python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
# pretrained = 'https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth'
p... | ViT-Adapter-main | detection/configs/upgraded_mask_rcnn/mask_rcnn_mae_adapter_base_lsj_fpn_50ep_coco.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
drop_path_rate = 0.4
model = dict(
type='HybridTaskCascadeAug',
backbone=dict(
type='ViTAdapter',
img_size=38... | ViT-Adapter-main | detection/configs/htc++/htc++_augreg_adapter_large_fpn_3x_coco_ms.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# pretrained = 'https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21k.pth'
pretr... | ViT-Adapter-main | detection/configs/htc++/htc++_beitv2_adapter_large_fpn_3x_coco.py |
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
NUM_CLASSES = 80
drop_path_rate = 0.3 # 0.4 (pre-train) -> 0.3 (fine-tune)
# https://github.com/czczup/ViT-Adapter/releases/download/v0.3.1/htc++_beitv2_adapter_large_fpn_o365.pth
load_fr... | ViT-Adapter-main | detection/configs/htc++/htc++_beitv2_adapter_large_fpn_o365_coco.py |
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
NUM_CLASSES = 80
drop_path_rate = 0.3 # 0.4 (pre-train) -> 0.3 (fine-tune)
model = dict(
type='HybridTaskCascadeAug',
backbone=dict(
type='BEiTAdapter',
img_size=2... | ViT-Adapter-main | detection/configs/htc++/htc++_beitv2_adapter_large_fpn_o365_coco_ms.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
model = dict(
type='HybridTaskCascadeAug',
backbone=dict(
type='BEiTAdapter',
img_size=224,
patch_siz... | ViT-Adapter-main | detection/configs/htc++/htc++_beit_adapter_large_fpn_3x_coco_ms.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# pretrained = 'https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth'
pretrain... | ViT-Adapter-main | detection/configs/htc++/htc++_beit_adapter_large_fpn_3x_coco_old.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# pretrained = 'https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2... | ViT-Adapter-main | detection/configs/htc++/htc++_augreg_adapter_large_fpn_3x_coco.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
model = dict(
type='HybridTaskCascadeAug',
backbone=dict(
type='BEiTAdapter',
img_size=224,
patch_siz... | ViT-Adapter-main | detection/configs/htc++/htc++_beitv2_adapter_large_fpn_3x_coco_ms.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# pretrained = 'https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth'
pretrain... | ViT-Adapter-main | detection/configs/htc++/htc++_beit_adapter_large_fpn_3x_coco.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
pretrained = 'pretrained/uni-perceiver-large-L24-H1024-224size-pretrained_converted.pth'
drop_path_rate = 0.4
model = dict(
type=... | ViT-Adapter-main | detection/configs/htc++/htc++_uniperceiver_adapter_large_fpn_3x_coco.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# pretrained = 'https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'
pretrained = 'pretrained/deit_small_patch16... | ViT-Adapter-main | detection/configs/atss/atss_deit_adapter_small_fpn_3x_coco.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_3x.py',
'../_base_/default_runtime.py'
]
# pretrained = 'https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'
pretrained = 'pretrained/deit_small_patch16... | ViT-Adapter-main | detection/configs/gfl/gfl_deit_adapter_small_fpn_3x_coco.py |
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | ViT-Adapter-main | detection/ops/test.py |
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | ViT-Adapter-main | detection/ops/setup.py |
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | ViT-Adapter-main | detection/ops/functions/ms_deform_attn_func.py |
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | ViT-Adapter-main | detection/ops/functions/__init__.py |
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | ViT-Adapter-main | detection/ops/modules/ms_deform_attn.py |
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | ViT-Adapter-main | detection/ops/modules/__init__.py |
import torch
from qwen.model import QwenVL
#usage
img = torch.randn(1, 3, 256, 256)
caption = torch.randint(0, 20000, (1, 1024))
model = QwenVL()
output = model(img, caption)
print(output.shape)
| Qwen-VL-main | example.py |
from qwen.inference import QwenVLChat
qwen_chat = QwenVLChat(model_name="Qwen/Qwen-VL-Chat", device_map="cuda")
response = qwen_chat.chat([
{"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
{"text": "这是什么?"}
])
print(response)
response = qwen_chat.chat("框出图中击掌的位置")
print(... | Qwen-VL-main | inference.py |
from qwen.model import QwenVL, QwenVLTokenizer
from qwen.train import CFG, Train
from qwen.inference import QwenVLChat
| Qwen-VL-main | qwen/__init__.py |
import torch
import torch.nn as nn
from transformers import AutoTokenizer, CLIPProcessor
from qwen.transformer import (
Decoder,
Encoder,
Transformer,
ViTransformerWrapper,
AutoregressiveWrapper
)
class QwenVLTokenizer:
def __init__(self):
try:
