}
id_to_word = {value: key for key, value in word_to_id.items()}
id_to_word[0] = ""  # Padding
id_to_word[1] = ""  # Start token
id_to_word[2] = "�"  # Unknown
id_to_word[3] = ""  # End token


def decode(word):

SamuelMarks

Google Cloud configuration

First: install the CLI program for your distribution: https://cloud.google.com/sdk/install

Parameters

Modify accordingly:

export REGION='us-central1'
export ZONE='us-central1-f'
export PROJECT_NAME='proj'

borisdayma

To be posted in: https://forums.fast.ai/c/fastai-users/fastai-v2/

Title: Proposed workflow to compare & monitor models using WandbCallback

Content:

Hi,

I’ve been working on WandbCallback for the past few months (with a lot of help from @sgugger) and I'm very excited to show how it works!

CallmeMehdi

TF-Hub text embedding modules for underrepresented languages

Mentors:

• Morgan Roff
• Sayak Paul
• jaeyounkim

This is a summary of my GSoC 2021 project. In this project, I tried to produce text embedding modules trained on underrepresented languages like Arabic and Swahili and publish them on tfhub.dev.

YashasSamaga

DISCLAIMER

This gist is unofficial. It was created for personal use but have kept it public in case it would be of use to others. This document is not updated regularly and may not reflect the current status of the CUDA backend.

sararob

# Copyright 2021 Google LLC.
# SPDX-License-Identifier: Apache-2.0
import kfp
import json
import time
from google.cloud import bigquery
from google.cloud.exceptions import NotFound
from kfp.v2.google.client import AIPlatformClient

client = bigquery.Client()

apaszke

import sys
from collections import OrderedDict

PY2 = sys.version_info[0] == 2
_internal_attrs = {'_backend', '_parameters', '_buffers', '_backward_hooks', '_forward_hooks', '_forward_pre_hooks', '_modules'}


class Scope(object):
    def __init__(self):
        self._modules = OrderedDict()

ariG23498

fclesio

def get_classification_report(y_test, y_pred):
    '''Source: https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format'''
    from sklearn import metrics
    report = metrics.classification_report(y_test, y_pred, output_dict=True)
    df_classification_report = pd.DataFrame(report).transpose()
    df_classification_report = df_classification_report.sort_values(by=['f1-score'], ascending=False)
    return df_classification_report