text stringlengths 5 22M | id stringlengths 12 177 | metadata dict | __index_level_0__ int64 0 1.37k |
|---|---|---|---|
resource "azurerm_network_interface" "internal" {
name = "internal-nic-${local.service_resource_name_suffix}"
location = data.azurerm_resource_group.ws.location
resource_group_name = data.azurerm_resource_group.ws.name
tags = local.tre_user_resources_tags
ip_configura... | AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-import-reviewvm/terraform/windowsvm.tf/0 | {
"file_path": "AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-import-reviewvm/terraform/windowsvm.tf",
"repo_id": "AzureTRE",
"token_count": 1531
} | 124 |
output "ip" {
value = azurerm_network_interface.internal.private_ip_address
}
output "hostname" {
value = azurerm_linux_virtual_machine.linuxvm.name
}
output "azure_resource_id" {
value = azurerm_linux_virtual_machine.linuxvm.id
}
output "connection_uri" {
value = "https://${data.azurerm_linux_web_app.guacam... | AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-linuxvm/terraform/outputs.tf/0 | {
"file_path": "AzureTRE/templates/workspace_services/guacamole/user_resources/guacamole-azure-linuxvm/terraform/outputs.tf",
"repo_id": "AzureTRE",
"token_count": 245
} | 125 |
#!/bin/bash
set -e
eval "$(jq -r '@sh "firewall_name=\(.firewall_name) resource_group_name=\(.resource_group_name) collection_name_suffix=\(.collection_name_suffix)"')"
if NETWORK_RULES=$(az network firewall network-rule list -g $resource_group_name -f $firewall_name --collection-name "nrc-$collection_name_suffix" ... | AzureTRE/templates/workspace_services/innereye/terraform/get_firewall_priorities.sh/0 | {
"file_path": "AzureTRE/templates/workspace_services/innereye/terraform/get_firewall_priorities.sh",
"repo_id": "AzureTRE",
"token_count": 518
} | 126 |
[Environment]::SetEnvironmentVariable("AZURE_STORAGE_CONNECTION_STRING", "${MLFlow_Connection_String}", "Machine")
pip install mlflow==1.24.0
pip install azure-storage-blob==12.10.0
pip install azure-identity==1.8.0
| AzureTRE/templates/workspace_services/mlflow/mlflow-vm-config/windows/template_config.ps1/0 | {
"file_path": "AzureTRE/templates/workspace_services/mlflow/mlflow-vm-config/windows/template_config.ps1",
"repo_id": "AzureTRE",
"token_count": 81
} | 127 |
#!/bin/bash
set -o errexit
set -o pipefail
set -o nounset
function build_daimon_object() {
local DAIMON_TYPE=$1
local VALUE=$2
echo '{
"tableQualifier": "'"$VALUE"'",
"priority": 0,
"sourceDaimonId": null,
"daimonType": "'"$DAIMON_TYPE"'"
}'
}
# Login
login_response=$(curl "https://${OHDSI_W... | AzureTRE/templates/workspace_services/ohdsi/scripts/add_data_source.sh/0 | {
"file_path": "AzureTRE/templates/workspace_services/ohdsi/scripts/add_data_source.sh",
"repo_id": "AzureTRE",
"token_count": 1329
} | 128 |
define([], function () {
var configLocal = {};
// clearing local storage otherwise source cache will obscure the override settings
localStorage.clear();
// WebAPI
configLocal.api = {
name: 'OHDSI',
url: "${OHDSI_WEBAPI_URL}"
};
configLocal.cohortComparisonResultsEnabled = false;
configLocal.userAuthentic... | AzureTRE/templates/workspace_services/ohdsi/terraform/config_local.tftpl/0 | {
"file_path": "AzureTRE/templates/workspace_services/ohdsi/terraform/config_local.tftpl",
"repo_id": "AzureTRE",
"token_count": 290
} | 129 |
{
"$schema": "http://json-schema.org/draft-07/schema",
"$id": "https://github.com/microsoft/AzureTRE/templates/workspaces/airlock_import_review/template_schema.json",
"type": "object",
"title": "Airlock Import Review Workspace",
"description": "This workspace template is intended to conduct Airlock Data Impor... | AzureTRE/templates/workspaces/airlock-import-review/template_schema.json/0 | {
"file_path": "AzureTRE/templates/workspaces/airlock-import-review/template_schema.json",
"repo_id": "AzureTRE",
"token_count": 2550
} | 130 |
locals {
core_resource_group_name = "rg-${var.tre_id}"
workspace_resource_name_suffix = "${var.tre_id}-ws-${var.short_workspace_id}"
import_approved_sys_topic_name = "evgt-airlock-import-approved-${local.workspace_resource_name_suffix}"
export_inprogress_sys_topic_name = "evgt-airlock-export-inprog-${l... | AzureTRE/templates/workspaces/base/terraform/airlock/locals.tf/0 | {
"file_path": "AzureTRE/templates/workspaces/base/terraform/airlock/locals.tf",
"repo_id": "AzureTRE",
"token_count": 724
} | 131 |
# For recommended Azure private DNS zone names see https://docs.microsoft.com/azure/private-link/private-endpoint-dns#azure-services-dns-zone-configuration
# To enable connecting to Azure Monitor from within a workspace VNET (where traffic is restricted), we need to have an Azure Monitor Private Link Scope (AMPLS) tha... | AzureTRE/templates/workspaces/base/terraform/network/dns_zones.tf/0 | {
"file_path": "AzureTRE/templates/workspaces/base/terraform/network/dns_zones.tf",
"repo_id": "AzureTRE",
"token_count": 1721
} | 132 |
# syntax=docker/dockerfile-upstream:1.4.0
FROM --platform=linux/amd64 debian:bullseye-slim
# PORTER_INIT
RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