self.processor = CLIPProce... | Qwen-VL-main | qwen/model.py |
from functools import partial
from typing import Optional
import torch
from torch import nn, einsum, Tensor
import torch.nn.functional as F
from collections import namedtuple
from functools import wraps
from packaging import version
from dataclasses import dataclass
from einops import rearrange
# constants
Efficie... | Qwen-VL-main | qwen/attend.py |
import torch
# This is the unfused version of StableAdamW. It is slower than the fused version (coming).
class StableAdamWUnfused(torch.optim.Optimizer):
def __init__(
self,
params,
lr=0.002,
weight_decay=0.2,
betas=(0.9, 0.99),
eps=1e-8,
clip_thresh=1.0,
... | Qwen-VL-main | qwen/utils.py |
import math
from dataclasses import dataclass
from functools import partial, wraps
from inspect import isfunction
# constants
from math import ceil
from random import random
from typing import Callable, List, Optional
import torch
import torch.nn.functional as F
from einops import pack, rearrange, reduce, repeat, unp... | Qwen-VL-main | qwen/transformer.py |
import math
import multiprocessing
import os
from datetime import timedelta
from functools import partial
from itertools import chain
import torch
########### SETUP CONFIG
import torch.distributed as dist
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import Acceler... | Qwen-VL-main | qwen/train.py |
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import torch
class QwenVLChat:
def __init__(self,
model_name,
device_map="cuda",
trust_remote_code=True,
bf16=False,
... | Qwen-VL-main | qwen/inference.py |
import torch
from cm3.model import CM3
#usage
img = torch.randn(1, 3, 256, 256)
caption = torch.randint(0, 20000, (1, 1024))
model = CM3()
output = model(img, caption)
print(output.shape) # (1, 1024, 20000)
| CM3Leon-main | example.py |
from cm3.model import CM3Tokenizer, CM3 | CM3Leon-main | cm3/__init__.py |
import logging
import torch
from torch import nn
from torch.nn import Module
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import AutoTokenizer, CLIPProcessor
from zeta.nn.architecture.transformer import Decoder, Encoder, Transformer, ViTransformerWrapper
from zeta.nn.arch... | CM3Leon-main | cm3/model.py |
CM3Leon-main | cm3/transformer.py | |
import math
import multiprocessing
import os
from datetime import timedelta
from functools import partial
from itertools import chain
import torch
from torch.distributed.fsdp import (
FullyShardedDataParallel,
MixedPrecision,
BackwardPrefetch,
ShardingStrategy,
)
from accelerate import Accelerator
from... | CM3Leon-main | cm3/train.py |
import multiprocessing
import argparse
from itertools import chain
from datasets import load_dataset
from cm3.model import CM3LEONTokenizer
class CFG:
SEED: int = 42
SEQ_LEN: int = 8192
NUM_CPU: int = multiprocessing.cpu_count()
HF_ACCOUNT_REPO: str = "YOUR HUGGINGFACE API KEY"
