text stringlengths 5 22M | id stringlengths 12 177 | metadata dict | __index_level_0__ int64 0 1.37k |
|---|---|---|---|
class TREError extends Error {
constructor() {
super();
Object.setPrototypeOf(this, new.target.prototype);
}
}
export class APIError extends TREError {
status?: number;
exception?: any;
userMessage?: string;
endpoint?: string;
}
| AzureTRE/ui/app/src/models/exceptions.ts/0 | {
"file_path": "AzureTRE/ui/app/src/models/exceptions.ts",
"repo_id": "AzureTRE",
"token_count": 79
} | 142 |
import { configureStore } from "@reduxjs/toolkit";
import operationsReducer from "../components/shared/notifications/operationsSlice";
export const store = configureStore({
reducer: {
operations: operationsReducer
}
});
export type AppDispatch = typeof store.dispatch;
export type RootState = ReturnType<typeof... | AzureTRE/ui/app/src/store/store.ts/0 | {
"file_path": "AzureTRE/ui/app/src/store/store.ts",
"repo_id": "AzureTRE",
"token_count": 96
} | 143 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import argparse
from fairseq.models.transformer_lm import TransformerLanguageModel
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", type=str, default="../../data/PubMed/data-bin")
parser.add_argument("--model_dir", type=str, ... | BioGPT/examples/text-generation/interactive.py/0 | {
"file_path": "BioGPT/examples/text-generation/interactive.py",
"repo_id": "BioGPT",
"token_count": 656
} | 144 |
# Transparency FAQ for BitBLAS
## What is BitBLAS?
BitBLAS is a lightweight framework designed for generating high-performance CUDA/HIP code for BLAS (Basic Linear Algebra Subprograms) operators, emphasizing swizzling and layout propagation. It leverages a Domain-Specific Language (DSL), specifically TIR Script, to o... | BitBLAS/TRANSPARENCY.md/0 | {
"file_path": "BitBLAS/TRANSPARENCY.md",
"repo_id": "BitBLAS",
"token_count": 870
} | 145 |
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to G... | BitBLAS/integration/BitNet/modeling_bitnet.py/0 | {
"file_path": "BitBLAS/integration/BitNet/modeling_bitnet.py",
"repo_id": "BitBLAS",
"token_count": 27678
} | 146 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""Base infra"""
from .analysis import (
BlockInfo,
IterInfo,
collect_block_iter_vars_used_in_access_region,
collect_vars_used_in_prim_expr,
detect_dominant_read,
is_broadcast_epilogue,
normalize_prim_func,
)
from .com... | BitBLAS/python/bitblas/base/__init__.py/0 | {
"file_path": "BitBLAS/python/bitblas/base/__init__.py",
"repo_id": "BitBLAS",
"token_count": 202
} | 147 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from .tir import get_analyzer_by_tir # pylint: disable=unused-import
| BitBLAS/python/bitblas/base/roller/shape_inference/__init__.py/0 | {
"file_path": "BitBLAS/python/bitblas/base/roller/shape_inference/__init__.py",
"repo_id": "BitBLAS",
"token_count": 44
} | 148 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from .lop3 import get_lop3_intrin_group # noqa: F401
| BitBLAS/python/bitblas/gpu/intrin/__init__.py/0 | {
"file_path": "BitBLAS/python/bitblas/gpu/intrin/__init__.py",
"repo_id": "BitBLAS",
"token_count": 41
} | 149 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from bitblas.gpu.matmul_analysis import get_propagate_map
from typing import Literal
from tvm import te, IRModule, DataType
from tvm.tir import IndexMap
def select_implementation(
M: int,
N: int,
datatype: Literal["float16", "int8", ... | BitBLAS/python/bitblas/ops/impl/ladder_permutate_impl.py/0 | {
"file_path": "BitBLAS/python/bitblas/ops/impl/ladder_permutate_impl.py",
"repo_id": "BitBLAS",
"token_count": 1419
} | 150 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from typing import Dict, Tuple
from tvm.ir import IRModule
from tvm.ir.transform import PassContext, module_pass
from tvm import tir
from tvm.tir.schedule import BlockRV
from mlc_llm.quantization import quantization_schemes, GroupQuantizationSpec
... | BitBLAS/python/bitblas/relax/transform/annotate_decode_block.py/0 | {
"file_path": "BitBLAS/python/bitblas/relax/transform/annotate_decode_block.py",
"repo_id": "BitBLAS",
"token_count": 2169
} | 151 |
// Copyright (c) Microsoft Corporation.
// Licensed under the MIT License.
#include <cuda_runtime.h>
#include <cuda_fp16.h>
// Pack two half values.
