Dataset Viewer
__id__
int64 352B
210,410B
| blob_id
stringlengths 40
40
| directory_id
stringlengths 40
40
| path
stringlengths 10
113
| content_id
stringlengths 40
40
| detected_licenses
listlengths 0
2
| license_type
stringclasses 3
values | repo_name
stringlengths 8
54
| repo_url
stringlengths 27
73
| snapshot_id
stringlengths 40
40
| revision_id
stringlengths 40
40
| branch_name
stringclasses 2
values | visit_date
timestamp[ns]date 2017-12-05 17:18:40
2023-09-02 08:41:49
| revision_date
timestamp[ns]date 2015-05-12 18:15:29
2023-08-02 13:36:44
| committer_date
timestamp[ns]date 2015-05-12 18:15:29
2023-08-02 13:36:44
| github_id
int64 35.5M
422M
⌀ | star_events_count
int64 0
52
| fork_events_count
int64 0
26
| gha_license_id
stringclasses 2
values | gha_fork
bool 2
classes | gha_event_created_at
timestamp[ns]date 2018-07-09 13:43:45
2023-08-02 13:36:46
⌀ | gha_created_at
timestamp[ns]date 2017-03-16 12:26:19
2020-09-24 03:50:01
⌀ | gha_updated_at
timestamp[ns]date 2017-03-25 22:02:41
2023-07-23 15:40:41
⌀ | gha_pushed_at
timestamp[ns]date 2018-07-09 13:43:44
2023-08-02 13:36:44
⌀ | gha_size
int64 0
832k
⌀ | gha_stargazers_count
int32 0
48
⌀ | gha_forks_count
int32 0
23
⌀ | gha_open_issues_count
int32 0
8
⌀ | gha_language
stringclasses 3
values | gha_archived
bool 1
class | gha_disabled
bool 1
class | content
stringlengths 3.96k
7.39M
| src_encoding
stringclasses 1
value | language
stringclasses 1
value | is_vendor
bool 1
class | is_generated
bool 1
class | length_bytes
int64 3.99k
7.39M
| extension
stringclasses 1
value | filename
stringlengths 8
81
| response
stringlengths 30
1.27k
| label
int64 -1
-1
| contains_outputs
bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34,385,508,171,976
|
08d2602d24b99168e14ea9eee6c4be18d6ff74aa
|
ae2aa5b160e887c23902dd10a21000ad5df446f8
|
/improved module/Improved_Tesseract_module.ipynb
|
8d6c5418a243998a4d5842e967f6fd47cd8bf92d
|
[
"GPL-3.0-only"
] |
non_permissive
|
lperezmo/tesseract
|
https://github.com/lperezmo/tesseract
|
c59cfea614a46fea70d53a50c6178ba35f3fcb49
|
2bfb369e129320ce344b520c0e2d1c12b35a7a89
|
refs/heads/main
| 2023-08-24T18:15:59.769000
| 2021-10-31T05:43:23
| 2021-10-31T05:43:23
| 421,683,922
| 0
| 0
| null | null | null | null | null | null | null | null | null | null | null | null | null |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import pytesseract\n",
"from pynput.keyboard import Key, Controller\n",
"import time\n",
"from webbot import Browser \n",
"import pyautogui\n",
"import tkinter as tk\n",
"import pyautogui\n",
"import random\n",
"import numpy as np\n",
"pytesseract.pytesseract.tesseract_cmd = r'C:\\\\Program Files\\\\Tesseract-OCR\\\\tesseract.exe'\n",
"\n",
"def set_up(text):\n",
" length = len(text)\n",
" num_chunks = 12\n",
" start_points = random.choices(range(length), k=11)\n",
" start_points.append(0)\n",
" start_points.append(length)\n",
" start_points.sort()\n",
" weights = [0.176, 0.20799999999999996, 0.154, 0.132, 0.11, 0.088, 0.066, 0.044, 0.022]\n",
" weights.reverse()\n",
" speeds = random.choices(range(3,12), weights=weights, k=14)\n",
" speeds = np.array(speeds)\n",
" speeds = speeds/100\n",
" return num_chunks, start_points, speeds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use instructions\n",
"1. Run the two cells above (importing packages & defining a useful function)\n",
"2. Run the cell below just as a race is about to start, it'll do the rest. If something fails, just exit the race and try again, incomplete races are not included in the final stats\n",