# Git is required for terraform_azurerm_environment_configuration
RUN --mount... | AzureTRE/templates/workspaces/unrestricted/Dockerfile.tmpl/0 | {
"file_path": "AzureTRE/templates/workspaces/unrestricted/Dockerfile.tmpl",
"repo_id": "AzureTRE",
"token_count": 403
} | 133 |
import React, { useEffect, useState } from 'react';
import { DefaultPalette, IStackStyles, MessageBar, MessageBarType, Stack } from '@fluentui/react';
import './App.scss';
import { TopNav } from './components/shared/TopNav';
import { Routes, Route } from 'react-router-dom';
import { RootLayout } from './components/root... | AzureTRE/ui/app/src/App.tsx/0 | {
"file_path": "AzureTRE/ui/app/src/App.tsx",
"repo_id": "AzureTRE",
"token_count": 2860
} | 134 |
import React from 'react';
import { VMPowerStates } from '../../models/resource';
interface PowerStateBadgeProps {
state: VMPowerStates
}
export const PowerStateBadge: React.FunctionComponent<PowerStateBadgeProps> = (props: PowerStateBadgeProps) => {
let stateClass = "tre-power-off";
if (props.state === VMPower... | AzureTRE/ui/app/src/components/shared/PowerStateBadge.tsx/0 | {
"file_path": "AzureTRE/ui/app/src/components/shared/PowerStateBadge.tsx",
"repo_id": "AzureTRE",
"token_count": 236
} | 135 |
import { Stack, FontWeights, Text, Spinner, FontIcon, mergeStyles, getTheme, SpinnerSize, TooltipHost, ITooltipProps } from '@fluentui/react';
import React from 'react';
import { awaitingStates, failedStates, inProgressStates } from '../../models/operation';
import { Resource } from '../../models/resource';
interface ... | AzureTRE/ui/app/src/components/shared/StatusBadge.tsx/0 | {
"file_path": "AzureTRE/ui/app/src/components/shared/StatusBadge.tsx",
"repo_id": "AzureTRE",
"token_count": 1267
} | 136 |
import { createSlice, PayloadAction } from '@reduxjs/toolkit';
import { completedStates, Operation } from '../../../models/operation';
interface OperationsState {
items: Array<Operation>
}
const initialState: OperationsState = {
items: []
};
// note - we can write what looks like state mutations here because the... | AzureTRE/ui/app/src/components/shared/notifications/operationsSlice.ts/0 | {
"file_path": "AzureTRE/ui/app/src/components/shared/notifications/operationsSlice.ts",
"repo_id": "AzureTRE",
"token_count": 433
} | 137 |
import { useContext, useEffect, useState } from "react";
import { WorkspaceContext } from "../contexts/WorkspaceContext";
import { completedStates, inProgressStates, Operation } from "../models/operation";
import { ResourceUpdate, ComponentAction, getResourceFromResult, Resource } from "../models/resource";
import { Re... | AzureTRE/ui/app/src/hooks/useComponentManager.ts/0 | {
"file_path": "AzureTRE/ui/app/src/hooks/useComponentManager.ts",
"repo_id": "AzureTRE",
"token_count": 1053
} | 138 |
import { Resource } from "./resource";
export interface Workspace extends Resource {
workspaceURL: string
} | AzureTRE/ui/app/src/models/workspace.ts/0 | {
"file_path": "AzureTRE/ui/app/src/models/workspace.ts",
"repo_id": "AzureTRE",
"token_count": 28
} | 139 |
# Relation Extraction on BC5CDR
## Data
You can process the data by:
``` bash
bash preprocess.sh
```
## Training
You can fine-tune the pre-trained BioGPT on the task by:
``` bash
bash train.sh
```
## Model Checkpoint
We provide our fine-tuned model on the task. See [here](../../README.md#pre-trained-models)
## Infe... | BioGPT/examples/RE-BC5CDR/README.md/0 | {
"file_path": "BioGPT/examples/RE-BC5CDR/README.md",
"repo_id": "BioGPT",
"token_count": 142
} | 140 |
recursive-include 3rdparty/tvm *
recursive-exclude 3rdparty/tvm/build *
recursive-exclude 3rdparty/clang* *
recursive-exclude 3rdparty/llvm* *
| BitBLAS/MANIFEST.in/0 | {
"file_path": "BitBLAS/MANIFEST.in",
"repo_id": "BitBLAS",
"token_count": 56
} | 141 |
### Using BitBLAS from DSL
```python
from bitblas.base.roller.policy import TensorCorePolicy, DefaultPolicy
from bitblas.base.roller.arch import CUDA
from bitblas.base.utils import apply_and_build
@tvm.script.ir_module
class MatmulNT:
@T.prim_func
def main(a: T.handle, b: T.handle, c: T.handle):
T.func_... | BitBLAS/docs/ExtendOperatorsWithDSL.md/0 | {
"file_path": "BitBLAS/docs/ExtendOperatorsWithDSL.md",
"repo_id": "BitBLAS",
"token_count": 3492
} | 142 |
# 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/configuration_bitnet.py/0 | {
"file_path": "BitBLAS/integration/BitNet/configuration_bitnet.py",
"repo_id": "BitBLAS",
"token_count": 3674
} | 143 |
please checkout https://github.com/kaleid-liner/fastertransformer_backend | BitBLAS/integration/fastertransformer/README.md/0 | {
"file_path": "BitBLAS/integration/fastertransformer/README.md",
"repo_id": "BitBLAS",
"token_count": 21
} | 144 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from .default import DefaultPolicy
from .tensorcore import TensorCorePolicy
| BitBLAS/python/bitblas/base/roller/policy/__init__.py/0 | {
"file_path": "BitBLAS/python/bitblas/base/roller/policy/__init__.py",
"repo_id": "BitBLAS",
"token_count": 36
} | 145 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# pylint: disable=missing-docstring
"""A fallback schedule rule for GPU operators."""