static inline __device__ __host__ unsigned
__pack_half2(const half x, const half y)
{
unsigned v0 = *((unsigned short *)&x);
unsigned v1 = *((unsigned short *)&y)... | BitBLAS/testing/cpp/lop3_type_conversion/fast_decoding.hpp/0 | {
"file_path": "BitBLAS/testing/cpp/lop3_type_conversion/fast_decoding.hpp",
"repo_id": "BitBLAS",
"token_count": 18164
} | 152 |
from .vg_caption_datamodule import VisualGenomeCaptionDataModule
from .f30k_caption_karpathy_datamodule import F30KCaptionKarpathyDataModule
from .coco_caption_karpathy_datamodule import CocoCaptionKarpathyDataModule
from .conceptual_caption_datamodule import ConceptualCaptionDataModule
from .sbu_datamodule import SBUC... | BridgeTower/src/datamodules/__init__.py/0 | {
"file_path": "BridgeTower/src/datamodules/__init__.py",
"repo_id": "BridgeTower",
"token_count": 303
} | 153 |
from .base_dataset import BaseDataset
import sys
import random
class NLVR2Dataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if split == "train":
names = ["nlvr2_train"]
elif split == "val":
... | BridgeTower/src/datasets/nlvr2_dataset.py/0 | {
"file_path": "BridgeTower/src/datasets/nlvr2_dataset.py",
"repo_id": "BridgeTower",
"token_count": 872
} | 154 |
""" Model creation / weight loading / state_dict helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
import math
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Callable, Optional, Tuple
import torch
import torch.nn as nn
from timm.models.featu... | BridgeTower/src/modules/swin_helpers.py/0 | {
"file_path": "BridgeTower/src/modules/swin_helpers.py",
"repo_id": "BridgeTower",
"token_count": 9842
} | 155 |
import json
import pandas as pd
import pyarrow as pa
import os
from tqdm import tqdm
from collections import defaultdict
label2id = {'contradiction': 0, 'neutral': 1, 'entailment': 2}
def process(root, imgid, ann):
with open(f"{root}/flickr30k-images/{imgid}.jpg", "rb") as fp:
img = fp.read()
senten... | BridgeTower/src/utils/write_snli.py/0 | {
"file_path": "BridgeTower/src/utils/write_snli.py",
"repo_id": "BridgeTower",
"token_count": 999
} | 156 |
FROM nvidia/cuda:11.1-base-ubuntu20.04
RUN apt update && DEBIAN_FRONTEND=noninteractive apt install git bzip2 wget unzip python3-pip python3-dev cmake libgl1-mesa-dev python-is-python3 libgtk2.0-dev -yq
ADD . /app
WORKDIR /app
RUN cd Face_Enhancement/models/networks/ &&\
git clone https://github.com/vacancy/Synchron... | Bringing-Old-Photos-Back-to-Life/Dockerfile/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Dockerfile",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 540
} | 157 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.networks.base_network import BaseNetwork
from models.networks.normalization import get_nonspade_norm_layer
from models.networks.architecture import ResnetBlock as Resn... | Bringing-Old-Photos-Back-to-Life/Face_Enhancement/models/networks/generator.py/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Face_Enhancement/models/networks/generator.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 4241
} | 158 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
class BaseDataLoader():
def __init__(self):
pass
def initialize(self, opt):
self.opt = opt
pass
def load_data():
return None
| Bringing-Old-Photos-Back-to-Life/Global/data/base_data_loader.py/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/data/base_data_loader.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 119
} | 159 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import functools
from torch.autograd import Variable
import numpy as np
from torch.nn.utils import spectral_norm
# from util.util import SwitchNorm2d
import torch.nn.functional as F
###########################... | Bringing-Old-Photos-Back-to-Life/Global/models/networks.py/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/models/networks.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 16443
} | 160 |
# Old Photo Restoration (Official PyTorch Implementation)
<img src='imgs/0001.jpg'/>
### [Project Page](http://raywzy.com/Old_Photo/) | [Paper (CVPR version)](https://arxiv.org/abs/2004.09484) | [Paper (Journal version)](https://arxiv.org/pdf/2009.07047v1.pdf) | [Pretrained Model](https://hkustconnect-my.sharepoint.c... | Bringing-Old-Photos-Back-to-Life/README.md/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/README.md",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 4200
} | 161 |
apiVersion: v1
kind: Pod
metadata:
name: photo-back2life
spec:
containers:
- name: photos-back2life
image: <YOUR IMAGE>
volumeMounts:
- mountPath: /in
name: in-folder
- mountPath: /out
name: out-folder
command:
- python
- /app/run.py
args:
... | Bringing-Old-Photos-Back-to-Life/kubernetes-pod.yml/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/kubernetes-pod.yml",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 393
} | 162 |
import torch
import torch.nn as nn
from torch.nn import functional as nnf
from enum import Enum
from transformers import GPT2LMHeadModel
from typing import Tuple, Optional
def get_clapcap(name: str):
if name == "ClapCaption":
return ClapCaptionModel
else:
raise Exception('The ClapCap model {} ... | CLAP/msclap/models/mapper.py/0 | {
"file_path": "CLAP/msclap/models/mapper.py",
"repo_id": "CLAP",
"token_count": 4039
} | 163 |
## Hydra
[Hydra](https://github.com/facebookresearch/hydra) is an open-source Python
framework that simplifies the development of research and other complex
applications. The key feature is the ability to dynamically create a
hierarchical configuration by composition and override it through config files
and the comman... | COCO-LM/fairseq/docs/hydra_integration.md/0 | {
"file_path": "COCO-LM/fairseq/docs/hydra_integration.md",
"repo_id": "COCO-LM",
"token_count": 2942
} | 164 |
# 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.
from torch.optim import Adagrad
from fairseq.optim import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("adagrad_with_grad... | COCO-LM/fairseq/examples/adaptive_span/adagrad_with_grad_clip.py/0 | {
"file_path": "COCO-LM/fairseq/examples/adaptive_span/adagrad_with_grad_clip.py",
"repo_id": "COCO-LM",
"token_count": 2256
} | 165 |
# Neural Machine Translation with Byte-Level Subwords
https://arxiv.org/abs/1909.03341
We provide an implementation of byte-level byte-pair encoding (BBPE), taking IWSLT 2017 Fr-En translation as
example.