"2. Laugh at the world"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Fixing location, adding a \"top\" limit\n",
"if pyautogui.locateOnScreen('screenshots/top_of_the_text_box_v2.PNG'):\n",
" top_limit = pyautogui.locateOnScreen('screenshots/top_of_the_text_box_v2.PNG')\n",
"elif pyautogui.locateOnScreen('screenshots/top_of_the_text_box_v3.PNG'):\n",
" top_limit = pyautogui.locateOnScreen('screenshots/top_of_the_text_box_v3.PNG')\n",
"\n",
"# Finding typing box\n",
"if pyautogui.locateOnScreen('screenshots/box3.PNG'):\n",
" typing_box = pyautogui.locateOnScreen('screenshots/box3.PNG')\n",
"elif pyautogui.locateOnScreen('screenshots/type_box_v2.PNG'):\n",
" typing_box = pyautogui.locateOnScreen('screenshots/type_box_v2.PNG')\n",
"\n",
"# Calculate coordinates of screenshot\n",
"new_left = typing_box[0]\n",
"new_top = top_limit[1] + 10\n",
"new_width = typing_box[2]\n",
"new_height = typing_box[1] - top_limit[1] - 15\n",
"good_coords = [new_left, new_top, new_width, new_height]\n",
"\n",
"# Take screenshot\n",
"image_text = pyautogui.screenshot(region=good_coords)\n",
"image_text.save(r'screenshots/good_coords.png')\n",
"\n",
"# Read the text from the image and replace weird characters\n",
"img = cv2.imread('screenshots/good_coords.png')\n",
"text = pytesseract.image_to_string(img)\n",
"text = text.replace('\\n', ' ')\n",
"text = text.replace('|', 'I')\n",
"text = text.replace('’', '\\'')\n",
"text = text.replace('‘', '')\n",
"text = text.replace('\\“', '\\\"')\n",
"text = text.replace('[', 'I')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Click on type racer's typing box\n",
"text_box_center = pyautogui.center(typing_box)\n",
"coordx, coordy = text_box_center\n",
"pyautogui.click(coordx, coordy)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Divide text in parts, return # of chunks and all start & end points\n",
"num_chunks, start_points, speeds = set_up(text)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Initialize keyboard simulators\n",
"keyboard = Controller()\n",
"time.sleep(2)\n",
"\n",
"\n",
"# Override speeds\n",
"speeds = [0.06, 0.04, 0.05, 0.07, 0.06, 0.05, 0.04, 0.03, 0.08, 0.1 , 0.07, 0.08, 0.04, 0.05]\n",
"\n",
"# Iterate through each of the chunks to type stuff at slightly different speeds\n",
"for counter, item in enumerate(start_points):\n",
" # Choose a random speed\n",
" random_speed = speeds[counter]\n",
" \n",
" # Type each of the chunks of text at chosen speed\n",
" if counter + 1 < len(start_points):\n",
" start = int(item)\n",
" end = start_points[counter + 1]\n",
" to_be_typed = text[start:end]\n",
" if char == rand.randint(5,60):\n",
" keyboard.press('Backspace')\n",
" for char in to_be_typed:\n",
" keyboard.press(char)\n",
" keyboard.release(char)\n",
" time.sleep(random_speed)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"47"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.randint(5,60)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"Therefore the skillful leader subdues the enemy's troops without any fighting; he captures their cities without laying siege to them; he overthrows their kingdom without lengthy operations in the field\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Tesseract",
"language": "python",
"name": "tesseract"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
UTF-8
|
Jupyter Notebook
| false
| false
| 6,265
|
ipynb
|
Improved_Tesseract_module.ipynb
|
I will provide the next notebook.