from typing import List
from tvm import tir
from ..base import ScheduleRule, normalize_prim_func, try_inline
class ElementWise(ScheduleRule):
"""
An... | BitBLAS/python/bitblas/gpu/element_wise.py/0 | {
"file_path": "BitBLAS/python/bitblas/gpu/element_wise.py",
"repo_id": "BitBLAS",
"token_count": 1757
} | 146 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import ctypes
import operator
from functools import reduce
from logging import getLogger
import torch
import torch.nn as nn
logger = getLogger(__name__)
from typing import List, Union
from bitblas.cache import global_operator_cache, get_datab... | BitBLAS/python/bitblas/module/__init__.py/0 | {
"file_path": "BitBLAS/python/bitblas/module/__init__.py",
"repo_id": "BitBLAS",
"token_count": 5723
} | 147 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from .quantization import (
_tir_packed_int_to_int_convert, # noqa: F401
_tir_packed_to_signed_convert, # noqa: F401
_tir_packed_to_unsigned_convert, # noqa: F401
_tir_u32_to_f4_to_f16, # noqa: F401
_tir_packed_to_unsigned_... | BitBLAS/python/bitblas/quantization/__init__.py/0 | {
"file_path": "BitBLAS/python/bitblas/quantization/__init__.py",
"repo_id": "BitBLAS",
"token_count": 179
} | 148 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import pytest
import bitblas
from bitblas.ops.matmul import Matmul, MatmulConfig
from bitblas.utils import auto_detect_nvidia_target
target = auto_detect_nvidia_target()
def get_codegen_result(ops, target):
code = ops.get_source(target=targ... | BitBLAS/testing/python/operators/test_matmul_ops.py/0 | {
"file_path": "BitBLAS/testing/python/operators/test_matmul_ops.py",
"repo_id": "BitBLAS",
"token_count": 3626
} | 149 |
import os
import copy
import pytorch_lightning as pl
import wandb
import torch
import time
from pytorch_lightning.loggers import WandbLogger
import os
os.environ["NCCL_DEBUG"] = "INFO"
from src.config import ex
from src.modules import METERTransformerSS
from src.modules import BTTransformer
from src.datamodules.multit... | BridgeTower/run.py/0 | {
"file_path": "BridgeTower/run.py",
"repo_id": "BridgeTower",
"token_count": 2024
} | 150 |
date ; hostname ; pwd
EXP_IS=288
EXP_PGB=32
EXP_PGEB=128
EXP_LR=1e-5
export MASTER_ADDR=node-0
export MASTER_PORT=19800
export NODE_RANK=$1
PREFIX_NAME="pt"
echo $MASTER_ADDR, $MASTER_PORT, $NODE_RANK, $EXP_IS, $EXP_PGB, $EXP_PGEB, $EXP_LR
TIME=$(date "+%Y%m%d%H%M")
RUN_NAME=""$PREFIX_NAME"_"$EXP_IS"_"$EXP_PGB"_"... | BridgeTower/scripts/pre_train.sh/0 | {
"file_path": "BridgeTower/scripts/pre_train.sh",
"repo_id": "BridgeTower",
"token_count": 299
} | 151 |
from .vg_caption_dataset import VisualGenomeCaptionDataset
from .coco_caption_karpathy_dataset import CocoCaptionKarpathyDataset
from .f30k_caption_karpathy_dataset import F30KCaptionKarpathyDataset
from .conceptual_caption_dataset import ConceptualCaptionDataset
from .sbu_caption_dataset import SBUCaptionDataset
from ... | BridgeTower/src/datasets/__init__.py/0 | {
"file_path": "BridgeTower/src/datasets/__init__.py",
"repo_id": "BridgeTower",
"token_count": 168
} | 152 |
# Copyright (c) Facebook, Inc. and its affiliates.
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/comm.py
"""
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import functools
import numpy as np
import torch
import torch.d... | BridgeTower/src/modules/dist_utils.py/0 | {
"file_path": "BridgeTower/src/modules/dist_utils.py",
"repo_id": "BridgeTower",
"token_count": 2222
} | 153 |
import json
import os
import pandas as pd
import pyarrow as pa
import random
from tqdm import tqdm
from glob import glob
from collections import defaultdict
def path2rest(path, iid2captions, iid2split):
name = path.split("/")[-1]
with open(path, "rb") as fp:
binary = fp.read()
captions = iid2capt... | BridgeTower/src/utils/write_coco_karpathy.py/0 | {
"file_path": "BridgeTower/src/utils/write_coco_karpathy.py",
"repo_id": "BridgeTower",
"token_count": 887
} | 154 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import ntpath
import time
from . import util
import scipy.misc
try:
from StringIO import StringIO # Python 2.7
except ImportError:
from io import BytesIO # Python 3.x
import torchvision.utils as vutils
from tensorboardX impor... | Bringing-Old-Photos-Back-to-Life/Face_Enhancement/util/visualizer.py/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Face_Enhancement/util/visualizer.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 2392
} | 155 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import functools
from torch.autograd import Variable
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
im... | Bringing-Old-Photos-Back-to-Life/Global/models/NonLocal_feature_mapping_model.py/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/models/NonLocal_feature_mapping_model.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 3615
} | 156 |
from . import clap
from . import audio
from . import htsat
from . import config
from . import pytorch_utils
from . import htsat | CLAP/msclap/models/__init__.py/0 | {
"file_path": "CLAP/msclap/models/__init__.py",
"repo_id": "CLAP",
"token_count": 39
} | 157 |
.. role:: hidden
:class: hidden-section
.. module:: fairseq.data
Data Loading and Utilities
==========================
.. _datasets:
Datasets
--------
**Datasets** define the data format and provide helpers for creating
mini-batches.