## Data
Get data and generate fairseq binary dataset:
```bash
bash ./get_data.sh
```
## Model Training
Train Tr... | COCO-LM/fairseq/examples/byte_level_bpe/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/byte_level_bpe/README.md",
"repo_id": "COCO-LM",
"token_count": 1325
} | 166 |
#!/bin/bash
# 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.
#
source_lang=kk_KZ
target_lang=en_XX
MODEL=criss_checkpoints/criss.3rd.pt
SPM=criss_checkpoints/sentence.bpe.mo... | COCO-LM/fairseq/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh/0 | {
"file_path": "COCO-LM/fairseq/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh",
"repo_id": "COCO-LM",
"token_count": 729
} | 167 |
# LASER Language-Agnostic SEntence Representations
LASER is a library to calculate and use multilingual sentence embeddings.
You can find more information about LASER and how to use it on the official [LASER repository](https://github.com/facebookresearch/LASER).
This folder contains source code for training LASER ... | COCO-LM/fairseq/examples/laser/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/laser/README.md",
"repo_id": "COCO-LM",
"token_count": 1998
} | 168 |
# Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)
This page contains information for how to train models with LayerDrop, based on this [paper](https://arxiv.org/abs/1909.11556).
## Citation:
If you found this technique useful, please cite our paper:
```bibtex
@article{fan2019reducing,
... | COCO-LM/fairseq/examples/layerdrop/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/layerdrop/README.md",
"repo_id": "COCO-LM",
"token_count": 2747
} | 169 |
# 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 os
import argparse
import pandas as pd
import sys
WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
if WORKDIR_ROOT is None or n... | COCO-LM/fairseq/examples/multilingual/data_scripts/check_valid_test_overlaps.py/0 | {
"file_path": "COCO-LM/fairseq/examples/multilingual/data_scripts/check_valid_test_overlaps.py",
"repo_id": "COCO-LM",
"token_count": 2019
} | 170 |
# 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.
#!/bin/python
import fasttext
from multiprocessing import Pool
import contextlib
import sys
import argparse
from functools import partial
im... | COCO-LM/fairseq/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py/0 | {
"file_path": "COCO-LM/fairseq/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py",
"repo_id": "COCO-LM",
"token_count": 1020
} | 171 |
# Paraphrasing with round-trip translation and mixture of experts
Machine translation models can be used to paraphrase text by translating it to
an intermediate language and back (round-trip translation).
This example shows how to paraphrase text by first passing it to an
English-French translation model, followed by... | COCO-LM/fairseq/examples/paraphraser/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/paraphraser/README.md",
"repo_id": "COCO-LM",
"token_count": 757
} | 172 |
# Finetuning RoBERTa on Commonsense QA
We follow a similar approach to [finetuning RACE](../README.race.md). Specifically
for each question we construct five inputs, one for each of the five candidate
answer choices. Each input is constructed by concatenating the question and
candidate answer. We then encode each inpu... | COCO-LM/fairseq/examples/roberta/commonsense_qa/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/roberta/commonsense_qa/README.md",
"repo_id": "COCO-LM",
"token_count": 1525
} | 173 |
# 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 math
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, ... | COCO-LM/fairseq/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py/0 | {
"file_path": "COCO-LM/fairseq/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py",
"repo_id": "COCO-LM",
"token_count": 2934
} | 174 |
# 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.
from . import register_scorer
from .scorer import SimulScorer
@register_scorer("text")
class SimulTextScorer(SimulScorer):
def __init__(... | COCO-LM/fairseq/examples/simultaneous_translation/eval/scorers/text_scorer.py/0 | {
"file_path": "COCO-LM/fairseq/examples/simultaneous_translation/eval/scorers/text_scorer.py",
"repo_id": "COCO-LM",
"token_count": 644
} | 175 |
import importlib
import os
# ASG loss requires flashlight bindings
files_to_skip = set()
try:
import flashlight.lib.sequence.criterion
except ImportError:
files_to_skip.add("ASG_loss.py")
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_") and file not ... | COCO-LM/fairseq/examples/speech_recognition/criterions/__init__.py/0 | {
"file_path": "COCO-LM/fairseq/examples/speech_recognition/criterions/__init__.py",
"repo_id": "COCO-LM",
"token_count": 197
} | 176 |
# 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 math
from collections.abc import Iterable
import torch
import torch.nn as nn
from examples.speech_recognition.data.dat... | COCO-LM/fairseq/examples/speech_recognition/models/vggtransformer.py/0 | {
"file_path": "COCO-LM/fairseq/examples/speech_recognition/models/vggtransformer.py",
"repo_id": "COCO-LM",
"token_count": 17782
} | 177 |
#!/usr/bin/env python3
# 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 logging
import os
from pathlib import Path
import shutil
from itertools import groupby
from temp... | COCO-LM/fairseq/examples/speech_to_text/prep_mustc_data.py/0 | {
"file_path": "COCO-LM/fairseq/examples/speech_to_text/prep_mustc_data.py",
"repo_id": "COCO-LM",
"token_count": 5033
} | 178 |
# 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 numpy as np
import torch
from fairseq.data import Dictionary, FairseqDataset
from fairseq.tasks import LegacyFairseqTas... | COCO-LM/fairseq/fairseq/benchmark/dummy_masked_lm.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/benchmark/dummy_masked_lm.py",
"repo_id": "COCO-LM",
"token_count": 1793
} | 179 |
# 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 inspect
from typing import Any, Dict, List
from fairseq import metrics, utils
from fairseq.dataclass import FairseqDataclass
from fair... | COCO-LM/fairseq/fairseq/criterions/fairseq_criterion.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/criterions/fairseq_criterion.py",
"repo_id": "COCO-LM",
"token_count": 1954
} | 180 |
from pathlib import Path
from typing import BinaryIO, Optional, Tuple, Union
import numpy as np
import torch
SF_AUDIO_FILE_EXTENSIONS = {".wav", ".flac", ".ogg"}
def _convert_to_mono(
waveform: torch.FloatTensor, sample_rate: int
) -> torch.FloatTensor:
if waveform.shape[0] > 1:
try:
... | COCO-LM/fairseq/fairseq/data/audio/audio_utils.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/audio/audio_utils.py",
"repo_id": "COCO-LM",
"token_count": 1918
} | 181 |
# 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 math
import numpy as np
import torch
from . import FairseqDataset, data_utils
def collate(
samples,
pad_idx,
eos_idx,
... | COCO-LM/fairseq/fairseq/data/denoising_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/denoising_dataset.py",