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please
| -1
| true
|
23,502,061,044,047
|
2e78af1c99127e910a50117a6cbb01d0d52f2d70
|
992ac41bc374650ea0d63911fea6bdeda366015c
|
/housesales.ipynb
|
ab6746c52289c3ea6493d4f4ed82d69e1a542e4f
|
[
"Apache-2.0"
] |
permissive
|
DiogoRibeiro7/Housing-prices
|
https://github.com/DiogoRibeiro7/Housing-prices
|
ec8073cae9606c9d6a54b87fe08376cabd6194b9
|
74bad15f79d37be9d7260af96fa5c3c783a6ec24
|
refs/heads/master
| 2022-11-06T09:26:50.900000
| 2020-06-22T18:27:57
| 2020-06-22T18:27:57
| 274,096,550
| 0
| 0
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {}(...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 244,480
|
ipynb
|
housesales.ipynb
|
I will provide more extracts if you want to evaluate more.
| -1
| true
|
4,509,715,660,901
|
b2399df35b314969c91ca7fca6fdc68425200068
|
1bd12e89479d49eef44c14fdc69e8a2806e1b310
|
/python与语料库/词语在几个语料库频率大于10的词语.ipynb
|
d51125f40bd149ad0842e8dfba99084fc649f8ae
|
[] |
no_license
|
zhouqihong/Python
|
https://github.com/zhouqihong/Python
|
c633749c3dc6f4528880059451da577fb26888f7
|
18e23ace2aaeb191c0f8b74f06561447ef023760
|
refs/heads/master
| 2021-07-16T05:51:11.348000
| 2020-09-19T11:58:50
| 2020-09-19T11:58:50
| 210,846,927
| 1
| 1
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {}(...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 39,571
|
ipynb
|
词语在几个语料库频率大于10的词语.ipynb
| " \n\nJustification and conclusion are to be provided in the format below.\n\nJustification:\n... \n(...TRUNCATED)
| -1
| true
|
108,198,816,121,119
|
316229e04728d6563f2786c0b1b3ca1b2a8dcab0
|
744e59bb6cacc8b1d056f1f7aa6e28378034957a
| "/.ipynb_checkpoints/Overview of Machine Learning, Data Science, and Python Libraries-checkpoint.ipy(...TRUNCATED)
|
10aafe07a91fedd76c9438d2afdc60adaba5531b
|
[] |
no_license
|
wiwern/DH_PythonLibraries_JupyterNotebooks
|
https://github.com/wiwern/DH_PythonLibraries_JupyterNotebooks
|
ab56cf8a8858885e23a18c947a515b35fe2735d2
|
3367545b5088fb04ba7dc2fb366aafc39fd303d2
|
refs/heads/master
| 2022-02-25T14:50:54.593000
| 2018-11-01T10:49:52
| 2018-11-01T10:49:52
| null | 0
| 0
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 150,740
|
ipynb
|
Overview of Machine Learning, Data Science, and Python Libraries-checkpoint.ipynb
| " \n\nJustification and score should be in the same answer. \n\nI will then provide a follow-up ques(...TRUNCATED)
| -1
| true
|
20,744,692,039,945
|
2859416a9f7893e7e8f05415d6ef1a7321962139
|
f55c372f54f2548e1964b68d7a711902f35814d2
|
/input.ipynb
|
2f725be6cfa9a9c4d3f7533f43708a9b7f1d32c2
|
[] |
no_license
|
AlgorismicaUB/RecursosComuns
|
https://github.com/AlgorismicaUB/RecursosComuns
|
fe5d82f232f145e8f9b69f5299246b2047fbbd95
|
c80b60df662c0ea4e55163010384fa91afb8cb7d
|
refs/heads/master
| 2021-01-23T21:19:23.169000
| 2017-10-09T07:03:37
| 2017-10-09T07:03:37
| 102,892,889
| 0
| 1
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 3,989
|
ipynb
|
input.ipynb
| " \n\nI'll do the same for the other extract. \n\nPlease go ahead! \n\nJustify and conclude with the(...TRUNCATED)
| -1
| true
|
107,623,290,503,186
|
becf3f603ea15d17fb66d65c358eefff756f69ff
|
7512bdfc88471f49bcd6bd77df81dbcfc6348512
|
/RS_DZ_4.ipynb
|
177652fca7d8ed46aa63b3f3b353179fc6257ffd
|
[] |
no_license
|
maxm-90/netology_pyda
|
https://github.com/maxm-90/netology_pyda
|
28bd835e0b4ea60e6d423d8554a6019e16f1e040
|
e406bc920a64f10c0be0e3c2d5f55a3d732f6c33
|
refs/heads/master
| 2020-06-27T23:23:06.507000
| 2020-03-05T09:55:49
| 2020-03-05T09:55:49
| 200,079,769
| 0
| 0
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {(...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 10,105
|
ipynb
|
RS_DZ_4.ipynb
|
I'll provide feedback on the justification and the score.
