Backup-bdg/Xoron-Dev-MultiMoe
Any-to-Any • 5B • Updated • 72 • 4
Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
code
License:
repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
keras-team/keras-io | examples/vision/ipynb/mnist_convnet.ipynb | apache-2.0 | import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
"""
Explanation: Simple MNIST convnet
Author: fchollet<br>
Date created: 2015/06/19<br>
Last modified: 2020/04/21<br>
Description: A simple convnet that achieves ~99% test accuracy on MNIST.
Setup
End of explanation
"""
# Model / dat... |
tensorflow/docs-l10n | site/ja/tfx/tutorials/tfx/components_keras.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
ganguli-lab/twpca | notebooks/warp_unit_tests.ipynb | mit | _, _, data = twpca.datasets.jittered_neuron()
model = TWPCA(data, n_components=1, warpinit='identity')
np.all(np.isclose(model.params['warp'], np.arange(model.shared_length), atol=1e-5, rtol=2))
np.nanmax(np.abs(model.transform() - data)) < 1e-5
"""
Explanation: check identity warp does not change data appreciably
E... |
oddt/notebooks | DUD-E.ipynb | bsd-3-clause | from __future__ import print_function, division, unicode_literals
import oddt
from oddt.datasets import dude
print(oddt.__version__)
"""
Explanation: <h1>DUD-E: A Database of Useful Decoys: Enhanced</h1>
End of explanation
"""
%%bash
mkdir -p ./DUD-E_targets/
wget -qO- http://dude.docking.org/targets/ampc/ampc.tar.... |
iAInNet/tensorflow_in_action | _pratice_cifar10.ipynb | gpl-3.0 | max_steps = 3000
batch_size = 128
data_dir = 'data/cifar10/cifar-10-batches-bin/'
model_dir = 'model/_cifar10_v2/'
"""
Explanation: 全局参数
End of explanation
"""
X_train, y_train = cifar10_input.distorted_inputs(data_dir, batch_size)
X_test, y_test = cifar10_input.inputs(eval_data=True, data_dir=data_dir, batch_size=... |
mitdbg/modeldb | demos/webinar-2020-5-6/02-mdb_versioned/01-train/01 Basic NLP.ipynb | mit | !python -m spacy download en_core_web_sm
"""
Explanation: Versioning Example (Part 1/3)
In this example, we'll train an NLP model for sentiment analysis of tweets using spaCy.
Through this series, we'll take advantage of ModelDB's versioning system to keep track of changes.
This workflow requires verta>=0.14.4 and ... |
cipri-tom/Swiss-on-Amazon | filter_swiss_helpful_reviews.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import yaml
"""
Explanation: The following script extracts the (more) helpful reviews from the swiss reviews and saves them locally.
From the extracted reviews it also saves a list with their asin identifiers.
The list of asin id... |
simonsfoundation/CaImAn | demos/notebooks/demo_Ring_CNN.ipynb | gpl-2.0 | get_ipython().magic('load_ext autoreload')
get_ipython().magic('autoreload 2')
import glob
import logging
import numpy as np
import os
logging.basicConfig(format=
"%(relativeCreated)12d [%(filename)s:%(funcName)20s():%(lineno)s] [%(process)d] %(message)s",
# filename="/tm... |
Kaggle/learntools | notebooks/deep_learning_intro/raw/tut3.ipynb | apache-2.0 | #$HIDE_INPUT$
import pandas as pd
from IPython.display import display
red_wine = pd.read_csv('../input/dl-course-data/red-wine.csv')
# Create training and validation splits
df_train = red_wine.sample(frac=0.7, random_state=0)
df_valid = red_wine.drop(df_train.index)
display(df_train.head(4))
# Scale to [0, 1]
max_ =... |
GoogleCloudPlatform/mlops-on-gcp | model_serving/caip-load-testing/03-analyze-results.ipynb | apache-2.0 | import time
from datetime import datetime
from typing import List
import numpy as np
import pandas as pd
import google.auth
from google.cloud import logging_v2
from google.cloud.monitoring_dashboard.v1 import DashboardsServiceClient
from google.cloud.logging_v2 import MetricsServiceV2Client
from google.cloud.monito... |
Neuroglycerin/neukrill-net-work | notebooks/augmentation/Preliminary Online Augmentation Results.ipynb | mit | import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.dense_dataset
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import holoviews as hl
%load_ext holoviews.ipython
import sklearn.metrics
cd ..
settings = neukrill_net.utils.Settings("settings.... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/examples/tracebuffer_i2c.ipynb | bsd-3-clause | from pprint import pprint
from time import sleep
from pynq import PL
from pynq import Overlay
from pynq.drivers import Trace_Buffer
from pynq.iop import Pmod_TMP2
from pynq.iop import PMODA
from pynq.iop import PMODB
from pynq.iop import ARDUINO
ol = Overlay("base.bit")
ol.download()
pprint(PL.ip_dict)
"""
Explanatio... |
rnder/data-science-from-scratch | notebook/ch21_network_analysis.ipynb | unlicense | from __future__ import division
import math, random, re
from collections import defaultdict, Counter, deque
from linear_algebra import dot, get_row, get_column, make_matrix, magnitude, scalar_multiply, shape, distance
from functools import partial
users = [
{ "id": 0, "name": "Hero" },
{ "id": 1, "name": "Dunn... |
End of preview. Expand in Data Studio
Dataset description
This dataset consists of sequences of Python code followed by a a docstring explaining its function. It was constructed by concatenating code and text pairs from this dataset that were originally code and markdown cells in Jupyter Notebooks.
The content of each example the following:
[CODE]
"""
Explanation: [TEXT]
End of explanation
"""
[CODE]
"""
Explanation: [TEXT]
End of explanation
"""
...
How to use it
from datasets import load_dataset
ds = load_dataset("codeparrot/github-jupyter-code-to-text", split="train")
Dataset({
features: ['repo_name', 'path', 'license', 'content'],
num_rows: 47452
})
- Downloads last month
- 150
Size of downloaded dataset files:
283 MB
Size of the auto-converted Parquet files:
283 MB
Number of rows:
59,316
Models trained or fine-tuned on codeparrot/github-jupyter-code-to-text