"repo_id": "COCO-LM",
"token_count": 7651
} | 182 |
# 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.
from dataclasses import dataclass, field
from fairseq import file_utils
from fairseq.data.encoders import register_bpe
from fairseq.dataclass... | COCO-LM/fairseq/fairseq/data/encoders/subword_nmt_bpe.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/encoders/subword_nmt_bpe.py",
"repo_id": "COCO-LM",
"token_count": 882
} | 183 |
# 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 numpy as np
import torch
from . import FairseqDataset, data_utils
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
... | COCO-LM/fairseq/fairseq/data/monolingual_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/monolingual_dataset.py",
"repo_id": "COCO-LM",
"token_count": 4007
} | 184 |
# 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 numpy as np
import torch
from . import BaseWrapperDataset
class PrependDataset(BaseWrapperDataset):
def __init__(self, dataset, ... | COCO-LM/fairseq/fairseq/data/prepend_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/prepend_dataset.py",
"repo_id": "COCO-LM",
"token_count": 387
} | 185 |
# 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 numpy as np
import torch
from fairseq.data import FairseqDataset, plasma_utils
from fairseq.data.indexed_dataset import best_fitting_in... | COCO-LM/fairseq/fairseq/data/token_block_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/token_block_dataset.py",
"repo_id": "COCO-LM",
"token_count": 3345
} | 186 |
# 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 io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse imp... | COCO-LM/fairseq/fairseq/distributed/utils.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/distributed/utils.py",
"repo_id": "COCO-LM",
"token_count": 13032
} | 187 |
# 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 math
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options... | COCO-LM/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py",
"repo_id": "COCO-LM",
"token_count": 11018
} | 188 |
# 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 os
import signal
import threading
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParal... | COCO-LM/fairseq/fairseq/models/distributed_fairseq_model.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/distributed_fairseq_model.py",
"repo_id": "COCO-LM",
"token_count": 2076
} | 189 |
# 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.
from collections import OrderedDict
from fairseq import utils
from fairseq.models import (
FairseqMultiModel,
register_model,
reg... | COCO-LM/fairseq/fairseq/models/multilingual_transformer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/multilingual_transformer.py",
"repo_id": "COCO-LM",
"token_count": 4430
} | 190 |
# 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.
"""
GottBERT: a pure German Language Model
"""
from fairseq.models import register_model
from .hub_interface import RobertaHubInterface
from ... | COCO-LM/fairseq/fairseq/models/roberta/model_gottbert.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/roberta/model_gottbert.py",
"repo_id": "COCO-LM",
"token_count": 790
} | 191 |
# 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.
from dataclasses import dataclass, field
import logging
import math
from typing import Optional, Tuple
from omegaconf import II
import sys
im... | COCO-LM/fairseq/fairseq/models/wav2vec/wav2vec.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/wav2vec/wav2vec.py",
"repo_id": "COCO-LM",
"token_count": 10600
} | 192 |
# 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.
def gen_forward():
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
blocks = [32, 64, 128, 256]
head = """
/**
* Copyright (c) Facebo... | COCO-LM/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py",
"repo_id": "COCO-LM",
"token_count": 3476
} | 193 |
# 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 numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
class PQConv2... | COCO-LM/fairseq/fairseq/modules/quantization/pq/modules/qconv.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/quantization/pq/modules/qconv.py",
"repo_id": "COCO-LM",
"token_count": 1860
} | 194 |
# 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 math
from typing import Any, Optional
import torch
import torch.onnx.operators
from fairseq import utils
from torch import Tensor, nn
... | COCO-LM/fairseq/fairseq/modules/sinusoidal_positional_embedding.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/sinusoidal_positional_embedding.py",
"repo_id": "COCO-LM",
"token_count": 1835
} | 195 |
# 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 math
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
import tor... | COCO-LM/fairseq/fairseq/optim/adam.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/optim/adam.py",
"repo_id": "COCO-LM",
"token_count": 4083
} | 196 |
# 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.
from dataclasses import dataclass
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, r... | COCO-LM/fairseq/fairseq/optim/lr_scheduler/pass_through.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/optim/lr_scheduler/pass_through.py",
"repo_id": "COCO-LM",
"token_count": 507
} | 197 |
# 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.
from dataclasses import dataclass, field
from fairseq.dataclass import FairseqDataclass
from fairseq.scoring import BaseScorer, register_scor... | COCO-LM/fairseq/fairseq/scoring/wer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/scoring/wer.py",
"repo_id": "COCO-LM",
"token_count": 796
} | 198 |
# 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 os
import numpy as np
from fairseq import utils
from fairseq.data import (
ConcatSentencesDataset,
Dictionary,
... | COCO-LM/fairseq/fairseq/tasks/sentence_prediction.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/tasks/sentence_prediction.py",
"repo_id": "COCO-LM",
"token_count": 4712
} | 199 |
#!/usr/bin/env python3 -u
# 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.
"""
Evaluate the perplexity of a trained language model.