.. autoclass:: fairseq.data.FairseqDataset
:members:
.. autoclass:: fair... | COCO-LM/fairseq/docs/data.rst/0 | {
"file_path": "COCO-LM/fairseq/docs/data.rst",
"repo_id": "COCO-LM",
"token_count": 419
} | 158 |
Tutorial: Simple LSTM
=====================
In this tutorial we will extend fairseq by adding a new
:class:`~fairseq.models.FairseqEncoderDecoderModel` that encodes a source
sentence with an LSTM and then passes the final hidden state to a second LSTM
that decodes the target sentence (without attention).
This tutoria... | COCO-LM/fairseq/docs/tutorial_simple_lstm.rst/0 | {
"file_path": "COCO-LM/fairseq/docs/tutorial_simple_lstm.rst",
"repo_id": "COCO-LM",
"token_count": 8622
} | 159 |
#!/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.
SPM_ENCODE=flores/scripts/spm_encode.py
DATA=data_tmp
SPM_MODEL=criss_checkpoints/sentence.bpe.model
DICT=criss... | COCO-LM/fairseq/examples/criss/download_and_preprocess_tatoeba.sh/0 | {
"file_path": "COCO-LM/fairseq/examples/criss/download_and_preprocess_tatoeba.sh",
"repo_id": "COCO-LM",
"token_count": 758
} | 160 |
#!/bin/bash
# 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.
echo 'Cloning Moses github repository (for tokenization scripts)...'
git clone https://github.com/moses-smt/mosesdecoder.git
SCR... | COCO-LM/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh/0 | {
"file_path": "COCO-LM/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh",
"repo_id": "COCO-LM",
"token_count": 1526
} | 161 |
# 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 fairseq.models import register_model, register_model_architecture
from fairseq.models.multilingual_transformer import MultilingualTransfo... | COCO-LM/fairseq/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py/0 | {
"file_path": "COCO-LM/fairseq/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py",
"repo_id": "COCO-LM",
"token_count": 1294
} | 162 |
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--src', type=str, help='Source language')
parser.add_argument('--tgt', type=str, help='Target language')
parser.add_argument('--src-file', type=str, help='Input source file')
parser.add_argument('--tgt-file', type=str, help='Input target file')
pa... | COCO-LM/fairseq/examples/m2m_100/process_data/clean_histogram.py/0 | {
"file_path": "COCO-LM/fairseq/examples/m2m_100/process_data/clean_histogram.py",
"repo_id": "COCO-LM",
"token_count": 846
} | 163 |
# Multilingual Translation
[[Multilingual Translation with Extensible Multilingual Pretraining and Finetuning, https://arxiv.org/abs/2008.00401]](https://arxiv.org/abs/2008.00401)
## Introduction
This work is for training multilingual translation models with multiple bitext datasets. This multilingual translation fr... | COCO-LM/fairseq/examples/multilingual/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/multilingual/README.md",
"repo_id": "COCO-LM",
"token_count": 2561
} | 164 |
#!/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.
if [ -z $WORKDIR_ROOT ] ;
then
echo "please specify your working directory root in environment variable... | COCO-LM/fairseq/examples/multilingual/data_scripts/download_wmt20.sh/0 | {
"file_path": "COCO-LM/fairseq/examples/multilingual/data_scripts/download_wmt20.sh",
"repo_id": "COCO-LM",
"token_count": 11121
} | 165 |
# 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
from fairseq import options
from examples.noisychannel import rerank_options, rerank_utils
def score_lm(args):
using_nbest =... | COCO-LM/fairseq/examples/noisychannel/rerank_score_lm.py/0 | {
"file_path": "COCO-LM/fairseq/examples/noisychannel/rerank_score_lm.py",
"repo_id": "COCO-LM",
"token_count": 1096
} | 166 |
# Finetuning RoBERTa on a custom classification task
This example shows how to finetune RoBERTa on the IMDB dataset, but should illustrate the process for most classification tasks.
### 1) Get the data
```bash
wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
tar zxvf aclImdb_v1.tar.gz
```
### 2)... | COCO-LM/fairseq/examples/roberta/README.custom_classification.md/0 | {
"file_path": "COCO-LM/fairseq/examples/roberta/README.custom_classification.md",
"repo_id": "COCO-LM",
"token_count": 2196
} | 167 |
# 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 json
import os
import tempfile
import numpy as np
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.... | COCO-LM/fairseq/examples/roberta/wsc/wsc_task.py/0 | {
"file_path": "COCO-LM/fairseq/examples/roberta/wsc/wsc_task.py",
"repo_id": "COCO-LM",
"token_count": 6705
} | 168 |
# 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 typing import Optional
import requests
from scorers import build_scorer
class SimulSTEvaluationService(object):
DEFAULT_HOSTNAME =... | COCO-LM/fairseq/examples/simultaneous_translation/eval/client.py/0 | {
"file_path": "COCO-LM/fairseq/examples/simultaneous_translation/eval/client.py",
"repo_id": "COCO-LM",
"token_count": 1349
} | 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 torch
def exclusive_cumprod(tensor, dim: int, eps: float = 1e-10):
"""
Implementing exclusive cumprod.