DATASET_NAME: str =... | CM3Leon-main | cm3/tokenize.py |
import torch
# This is the unfused version of StableAdamW. It is slower than the fused version (coming).
class StableAdamWUnfused(torch.optim.Optimizer):
def __init__(
self,
params,
lr=0.002,
weight_decay=0.2,
betas=(0.9, 0.99),
eps=1e-8,
clip_thresh=1.0,
... | CM3Leon-main | cm3/utils/stable_adamw.py |
CM3Leon-main | cm3/utils/__init__.py | |
import unittest
import torch
from softmax_one.softmax_one import ScaledDotProductAttention
class TestScaledDotProductAttention(unittest.TestCase):
def setUp(self):
self.module = ScaledDotProductAttention(dropout=0.1)
self.q = torch.rand(16, 10, 64) #16 batches 10 queries of size 64
self.k ... | AttentionIsOFFByOne-main | test.py |
import torch
from softmax_one.softmax_one import softmax_one
x = torch.randn(5)
y = softmax_one(x, dim=0)
print(y)
print(y.shape) | AttentionIsOFFByOne-main | example.py |
import time
import torch
import argparse
import torch.nn.functional as F
import matplotlib.pyplot as plt
# from softmax_one.softmax_one_cupy import softmax_one_cupy as softmax_one
from softmax_one.softmax_one import softmax_one
import numpy as np
import logging
def benchmark(func, x, dim):
start = time.time()
... | AttentionIsOFFByOne-main | tests/benchmark.py |
from setuptools import setup, Extension
from torch.utils import cpp_extension
softmax_one_cpp = Extension(
name="softmax_one_cpp",
sources=["softmax_one/optimized/softmax_one.cpp", "softmax_one/optimized/binding.cpp"],
include_dirs=["sotmax_one/include"],
extra_compile_args=["-std=c++14"]
)
setup(
... | AttentionIsOFFByOne-main | tests/setup.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from softmax_one.softmax_one import softmax_one
# QuietAttention
class QuietAttention(nn.Module):
def __init__(self, dropout=0.0):
super().__init__()
self.dropout = nn.Droput(dropout)
def forward(self, q, k, v, ma... | AttentionIsOFFByOne-main | softmax_one/attention.py |
import math
import torch
import torch.nn.functional as F
# Define the softmax_one function with added one in the denominator , which helps to reduce
#the negative impact impact of tiny values in the softmax function and improves numerical stability
def softmax_one(x, dim=None, _stacklevel=3, dtype=None):
#subtract... | AttentionIsOFFByOne-main | softmax_one/softmax_one.py |
from softmax_one.softmax_one import softmax_one, ScaledDotProductAttention | AttentionIsOFFByOne-main | softmax_one/__init__.py |
#cupy allows you to compile raw python code into cuda, this a test
import cupy as cp
#softmax
def softmax_one_cupy(x, axis=None):
#substract the max for stability
x = x - cp.max(x, axis=axis, keepdims=True)
#compute exponentials
exp_x = cp.exp(x)
#compute the softmax values and add one in the de... | AttentionIsOFFByOne-main | optimized/softmax_one_cupy.py |
import json
from datetime import datetime
import re
def dmy_to_ymd(d):
return datetime.strptime(d, '%d %b %Y').strftime('%Y-%m-%d')
with open('../README.md', 'r') as f:
lines = f.readlines()
# remove empty line
lines = [line.strip() for line in lines if line.strip()]
st = lines.index('# Resources... | EXA-1-master | exa/papers/Awesome-Diffusion-Models/website/convert_resource.py |
import json
from datetime import datetime
def dmy_to_ymd(d):
return datetime.strptime(d, '%d %b %Y').strftime('%Y-%m-%d')
with open('../README.md', 'r') as f:
lines = f.readlines()
# remove empty line
lines = [line.strip() for line in lines if line.strip()]
idx = lines.index('# Papers')
line... | EXA-1-master | exa/papers/Awesome-Diffusion-Models/website/convert.py |
import itertools
from jinja2 import Template
import json
DOC_DIR = '../docs'
class Link:
def __init__(self, name, href):
self.name = name
self.href = href
class Paper:
def __init__(self, data):
self.title = data['title']
self.authors = data['authors']
self.source = ... | EXA-1-master | exa/papers/Awesome-Diffusion-Models/website/main.py |
# -*- coding: utf-8 -*-
import argparse
import logging
import pprint
from gensim.models import word2vec
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
parser = argparse.ArgumentParser(description='gensim skip-gram with negative sampling')
parser.add_argument('--is_train',... | EXA-1-master | exa/papers/awesome-embedding-models/examples/baseline.py |
# -*- coding: utf-8 -*-
import os
import zipfile
from keras.utils.data_utils import get_file
def maybe_download(url):
"""
Download a file if not present.