"""
import logging
import math
import os
import sys
from a... | COCO-LM/fairseq/fairseq_cli/eval_lm.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq_cli/eval_lm.py",
"repo_id": "COCO-LM",
"token_count": 5811
} | 200 |
#include <ATen/ATen.h>
#include "compat.h"
// Forward/backward compatiblity hack around
// https://github.com/pytorch/pytorch/commit/3aeb78079bcd68282fe9117088e138b77318e288
// pending more future-proof guidance from upstream.
// struct TypeShim
// {
// const at::Type& payload;
// TypeShim(const at::Type& type) : ... | COCO-LM/fairseq/fused_ops/csrc/type_shim.h/0 | {
"file_path": "COCO-LM/fairseq/fused_ops/csrc/type_shim.h",
"repo_id": "COCO-LM",
"token_count": 3042
} | 201 |
#!/usr/bin/env bash
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# GLUE task name, from ['MNLI', 'QQP', 'QNLI', 'SST-2', 'CoLA', 'RTE', 'MRPC', 'STS-B']
TASK=$1
# Path to pretrained COCO-LM checkpoints
PRETRAINED_MODEL_PATH=$2
# Path to processed GLUE dataset (containing binary files) 'p... | COCO-LM/fairseq/run_glue.sh/0 | {
"file_path": "COCO-LM/fairseq/run_glue.sh",
"repo_id": "COCO-LM",
"token_count": 1738
} | 202 |
#!/usr/bin/env python3
# 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.
"""
Split a large file into a train and valid set while respecting document
boundaries. Documents should be separated by... | COCO-LM/fairseq/scripts/split_train_valid_docs.py/0 | {
"file_path": "COCO-LM/fairseq/scripts/split_train_valid_docs.py",
"repo_id": "COCO-LM",
"token_count": 1183
} | 203 |
# 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 unittest
from typing import Sequence
from fairseq.data import LanguagePairDataset, ListDataset, RoundRobinZipDatasets
f... | COCO-LM/fairseq/tests/test_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_dataset.py",
"repo_id": "COCO-LM",
"token_count": 1257
} | 204 |
# 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 unittest
from typing import Dict, List
import tests.utils as test_utils
import torch
from fairseq import utils
from fairseq.data impor... | COCO-LM/fairseq/tests/test_noising.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_noising.py",
"repo_id": "COCO-LM",
"token_count": 10290
} | 205 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# The script is largely adapted from the huggingface transformers library
import re
import os
import unicodedata
from transformers.tokenization_utils import PreTrainedTokenizer
from cocolm.tokenization_utils import Dictionary
def _is_punctuat... | COCO-LM/huggingface/cocolm/tokenization_cocolm.py/0 | {
"file_path": "COCO-LM/huggingface/cocolm/tokenization_cocolm.py",
"repo_id": "COCO-LM",
"token_count": 4756
} | 206 |
# ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
from torch.utils.data import Dataset
import numpy as np
import io
from PIL import Image
import os
import json
i... | CSWin-Transformer/labeled_memcached_dataset.py/0 | {
"file_path": "CSWin-Transformer/labeled_memcached_dataset.py",
"repo_id": "CSWin-Transformer",
"token_count": 764
} | 207 |
seed_everything: 42
# ---------------------------- TRAINER -------------------------------------------
trainer:
default_root_dir: ${oc.env:AMLT_OUTPUT_DIR,/home/tungnd/ClimaX/exps/climate_projection_climax}
precision: 16
gpus: null
num_nodes: 1
accelerator: gpu
strategy: ddp
min_epochs: 1
max_epochs... | ClimaX/configs/climate_projection.yaml/0 | {
"file_path": "ClimaX/configs/climate_projection.yaml",
"repo_id": "ClimaX",
"token_count": 1367
} | 208 |
# Installation Guide
```bash title="clone the repo"
git clone https://github.com/microsoft/ClimaX
```
=== "`conda`"
```bash title="create and activate env"
cd ClimaX
conda env create --file docker/environment.yml
conda activate climaX
```
```bash title="install this package"
# install s... | ClimaX/docs/install.md/0 | {
"file_path": "ClimaX/docs/install.md",
"repo_id": "ClimaX",
"token_count": 440
} | 209 |
datadir: /data/CMIP6/AWI-ESM
name: v_component_of_wind
cmip_name: va
era_name: v
run: r1i1p1f1
res:
- 1.40625
# - 5.625 | ClimaX/snakemake_configs/AWI-ESM/config_v_component_of_wind.yml/0 | {
"file_path": "ClimaX/snakemake_configs/AWI-ESM/config_v_component_of_wind.yml",
"repo_id": "ClimaX",
"token_count": 67
} | 210 |
datadir: /data/CMIP6/MPI-ESM
server_prefix: https://esgf.ceda.ac.uk/thredds/fileServer/esg_cmip6/CMIP6/CMIP
name: 10m_u_component_of_wind
cmip_name: uas
era_name: u10
output_type: 6hrPlevPt
run: r1i1p1f1
version: v20190710
res:
- 1.40625
# - 5.625 | ClimaX/snakemake_configs/MPI-ESM/config_10m_u_component_of_wind.yml/0 | {
"file_path": "ClimaX/snakemake_configs/MPI-ESM/config_10m_u_component_of_wind.yml",