There is cumprod i... | COCO-LM/fairseq/examples/simultaneous_translation/utils/functions.py/0 | {
"file_path": "COCO-LM/fairseq/examples/simultaneous_translation/utils/functions.py",
"repo_id": "COCO-LM",
"token_count": 1936
} | 170 |
# @package _group_
defaults:
- task: null
- model: null
hydra:
run:
dir: ${common_eval.results_path}/${dataset.gen_subset}
sweep:
dir: ${common_eval.results_path}
subdir: ${dataset.gen_subset}
common_eval:
results_path: ${decoding.exp_dir}/decode/${decoding.decoder.name}
path: ${decoding.e... | COCO-LM/fairseq/examples/speech_recognition/hydra/conf/infer.yaml/0 | {
"file_path": "COCO-LM/fairseq/examples/speech_recognition/hydra/conf/infer.yaml",
"repo_id": "COCO-LM",
"token_count": 194
} | 171 |
[[Back]](..)
# S2T Example: Speech Translation (ST) on MuST-C
[MuST-C](https://www.aclweb.org/anthology/N19-1202) is multilingual speech-to-text translation corpus with
8-language translations on English TED talks. We match the state-of-the-art performance in
[ESPNet-ST](https://arxiv.org/pdf/2004.10234.pdf) with a s... | COCO-LM/fairseq/examples/speech_to_text/docs/mustc_example.md/0 | {
"file_path": "COCO-LM/fairseq/examples/speech_to_text/docs/mustc_example.md",
"repo_id": "COCO-LM",
"token_count": 4426
} | 172 |
# WMT 20
This page provides pointers to the models of Facebook-FAIR's WMT'20 news translation task submission [(Chen et al., 2020)](https://arxiv.org/abs/2011.08298).
## Single best MT models (after finetuning on part of WMT20 news dev set)
Model | Description | Download
---|---|---
`transformer.wmt20.ta-en` | Ta->E... | COCO-LM/fairseq/examples/wmt20/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/wmt20/README.md",
"repo_id": "COCO-LM",
"token_count": 2042
} | 173 |
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "edit_dist.h"
#include <THC/THC.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
... | COCO-LM/fairseq/fairseq/clib/libnat_cuda/edit_dist.cu/0 | {
"file_path": "COCO-LM/fairseq/fairseq/clib/libnat_cuda/edit_dist.cu",
"repo_id": "COCO-LM",
"token_count": 5172
} | 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.
"""isort:skip_file"""
import importlib
import os
from fairseq import registry
from fairseq.criterions.fairseq_criterion import ( # noqa
... | COCO-LM/fairseq/fairseq/criterions/__init__.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/criterions/__init__.py",
"repo_id": "COCO-LM",
"token_count": 351
} | 175 |
# 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 dataclasses import dataclass, field
from typing import List, Optional
import torch
import torch.nn.functional as F
from fair... | COCO-LM/fairseq/fairseq/criterions/wav2vec_criterion.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/criterions/wav2vec_criterion.py",
"repo_id": "COCO-LM",
"token_count": 4184
} | 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 bisect
import numpy as np
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
class ConcatDataset(... | COCO-LM/fairseq/fairseq/data/concat_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/concat_dataset.py",
"repo_id": "COCO-LM",
"token_count": 2203
} | 177 |
# 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.data.encoders import register_bpe
from fairseq.dataclass import FairseqDataclass
from f... | COCO-LM/fairseq/fairseq/data/encoders/hf_byte_bpe.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/encoders/hf_byte_bpe.py",
"repo_id": "COCO-LM",
"token_count": 723
} | 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.
from fairseq.data import Dictionary
class MaskedLMDictionary(Dictionary):
"""
Dictionary for Masked Language Modelling tasks. This e... | COCO-LM/fairseq/fairseq/data/legacy/masked_lm_dictionary.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/legacy/masked_lm_dictionary.py",
"repo_id": "COCO-LM",
"token_count": 699
} | 179 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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
#
# Unless required by ... | COCO-LM/fairseq/fairseq/data/squad/basic_tokenizer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/squad/basic_tokenizer.py",
"repo_id": "COCO-LM",
"token_count": 2029
} | 180 |
# 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
from torch import nn
logger = logging.getLogger(__name__)
class DistributedTimeou... | COCO-LM/fairseq/fairseq/distributed/distributed_timeout_wrapper.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/distributed/distributed_timeout_wrapper.py",
"repo_id": "COCO-LM",
"token_count": 1304
} | 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 copy
import logging
from typing import Dict, List
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as ... | COCO-LM/fairseq/fairseq/models/bart/hub_interface.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/bart/hub_interface.py",
"repo_id": "COCO-LM",
"token_count": 3628
} | 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 fairseq import utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from f... | COCO-LM/fairseq/fairseq/models/lightconv_lm.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/lightconv_lm.py",
"repo_id": "COCO-LM",
"token_count": 5387
} | 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 math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.dis... | COCO-LM/fairseq/fairseq/models/transformer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/transformer.py",
"repo_id": "COCO-LM",
"token_count": 23740
} | 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.