"""
filename = url.split('/')[-1]
path = get_file(filename, url)
return path
def read_data(filename):
"""
Extract the first file ... | EXA-1-master | exa/papers/awesome-embedding-models/examples/utils.py |
# -*- coding: utf-8 -*-
import pprint
from keras.utils.data_utils import get_file
from keras.utils import np_utils
from keras.preprocessing.text import Tokenizer, base_filter
from keras.preprocessing.sequence import skipgrams
from keras.models import Sequential
from keras.layers import Dense
from gensim.models.doc2vec ... | EXA-1-master | exa/papers/awesome-embedding-models/examples/skip-gram.py |
# -*- coding: utf-8 -*-
import argparse
import sys
import numpy as np
from gensim.models import word2vec
from gensim.models.doc2vec import Word2Vec
from keras.layers import Activation, Embedding, Merge, Reshape
from keras.models import Sequential
from keras.preprocessing.sequence import skipgrams, make_sampling_table
... | EXA-1-master | exa/papers/awesome-embedding-models/examples/skip-gram_with_ns.py |
# -*- coding:utf-8 -*-
import re
from pprint import pprint
import requests
from bs4 import BeautifulSoup
def get_html(url):
try:
html = requests.get(url).text
except Exception as e:
print('web requests url error: {}\nlink: {}'.format(e, url))
return html
class WebDownloader(object):
... | EXA-1-master | exa/papers/Awesome-Multimodal-Research-master/scripts/WebDownloader.py |
# -*- coding:utf-8 -*-
import re
import requests
import urllib.request
import os
import argparse
parser = argparse.ArgumentParser(description="pull_paper")
parser.add_argument('--keyword', type=str, default='Multimodal') # Match the keywords we want to find the paper
args = parser.parse_args()
# get web context
r =... | EXA-1-master | exa/papers/Awesome-Multimodal-Research-master/scripts/pull_paper.py |
import os
import pandas as pd
from tqdm import tqdm
BASE_URL="https://archive.org/download/stackexchange/"
table = pd.read_html(BASE_URL)[0]
sources = [x.replace(" (View Contents)", "") for x in table['Name'].tolist()]
sources = [x for x in sources if x.endswith(".7z")]
for source in tqdm(sources):
# if ".meta." ... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/stack_exchange/download.py |
EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/stack_exchange/__init__.py | |
import os
import json
LEMMA_DATA_DIR_SE_OUT = os.environ.get("LEMMA_DATA_DIR_SE_OUT", "./data/")
if __name__ == "__main__":
with open(os.path.join(LEMMA_DATA_DIR_SE_OUT,"token_counts", "tokens.json"), "r") as f:
counts = json.load(f)
'''
print a table of the counts
'''
print("|Idx|Site|Tok... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/stack_exchange/print_stats.py |
import os
import json
import tiktoken
from multiprocessing import Pool
from transformers import AutoTokenizer
# enc = tiktoken.get_encoding("r50k_base")
enc = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-6.9b-deduped",
# "gpt2"
)
def get_token_count(qa_pair):
# return len(enc.encode(qa_pair['text']))
... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/stack_exchange/token_count.py |
import os
import json
import sys
import xml.etree.ElementTree as ET
from tqdm import tqdm
sys.path.append("./")
from src.stack_exchange.count import get_sites_count
LEMMA_DATA_DIR_SE = os.environ.get("LEMMA_DATA_DIR_SE", "./data/")
if os.path.exists(os.path.join(LEMMA_DATA_DIR_SE, "counts.json")):
with open(os.pa... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/stack_exchange/filter.py |
import re
import os
import sys
import json
import fasttext
from bs4 import BeautifulSoup
from multiprocessing import Pool
sys.path.append("./")
site_name = ""
CLEANR = re.compile('<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});')
def cleanhtml(raw_html):
raw_html = raw_html.replace("<li>", "\n*")
raw_html = ... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/stack_exchange/post_processing.py |
import os
import json
from tqdm import tqdm
import xml.etree.ElementTree as ET
LEMMA_DATA_DIR_SE = os.environ.get("LEMMA_DATA_DIR_SE", "./data/stack_exchange/")
def get_sites_count(path=LEMMA_DATA_DIR_SE):
sites = os.listdir(path)
sites = [x for x in sites if x.endswith(".xml")]
counts = {}
for site i... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/stack_exchange/count.py |
import argparse
from datasets import load_dataset
import pathlib
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", type=str, default=None,
help="Path to the wikipedia data directory.")