"repo_id": "ClimaX",
"token_count": 128
} | 211 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from functools import lru_cache
import numpy as np
import torch
import torch.nn as nn
from timm.models.vision_transformer import Block, PatchEmbed, trunc_normal_
from climax.utils.pos_embed import (
get_1d_sincos_pos_embed_from_grid,
ge... | ClimaX/src/climax/arch.py/0 | {
"file_path": "ClimaX/src/climax/arch.py",
"repo_id": "ClimaX",
"token_count": 4699
} | 212 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
from pytorch_lightning.cli import LightningCLI
from climax.pretrain.datamodule import MultiSourceDataModule
from climax.pretrain.module import PretrainModule
def main():
# Initialize Lightning with the model and data modules, an... | ClimaX/src/climax/pretrain/train.py/0 | {
"file_path": "ClimaX/src/climax/pretrain/train.py",
"repo_id": "ClimaX",
"token_count": 349
} | 213 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import importlib
import torch.utils.data
from data.base_dataset import BaseDataset
def find_dataset_using_name(dataset_name):
dataset_filename = "data." + dataset_name + "_dataset"
datasetlib = importlib.import_module(dataset_filename)
... | CoCosNet-v2/data/__init__.py/0 | {
"file_path": "CoCosNet-v2/data/__init__.py",
"repo_id": "CoCosNet-v2",
"token_count": 608
} | 214 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
from models.networks.base_network import BaseNetwork
from models.networks.architecture import SPADEResnetBlock
class SPADEGenerator(BaseNetw... | CoCosNet-v2/models/networks/generator.py/0 | {
"file_path": "CoCosNet-v2/models/networks/generator.py",
"repo_id": "CoCosNet-v2",
"token_count": 1128
} | 215 |
# -*- coding: utf-8 -*-
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
import numpy as np
from more_itertools import chunked
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--test_batch_size', type=int, default=1000)
args = parser.p... | CodeBERT/CodeBERT/codesearch/mrr.py/0 | {
"file_path": "CodeBERT/CodeBERT/codesearch/mrr.py",
"repo_id": "CodeBERT",
"token_count": 676
} | 216 |
PER_NODE_GPU=8
python -m torch.distributed.launch --nproc_per_node=${PER_NODE_GPU} run.py \
--output_dir ../saved_models/pretrain_codeexecutor_stage_3 \
--data_cache_dir ../saved_models/pretrain_codeexecutor_stage_3 \
--train_data_path /drive/pretrain_codenetmut.json \
--another_train_data_path /drive/p... | CodeBERT/CodeExecutor/pretrain/run.sh/0 | {
"file_path": "CodeBERT/CodeExecutor/pretrain/run.sh",
"repo_id": "CodeBERT",
"token_count": 398
} | 217 |
# Natural Language Toolkit: Utility functions
#
# Copyright (C) 2001-2020 NLTK Project
# Author: Steven Bird <stevenbird1@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from itertools import chain
def pad_sequence(
sequence,
n,
pad_left=False,
pad_right=False,
left... | CodeBERT/CodeReviewer/code/evaluator/CodeBLEU/utils.py/0 | {
"file_path": "CodeBERT/CodeReviewer/code/evaluator/CodeBLEU/utils.py",
"repo_id": "CodeBERT",
"token_count": 1699
} | 218 |
# batch size 6 for 16 GB GPU
mnt_dir="/home/codereview"
MASTER_HOST=localhost && echo MASTER_HOST: ${MASTER_HOST}
MASTER_PORT=23333 && echo MASTER_PORT: ${MASTER_PORT}
RANK=0 && echo RANK: ${RANK}
PER_NODE_GPU=1 && echo PER_NODE_GPU: ${PER_NODE_GPU}
WORLD_SIZE=1 && echo WORLD_SIZE: ${WORLD_SIZE}
NODES=1 && echo NODES... | CodeBERT/CodeReviewer/code/sh/infer-json.sh/0 | {
"file_path": "CodeBERT/CodeReviewer/code/sh/infer-json.sh",
"repo_id": "CodeBERT",
"token_count": 442
} | 219 |
# Code Generation
## Data Download
```bash
mkdir dataset
cd dataset
wget https://github.com/microsoft/CodeXGLUE/raw/main/Text-Code/text-to-code/dataset/concode/train.json
wget https://github.com/microsoft/CodeXGLUE/raw/main/Text-Code/text-to-code/dataset/concode/dev.json
wget https://github.com/microsoft/CodeXGLUE/ra... | CodeBERT/UniXcoder/downstream-tasks/code-generation/README.md/0 | {
"file_path": "CodeBERT/UniXcoder/downstream-tasks/code-generation/README.md",
"repo_id": "CodeBERT",
"token_count": 553
} | 220 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import statistics
import numpy as np
from collections import defaultdict
import logging
from typing import List, Union
import itertools
logging.basicConfig(
format="SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s",
datefm... | CodeT/CodeT/src/evaluation.py/0 | {
"file_path": "CodeT/CodeT/src/evaluation.py",
"repo_id": "CodeT",
"token_count": 2525
} | 221 |
import os
import json
import random
import argparse
from tqdm import tqdm
import re
import utils_io