*/
template <typename U, typename V>
constexpr __host__ __device__ auto divUp(U a, V b) -> decltype(a + b) {
return (a + b - 1)... | COCO-LM/fairseq/fairseq/modules/cuda_utils.cu/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/cuda_utils.cu",
"repo_id": "COCO-LM",
"token_count": 2367
} | 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 torch
import torch.nn as nn
import torch.nn.functional as F
class GumbelVectorQuantizer(nn.Module):
def __init__(
self,
... | COCO-LM/fairseq/fairseq/modules/gumbel_vector_quantizer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/gumbel_vector_quantizer.py",
"repo_id": "COCO-LM",
"token_count": 3467
} | 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 torch
import torch.nn as nn
def quant_noise(module, p, block_size):
"""
Wraps modules and applies quantization noise to the w... | COCO-LM/fairseq/fairseq/modules/quant_noise.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/quant_noise.py",
"repo_id": "COCO-LM",
"token_count": 1844
} | 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 torch
import torch.nn as nn
import torch.nn.functional as F
from ..ops import emulate_int
class IntLinear(nn.Module):
"""
Qu... | COCO-LM/fairseq/fairseq/modules/quantization/scalar/modules/qlinear.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/quantization/scalar/modules/qlinear.py",
"repo_id": "COCO-LM",
"token_count": 1588
} | 188 |
# Originally from Microsoft Corporation.
# Licensed under the MIT License.
""" Wrapper for ngram_repeat_block cuda extension """
import torch
from torch import nn
import math
from typing import Dict, List, Optional
import warnings
try:
from fairseq import ngram_repeat_block_cuda
EXTENSION_BUILT = True
excep... | COCO-LM/fairseq/fairseq/ngram_repeat_block.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/ngram_repeat_block.py",
"repo_id": "COCO-LM",
"token_count": 2626
} | 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.
import math
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
from omegaconf import II
... | COCO-LM/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py",
"repo_id": "COCO-LM",
"token_count": 2432
} | 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.
from argparse import Namespace
from typing import Union
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import po... | COCO-LM/fairseq/fairseq/registry.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/registry.py",
"repo_id": "COCO-LM",
"token_count": 1659
} | 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.
import logging
import os
import numpy as np
from fairseq import utils
from fairseq.data import (
Dictionary,
IdDataset,
MaskToken... | COCO-LM/fairseq/fairseq/tasks/masked_lm.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/tasks/masked_lm.py",
"repo_id": "COCO-LM",
"token_count": 4534
} | 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.
"""
Train a network across multiple GPUs.
"""
import contextlib
import logging
import sys
import time
from argparse import Namespace
from ite... | COCO-LM/fairseq/fairseq/trainer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/trainer.py",
"repo_id": "COCO-LM",
"token_count": 26727
} | 193 |
#include <torch/extension.h>
#include <vector>
#include <cassert>
#include "compat.h"
namespace {
void compute_n1_n2(
at::Tensor input,
#ifdef VERSION_GE_1_1
at::IntArrayRef normalized_shape,
#else
at::IntList normalized_shape,
#endif
int& n1,
int& n2)
{
int idiff = input.ndimension... | COCO-LM/fairseq/fused_ops/csrc/layernorm/interface.cpp/0 | {
"file_path": "COCO-LM/fairseq/fused_ops/csrc/layernorm/interface.cpp",
"repo_id": "COCO-LM",
"token_count": 2939
} | 194 |
#!/usr/bin/env python
# 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 contextlib
import sys
from collections import Counter
from multiprocessing im... | COCO-LM/fairseq/preprocess/glue/multiprocessing_sp_encoder.py/0 | {
"file_path": "COCO-LM/fairseq/preprocess/glue/multiprocessing_sp_encoder.py",
"repo_id": "COCO-LM",
"token_count": 482
} | 195 |
#!/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.
"""
Count the number of documents and average number of lines and tokens per
document in a large file. Documents should ... | COCO-LM/fairseq/scripts/count_docs.py/0 | {
"file_path": "COCO-LM/fairseq/scripts/count_docs.py",
"repo_id": "COCO-LM",
"token_count": 803
} | 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.
import functools
import sys
import unittest
import torch
from fairseq.distributed import utils as dist_utils
from .utils import objects_are... | COCO-LM/fairseq/tests/distributed/test_utils.py/0 | {
"file_path": "COCO-LM/fairseq/tests/distributed/test_utils.py",
"repo_id": "COCO-LM",
"token_count": 1912
} | 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.