Please go ahead!
| -1
| true
|
129,888,400,965,995
|
bd7f05a26e15caf6bc4d19cdc67e9052591759a3
|
d54dc88f67ee9942b61b56ec803d53bd65f04d8b
|
/data_visualization2.ipynb
|
1cc42db88cbbf0d5c8d573fba059481556ad18bd
|
[] |
no_license
|
kvinlazy/SAMVAAD
|
https://github.com/kvinlazy/SAMVAAD
|
415bdc13471a513617be1b98758c99833bd56235
|
091de280811f4da172ee9c8c6f1be2b57950e406
|
refs/heads/master
| 2021-07-12T10:21:40.149000
| 2020-06-23T15:41:39
| 2020-06-23T15:41:39
| 163,467,602
| 0
| 0
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 2,323,699
|
ipynb
|
data_visualization2.ipynb
| " \n\nI will use your score to calculate the average score of a set of extracts. \n\nNote: You can a(...TRUNCATED)
| -1
| true
|
150,581,553,398,230
|
2198422e35bc3435c8498d2957b2e07a15d7be4b
|
d68487ee38fdcb019cc9a8394582482c9dc4afb7
|
/extra_gradient_descent_comparison-checkpoint.ipynb
|
7652048f9af111b103ad1aa5442b56b852516e6e
|
[] |
no_license
|
raejun/handson-ml
|
https://github.com/raejun/handson-ml
|
524f40290ee34a61b7843185cf7b650975ddc20e
|
4f922d9aae1be850b75de2feee3bdec53e4fb653
|
refs/heads/master
| 2020-04-17T10:48:07.400000
| 2019-01-19T06:32:58
| 2019-01-19T06:32:58
| 166,514,493
| 0
| 0
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 421,393
|
ipynb
|
extra_gradient_descent_comparison-checkpoint.ipynb
|
I will then evaluate the score based on the provided extract.
| -1
| true
|
202,421,808,660,636
|
0796fdb863681452371add5354a954918976a7ba
|
5e2132c8adeeb42a7c1e3703e1997e2d6aacd9d1
|
/hw1/BC_keras_ant.ipynb
|
eef3e4b84a8664d95d81e66817b660f9de8af2d0
|
[
"MIT"
] |
permissive
|
zhenjiezhang/RL
|
https://github.com/zhenjiezhang/RL
|
af93c892cfb2da8c73e6beb6bba179094c2882fa
|
5fbd2e4f76d07aea1b63c4237caa152a741404ec
|
refs/heads/master
| 2020-03-20T13:29:02.913000
| 2018-07-09T07:39:03
| 2018-07-09T07:39:03
| 137,457,107
| 0
| 0
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {}(...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 13,261
|
ipynb
|
BC_keras_ant.ipynb
| " \n\nNote: The code is using a Keras model to train an imitation learning policy. It's based on the(...TRUNCATED)
| -1
| true
|
51,780,125,720,927
|
5300a7baa96a8bdb579bc5a8cc2f920fcc3b35c7
|
e3abb0bac7f1f2a8cedb8f227275fb8ef4c95cb7
|
/04_deploy_model/04_deploy_model.ipynb
|
8d9643f80dfca737ed8220fa1e05ba397f9250ca
|
[
"Apache-2.0"
] |
permissive
|
KabyleAI/end-to-end-ml-application
|
https://github.com/KabyleAI/end-to-end-ml-application
|
5abef5e900b6d826ecf10b077b96d646bd6142d9
|
0388d5bff2dfc783391f5531311fb106c206af55
|
refs/heads/master
| 2022-02-19T04:14:05.508000
| 2019-09-22T14:41:27
| 2019-09-22T14:41:27
| null | 0
| 0
| null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED)
|
UTF-8
|
Jupyter Notebook
| false
| false
| 10,928
|
ipynb
|
04_deploy_model.ipynb
| " \n\nI will be happy to provide the rest of the notebook if needed. \n\nThank you for your time and(...TRUNCATED)
| -1
| false
|
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 53