from utils import (
GSM8KCase,
TextEntailmentCase,
GSM8KExample,
TextEntailmentExample,
compute_top1_and_recall,
post_process_answer_clutrr_mapping,
post_process_answer_clutrr_cutoff,
)
from t... | CodeT/DIVERSE/code/src/verifier_data_prepare.py/0 | {
"file_path": "CodeT/DIVERSE/code/src/verifier_data_prepare.py",
"repo_id": "CodeT",
"token_count": 4067
} | 222 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import functools
import os
from utils import Tools, FilePathBuilder, CodexTokenizer, CodeGenTokenizer, CONSTANTS
class PromptBuilder:
def __init__(self, query_lines_with_retrieval_results, task_path, log_message, tokenizer):
self.qu... | CodeT/RepoCoder/build_prompt.py/0 | {
"file_path": "CodeT/RepoCoder/build_prompt.py",
"repo_id": "CodeT",
"token_count": 3709
} | 223 |
#!/bin/zsh
#
# A shell script to clean up the setup of Codex CLI for zsh
#
set -e
CODEX_CLI_PATH="$( cd "$( dirname "$0" )" && cd .. && pwd )"
openAIConfigPath="$CODEX_CLI_PATH/src/openaiapirc"
zshrcPath="$HOME/.zshrc"
# 1. Remove settings in .zshrc
sed -i '' '/### Codex CLI setup - start/,/### Codex CLI setup - end... | Codex-CLI/scripts/zsh_cleanup.sh/0 | {
"file_path": "Codex-CLI/scripts/zsh_cleanup.sh",
"repo_id": "Codex-CLI",
"token_count": 212
} | 224 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: large_person_group.py
Description: Large Person Group section of the Cognitive Face API.
"""
from . import util
def create(large_person_group_id, name=None, user_data=None):
"""Create a new large person group with specified `large_person_group_id`,
`name... | Cognitive-Face-Python/cognitive_face/large_person_group.py/0 | {
"file_path": "Cognitive-Face-Python/cognitive_face/large_person_group.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 1613
} | 225 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: config.sample.py
Description: Unittest shared utilities for Python SDK of the Cognitive Face
API.
"""
import time
import uuid
import cognitive_face as CF
from . import config
# Base URL of online images.
BASE_URL_IMAGE = ('https://raw.githubusercontent.com... | Cognitive-Face-Python/cognitive_face/tests/util.py/0 | {
"file_path": "Cognitive-Face-Python/cognitive_face/tests/util.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 3869
} | 226 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: setup.py
Description: Setup script to build and distribute cognitive_face module.
"""
import io
from setuptools import find_packages
from setuptools import setup
README = 'README.md'
def readme():
"""Parse README for long_description."""
return io.ope... | Cognitive-Face-Python/setup.py/0 | {
"file_path": "Cognitive-Face-Python/setup.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 406
} | 227 |
# Does Deep Learning Learn to Abstract?
This is the official repo for the paper
*'Does Deep Learning Learn to Abstract? A Systematic Probing Framework'*.
This work has been accepted at ICLR 2023.
[OpenReview](https://openreview.net/forum?id=QB1dMPEXau5)
This repo contains data and main code used in this work.
We ho... | ContextualSP/abstraction_probing/README.md/0 | {
"file_path": "ContextualSP/abstraction_probing/README.md",
"repo_id": "ContextualSP",
"token_count": 976
} | 228 |
export CUDA_VISIBLE_DEVICES=4
python t5_run_eval.py \
--model_name_or_path ./checkpoint/Mod/MainExp_finetune_set1_seed1/checkpoint-50000 \
--subtask Mod \
--validation_file test \
--ebatch_size 16 \
--set set1 | ContextualSP/abstraction_probing/code/t5_code/Mod_MainExp_test.sh/0 | {
"file_path": "ContextualSP/abstraction_probing/code/t5_code/Mod_MainExp_test.sh",
"repo_id": "ContextualSP",
"token_count": 84
} | 229 |
import numpy as np
def update_roberta_keys(state, nlayer=24):
for key in state.keys():
if "self_attn.q_proj" in key:
return state
new_dict = {}
for key, val in state.items():
if not "self_attn.in_proj_" in key:
new_dict[key] = val
for i in range(nlayer):
... | ContextualSP/adaptershare/data_utils/roberta_utils.py/0 | {
"file_path": "ContextualSP/adaptershare/data_utils/roberta_utils.py",
"repo_id": "ContextualSP",
"token_count": 1281
} | 230 |
import os
import argparse
import random
from sys import path
path.append(os.getcwd())
from experiments.common_utils import dump_rows
from data_utils.task_def import DataFormat
from data_utils.log_wrapper import create_logger
from experiments.superglue.superglue_utils import *
logger = create_logger(__name__, to_disk=... | ContextualSP/adaptershare/experiments/superglue/superglue_prepro.py/0 | {
"file_path": "ContextualSP/adaptershare/experiments/superglue/superglue_prepro.py",