import contextlib
import logging
import os
import tempfile
import unittest
from io import StringIO
from unittest.mock import patch
from fairs... | COCO-LM/fairseq/tests/test_checkpoint_utils.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_checkpoint_utils.py",
"repo_id": "COCO-LM",
"token_count": 1976
} | 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 argparse
import logging
import unittest
import torch
from fairseq.optim.adam import FairseqAdam
from fairseq.optim.fp16_optimizer impo... | COCO-LM/fairseq/tests/test_memory_efficient_fp16.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_memory_efficient_fp16.py",
"repo_id": "COCO-LM",
"token_count": 1313
} | 199 |
datadir: /data/CMIP6/AWI-ESM
name: 2m_temperature
cmip_name: tas
era_name: t2m
run: r1i1p1f1
res:
- 1.40625
# - 5.625 | ClimaX/snakemake_configs/AWI-ESM/config_2m_temperature.yml/0 | {
"file_path": "ClimaX/snakemake_configs/AWI-ESM/config_2m_temperature.yml",
"repo_id": "ClimaX",
"token_count": 68
} | 200 |
datadir: /data/CMIP6/HAMMOZ
name: specific_humidity
cmip_name: hus
era_name: q
run: r1i1p1f1
version: v20190628
res:
- 1.40625
# - 5.625
| ClimaX/snakemake_configs/HAMMOZ/config_specific_humidity.yml/0 | {
"file_path": "ClimaX/snakemake_configs/HAMMOZ/config_specific_humidity.yml",
"repo_id": "ClimaX",
"token_count": 70
} | 201 |
datadir: /data/CMIP6/TaiESM1
server_prefix: https://esgf.ceda.ac.uk/thredds/fileServer/esg_cmip6/CMIP6/CMIP
name: specific_humidity
cmip_name: hus
era_name: q
run: r1i1p1f1
res:
- 1.40625
# - 5.625
| ClimaX/snakemake_configs/TaiESM1/config_specific_humidity.yml/0 | {
"file_path": "ClimaX/snakemake_configs/TaiESM1/config_specific_humidity.yml",
"repo_id": "ClimaX",
"token_count": 102
} | 202 |
import math
import torch
import torch.nn.functional as F
from timm.models.layers.helpers import to_2tuple
from torch import nn
def _get_conv2d_weights(
in_channels,
out_channels,
kernel_size,
):
weight = torch.empty(out_channels, in_channels, *kernel_size)
return weight
def _get_conv2d_biases(o... | ClimaX/src/climax/parallelpatchembed.py/0 | {
"file_path": "ClimaX/src/climax/parallelpatchembed.py",
"repo_id": "ClimaX",
"token_count": 1257
} | 203 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import glob
import os
import click
import numpy as np
import xarray as xr
from tqdm import tqdm
from climax.utils.data_utils import DEFAULT_PRESSURE_LEVELS, NAME_TO_VAR
HOURS_PER_YEAR = 8760 # 365-day year
def nc2np(path, variables, years, ... | ClimaX/src/data_preprocessing/nc2np_equally_era5.py/0 | {
"file_path": "ClimaX/src/data_preprocessing/nc2np_equally_era5.py",
"repo_id": "ClimaX",
"token_count": 3951
} | 204 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.spectral_norm as spectral_norm
from models.networks.normalization import SPADE
from util.util import vgg_preprocess
class ResidualBlock(nn.Module):
def __init__(sel... | CoCosNet-v2/models/networks/architecture.py/0 | {
"file_path": "CoCosNet-v2/models/networks/architecture.py",
"repo_id": "CoCosNet-v2",
"token_count": 3586
} | 205 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
from torchvision.utils import save_image
import os
import imageio
import numpy as np
import data
from util.util import mkdir
from options.test_options import TestOptions
from models.pix2pix_model import Pix2PixModel
if __name__ == ... | CoCosNet-v2/test.py/0 | {
"file_path": "CoCosNet-v2/test.py",
"repo_id": "CoCosNet-v2",
"token_count": 802
} | 206 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
from data.pix2pix_dataset import Pix2pixDataset
class FlickrDataset(Pix2pixDataset):
@staticmethod
def modify_commandline_options(parser, is_train):
parser = Pix2pixDataset.modify_commandline_options(parser, is_train)
... | CoCosNet/data/flickr_dataset.py/0 | {
"file_path": "CoCosNet/data/flickr_dataset.py",
"repo_id": "CoCosNet",
"token_count": 1203
} | 207 |
# Code Documentation Generation
This repo provides the code for reproducing the experiments on [CodeSearchNet](https://arxiv.org/abs/1909.09436) dataset for code document generation tasks in six programming languages.
**!News: We release a new pipeline for this task. The new pipeline only needs 2 p100 GPUs and less t... | CodeBERT/CodeBERT/code2nl/README.md/0 | {
"file_path": "CodeBERT/CodeBERT/code2nl/README.md",
"repo_id": "CodeBERT",
"token_count": 1583
} | 208 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | CodeBERT/CodeExecutor/inference/run.py/0 | {
"file_path": "CodeBERT/CodeExecutor/inference/run.py",
"repo_id": "CodeBERT",
"token_count": 5702
} | 209 |
git clone https://github.com/tree-sitter/tree-sitter-c
git clone https://github.com/tree-sitter/tree-sitter-cpp
git clone https://github.com/tree-sitter/tree-sitter-typescript
git clone https://github.com/tree-sitter/tree-sitter-go
git clone https://github.com/tree-sitter/tree-sitter-javascript
git clone https://github... | CodeBERT/LongCoder/parser/build.sh/0 | {
"file_path": "CodeBERT/LongCoder/parser/build.sh",
"repo_id": "CodeBERT",
"token_count": 209
} | 210 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | CodeBERT/UniXcoder/downstream-tasks/clone-detection/POJ-104/run.py/0 | {
"file_path": "CodeBERT/UniXcoder/downstream-tasks/clone-detection/POJ-104/run.py",
"repo_id": "CodeBERT",
"token_count": 6739
} | 211 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
import glob
import pickle
import json
import tiktoken
from transformers import AutoTokenizer
class CONSTANTS:
# regular version for Codex
api_benchmark = 'random_api'
line_benchmark = 'random_line'
# short version for C... | CodeT/RepoCoder/utils.py/0 | {
"file_path": "CodeT/RepoCoder/utils.py",
"repo_id": "CodeT",
"token_count": 2719
} | 212 |
# Codex CLI - Natural Language Command Line Interface
This project uses [GPT-3 Codex](https://openai.com/blog/openai-codex/) to convert natural language commands into commands in PowerShell, Z shell and Bash.