args = parser.parse_args()
LANGUAGES = [
"bg", "ca", "cs", "da", "de", "en", "es", "fr", "... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/wiki/download.py |
EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/wiki/__init__.py | |
import os
import json
from multiprocessing import Pool
from transformers import AutoTokenizer
print("start loading!")
enc = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-6.9b-deduped",
)
print("end loading!")
def get_token_count(qa_pair):
return len(enc.tokenize(qa_pair['text']))
LEMMA_DATA_DIR_SE_OUT = ".... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/wiki/token_count.py |
import os
import json
LEMMA_DATA_DIR_SE_OUT = "./data/wikipedia/"
LEMMA_DATA_SAVE_DIR = "./data/wikipedia/wiki-full.jsonl"
files = [x for x in os.listdir(os.path.join(LEMMA_DATA_DIR_SE_OUT)) if os.path.isfile(os.path.join(LEMMA_DATA_DIR_SE_OUT, x))]
files.sort()
with open(LEMMA_DATA_SAVE_DIR, "w") as fw:
for fil... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/wiki/convert_format.py |
import argparse
import hashlib
import gzip
import json
import re
import uuid
from datetime import datetime
from typing import Dict, Union
import pathlib
parser = argparse.ArgumentParser()
parser.add_argument('--input', type=str, default=None)
parser.add_argument('--target_dir', type=str,
default="... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/github/github_clean_dedup_local.py |
EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/github/__init__.py | |
import argparse
from datetime import datetime
import json
import multiprocessing as mp
import os
import gzip
from transformers import AutoTokenizer
import pathlib
parser = argparse.ArgumentParser()
parser.add_argument('--data_file', type=str, default=None)
parser.add_argument('--target_dir', type=str, default=None)
a... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/github/github_run_filter.py |
import argparse
import os
from transformers import AutoTokenizer
import json
import multiprocessing as mp
import pathlib
from datetime import datetime
parser = argparse.ArgumentParser()
parser.add_argument('--data_file', type=str, default=None)
args = parser.parse_args()
tokenizer = AutoTokenizer.from_pretrained("Ele... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/github/github_token_count.py |
import argparse
import json
from datetime import datetime
from typing import Dict
import pathlib
parser = argparse.ArgumentParser()
parser.add_argument('--first_step_dir', type=str, default=None)
parser.add_argument('--target_dir', type=str, default=None)
args = parser.parse_args()
def get_timestamp() -> str:
r... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/github/github_global_dedup.py |
import argparse
import json
from datetime import datetime
import pathlib
parser = argparse.ArgumentParser()
parser.add_argument(
'--first_step_dir', type=str,
default="./data/github/processed_v3"
)
parser.add_argument(
'--input', type=str,
default="data/github/processed_v3/run_ce60fbbc14684ed8b6590548... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/github/github_merge_dedup.py |
from datasets import load_dataset
book_dataset = load_dataset("the_pile_books3")
for split, dataset in book_dataset.items():
dataset.to_json(f"./data/book/books3-{split}.jsonl")
pg19_dataset = load_dataset("pg19")
for split, dataset in pg19_dataset.items():
dataset.to_json(f"./data/book/pg19-{split}.jsonl") | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/book/download.py |
EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/book/__init__.py | |
# Copyright 2023 Ontocord.ai, Together Computer, ETH Zürich, Stanford University
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/book/dedup.py |
import os
import json
from multiprocessing import Pool
from transformers import AutoTokenizer
enc = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-6.9b-deduped",
)
def get_token_count(qa_pair):
return len(enc.tokenize(qa_pair['text']))
LEMMA_DATA_DIR_SE_OUT = "./data/book/"
sites = [x for x in os.listdir(o... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/book/token_count.py |
import argparse
from datetime import datetime
import json
import gzip
import os
import pathlib
import joblib
from joblib import Parallel, delayed
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default="./data/c4/en")
parser.add_argument('--output_dir', type=str, default="./data/c4/proce... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/c4/c4_reformat.py |
EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/c4/__init__.py | |
import argparse
import boto3
from botocore.exceptions import ClientError
import configparser
import itertools
import numpy as np
import pathlib
parser = argparse.ArgumentParser()
parser.add_argument('--aws_config', type=str, help='aws config file')
parser.add_argument('--target_dir', type=str, default="./data/arxiv")
... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/arxiv/run_download.py |
import concurrent.futures
from datetime import datetime
import fasttext
import json
import pathlib
import tarfile
from typing import List, Tuple, Dict, Union
import gzip
import tempfile
import uuid
import re
from utils import predict_lang, get_timestamp, format_arxiv_id
# suppress fasttext warning
fasttext.FastText.e... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/arxiv/arxiv_cleaner.py |
from datetime import datetime
import fasttext
import re
from typing import List, Tuple
def get_timestamp() -> str:
return datetime.now().isoformat()
def predict_lang(
text: str, lang_model: fasttext.FastText._FastText, k=5
) -> Tuple[List[str], List[float]]:
r""" Predict top-k languages of text.
... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/arxiv/utils.py |
import argparse
import os
from collections import defaultdict
from datetime import datetime
from transformers import AutoTokenizer
import json
import multiprocessing as mp
import pathlib
import pandas as pd
from tabulate import tabulate
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, def... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/arxiv/token_count.py |
import argparse
import os
import uuid
import numpy as np
import pathlib
import tempfile
from typing import List
import joblib
from arxiv_cleaner import ArxivCleaner
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default="./data/arxiv/src")
parser.add_argument('--target_dir', type=str,... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/arxiv/run_clean.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
from setuptools import setup # type: ignore
setup(
name="cc_net",
version="1.0.0",
pa... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Main script to download a CC dump, remove duplicates, split by language and
filter the documents.
The pipeline parameters are described... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/mine.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Creates mono-lingual corpus from Wikipedia.
"""
import functools
import re
import subprocess
import urllib.request
from pathlib import ... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/get_wiki_cirrus.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Manipulate files containing one json per line.
"""
import argparse
import collections
import contextlib
import functools
import glob
imp... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/jsonql.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import functools
import itertools
import logging
import os
import sys
import time
import warnings
from pathlib import Path
from typing impor... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/execution.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import sys
import time
import warnings
from typing import Iterable, Iterator, Sequence, Sized, Tuple, Type
import numpy as np
HASH_TYPE: T... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/flat_hash_set.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import base64
import hashlib
import itertools
import urllib.parse
from pathlib import Path
from typing import Dict, Iterable, List, Optional... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/minify.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import re
import unicodedata
UNICODE_PUNCT = {
"，": ",",
"。": ".",
"、": ",",
"„": '"',
"”": '"',
"“": '"',
"«":... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/text_normalizer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import logging
import subprocess
from pathlib import Path
from typing import List
import func_argparse
import numpy as np
from cc_net impo... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/regroup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import time
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Tuple, Union
import kenlm # type: ... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/perplexity.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
| EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import time
from typing import Dict, Optional
import sacremoses # type: ignore
from cc_net import jsonql, text_normalizer
class RobustT... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/tokenizer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Tools to remove duplicate paragraphs across one or several shards.
"""
import argparse
import gc
import hashlib
import logging
import m... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/dedup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import contextlib
import functools
import logging
import re
import tempfile
import time
import urllib.request
from pathlib import Path
from ... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/process_wet_file.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import func_argparse
import cc_net.mine
def main():
func_argparse.parse_and_call(cc_net.mine.get_main_parser())
if __name__ == "__... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/__main__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import collections
from pathlib import Path
from typing import Dict, Optional
import fasttext # type: igno... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/split_by_lang.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import contextlib
import functools
import gzip
import logging
import multiprocessing
from collections import defaultdict
from pathlib import... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/tools/dl_cc_100.py |
EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/tools/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
"""
This code is used to train a fastText classifier to label document with DMOZ categories.
The data, distributed under the cc-by 3.0 lice... | EXA-1-master | exa/datasets/RedPajama-Data-main/data_prep/cc/cc_net/cc_net/tools/make_dmoz_corpus.py |
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