"repo_id": "ContextualSP",
"token_count": 3093
} | 231 |
# coding=utf-8
# Copyright (c) Microsoft. All rights reserved.
import shutil
import os
import subprocess
import filecmp
import os.path
def compare_files(dir1, dir2, common_files, text_mode=False):
same_files = []
diff_files = []
for common_file in common_files:
path0 = os.path.join(dir1, common_fi... | ContextualSP/adaptershare/tests/test_prepro.py/0 | {
"file_path": "ContextualSP/adaptershare/tests/test_prepro.py",
"repo_id": "ContextualSP",
"token_count": 960
} | 232 |
from .spider_align import SpiderAlignmentModel
from .wtq_align import WTQAlignmentModel
from baseline.wtq_s2s.seq2seq import WTQSeq2SeqModel
from .model_utils import *
from .optmizers import * | ContextualSP/awakening_latent_grounding/models/__init__.py/0 | {
"file_path": "ContextualSP/awakening_latent_grounding/models/__init__.py",
"repo_id": "ContextualSP",
"token_count": 63
} | 233 |
python train.py -model SpiderAlignmentModel -bert bert-large-uncased-whole-word-masking \
-lr 5e-5 -train_bs 6 \
-acc_steps 4 -alw linear_20-30 -num_epochs 30 \
--data_dir data/spider_grounding \
--out_dir checkpoints/model_spider \
--warmup_steps 2000 | ContextualSP/awakening_latent_grounding/train_spider_ground.sh/0 | {
"file_path": "ContextualSP/awakening_latent_grounding/train_spider_ground.sh",
"repo_id": "ContextualSP",
"token_count": 111
} | 234 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# Author: Qian Liu (SivilTaram)
# Original Repo: https://github.com/microsoft/ContextualSP
import torch
from overrides import overrides
from allennlp.modules.matrix_attention.matrix_attention import MatrixAttention
@MatrixAttention.register("e... | ContextualSP/incomplete_utterance_rewriting/src/similar_functions/element_wise.py/0 | {
"file_path": "ContextualSP/incomplete_utterance_rewriting/src/similar_functions/element_wise.py",
"repo_id": "ContextualSP",
"token_count": 355
} | 235 |
# coding: utf-8
import re
import nltk
from src.utils.utils import STOP_WORD_LIST
from src.utils.external import complex_rephrase
class NLModifier(object):
def __init__(self, mode='simple'):
self.database = ''
self.utterance = ''
self.utterance_tokens = []
self.utterance_tokens_n... | ContextualSP/interactive_text_to_sql/src/components/nl_modiifer.py/0 | {
"file_path": "ContextualSP/interactive_text_to_sql/src/components/nl_modiifer.py",
"repo_id": "ContextualSP",
"token_count": 2741
} | 236 |
# coding: utf-8
import json
from typing import List
from src.utils.utils import lemma_token
STOP_WORD_LIST = [_.strip() for _ in open('data/common/stop_words.txt', 'r', encoding='utf-8').readlines()]
def align_two_sentences_in_token_level(token_list1, token_list2, stop_word_list=[]):
token_list1 = [(word, idx... | ContextualSP/interactive_text_to_sql/src/utils/link_util.py/0 | {
"file_path": "ContextualSP/interactive_text_to_sql/src/utils/link_util.py",
"repo_id": "ContextualSP",
"token_count": 3558
} | 237 |
import json
import sys
import copy
from itertools import combinations, permutations
from random import choice, choices, shuffle
import math
import argparse
from multiprocessing import Pool
import multiprocessing
from collections import Counter
from functools import reduce
from math import gcd
from random import sample
... | ContextualSP/lemon/corpus_generation/tangrams_corpus_generation.py/0 | {
"file_path": "ContextualSP/lemon/corpus_generation/tangrams_corpus_generation.py",
"repo_id": "ContextualSP",
"token_count": 2964
} | 238 |
from abc import ABCMeta, abstractmethod
from collections import Mapping
import numpy as np
from gtd.utils import EqualityMixin
class Vocab(object, metaclass=ABCMeta):
@abstractmethod
def word2index(self, w):
pass
@abstractmethod
def index2word(self, i):
pass
class SimpleVocab(Voca... | ContextualSP/lemon/executor/gtd/ml/vocab.py/0 | {
"file_path": "ContextualSP/lemon/executor/gtd/ml/vocab.py",
"repo_id": "ContextualSP",
"token_count": 1448
} | 239 |
import json
import logging
import time
from abc import ABCMeta, abstractmethod
from collections import Counter
import pytest
from gtd.persist import LazyMapping, EagerMapping, TableMapping, ORM, ORMColumn, FileSequence, FileSerializer, SimpleORM, \
ShardedSequence, CustomSerializer, LazyIterator, BatchIterator, Si... | ContextualSP/lemon/executor/gtd/tests/test_persist.py/0 | {
"file_path": "ContextualSP/lemon/executor/gtd/tests/test_persist.py",
"repo_id": "ContextualSP",
"token_count": 7081
} | 240 |
from abc import ABCMeta, abstractproperty
from collections import Sequence
import numpy as np
from gtd.utils import set_once_attribute
class ParseCase(object, metaclass=ABCMeta):
"""Necessary and sufficient information to make a prediction about the next decision.
Attributes that must be assigned upon crea... | ContextualSP/lemon/executor/strongsup/parse_case.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/parse_case.py",
"repo_id": "ContextualSP",
"token_count": 6805
} | 241 |
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