The Command Line Interface (CLI) was the first major User Interface we used... | Codex-CLI/README.md/0 | {
"file_path": "Codex-CLI/README.md",
"repo_id": "Codex-CLI",
"token_count": 2674
} | 213 |
################################################
## *** Codex CLI plugin function for Bash *** ##
## loaded by $HOME/.codexclirc ##
################################################
create_completion()
{
# Check settings in case the CLI has just been uninstalled
# Note: CODEX_CLI_PATH is defined ... | Codex-CLI/scripts/bash_plugin.sh/0 | {
"file_path": "Codex-CLI/scripts/bash_plugin.sh",
"repo_id": "Codex-CLI",
"token_count": 363
} | 214 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: __init__.py
Description: Python SDK of the Cognitive Face API.
"""
from . import face
from . import face_list
from . import large_face_list
from . import large_face_list_face
from . import large_person_group
from . import large_person_group_person
from . import l... | Cognitive-Face-Python/cognitive_face/__init__.py/0 | {
"file_path": "Cognitive-Face-Python/cognitive_face/__init__.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 150
} | 215 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: test_large_person_group.py
Description: Unittests for Large Person Group section of the Cognitive Face
API.
"""
import uuid
import unittest
import cognitive_face as CF
from . import util
class TestLargePersonGroup(unittest.TestCase):
"""Unittests for ... | Cognitive-Face-Python/cognitive_face/tests/test_large_person_group.py/0 | {
"file_path": "Cognitive-Face-Python/cognitive_face/tests/test_large_person_group.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 1025
} | 216 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: panel_group.py
Description: Group Panel for Python SDK sample.
"""
import os
import wx
import wx.lib.scrolledpanel as scrolled
import util
import model
from view import base
class GroupPanel(base.MyPanel):
"""Group Panel."""
def __init__(self, parent... | Cognitive-Face-Python/sample/view/panel_group.py/0 | {
"file_path": "Cognitive-Face-Python/sample/view/panel_group.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 2168
} | 217 |
export CUDA_VISIBLE_DEVICES=6
python t5_run_eval.py \
--model_name_or_path ./checkpoint/Mod/ContrastExp_finetune_set1_seed1/checkpoint-50000 \
--subtask Mod \
--validation_file test \
--ebatch_size 16 \
--set set1 | ContextualSP/abstraction_probing/code/t5_code/Mod_ContrastExp_test.sh/0 | {
"file_path": "ContextualSP/abstraction_probing/code/t5_code/Mod_ContrastExp_test.sh",
"repo_id": "ContextualSP",
"token_count": 85
} | 218 |
import json
import numpy as np
from data_utils.task_def import TaskType, DataFormat
import tasks
def load_data(file_path, task_def):
data_format = task_def.data_type
task_type = task_def.task_type
label_dict = task_def.label_vocab
if task_type == TaskType.Ranking:
assert data_format == DataFo... | ContextualSP/adaptershare/data_utils/__init__.py/0 | {
"file_path": "ContextualSP/adaptershare/data_utils/__init__.py",
"repo_id": "ContextualSP",
"token_count": 1767
} | 219 |
#!/usr/bin/env bash
###############################
# Batch training script for domain adaptation.
# Xiaodong
###############################
declare -a SCITAIL=('scitail_001' 'scitail_01' 'scitail_1' 'scitail')
## Scitail
for split in "${SCITAIL[@]}"
do
export CUDA_VISIBLE_DEVICES=0
if [ ${split} == "scit... | ContextualSP/adaptershare/experiments/domain_adaptation/run_batch.sh/0 | {
"file_path": "ContextualSP/adaptershare/experiments/domain_adaptation/run_batch.sh",
"repo_id": "ContextualSP",
"token_count": 430
} | 220 |
import os
from sys import path
path.append(os.getcwd())
from data_utils.task_def import DataFormat
def load_conll_ner(file, is_train=True):
rows = []
cnt = 0
sentence = []
label = []
with open(file, encoding="utf8") as f:
for line in f:
line = line.strip()
if len(l... | ContextualSP/adaptershare/experiments/ner/ner_utils.py/0 | {
"file_path": "ContextualSP/adaptershare/experiments/ner/ner_utils.py",
"repo_id": "ContextualSP",
"token_count": 1318
} | 221 |
import torch.nn as nn
from module.common import activation
from module.dropout_wrapper import DropoutWrapper
class Pooler(nn.Module):
def __init__(self, hidden_size, dropout_p=0.1, actf="tanh"):
super(Pooler, self).__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.activation... | ContextualSP/adaptershare/module/pooler.py/0 | {
"file_path": "ContextualSP/adaptershare/module/pooler.py",
"repo_id": "ContextualSP",
"token_count": 279
} | 222 |
# coding=utf-8
# Copyright (c) Microsoft. All rights reserved.
import yaml
import os
import numpy as np
import argparse
import json
import sys
from data_utils import load_data
from data_utils.task_def import TaskType, DataFormat
from data_utils.log_wrapper import create_logger
from experiments.exp_def import TaskDefs
f... | ContextualSP/adaptershare/prepro_std.py/0 | {
"file_path": "ContextualSP/adaptershare/prepro_std.py",
"repo_id": "ContextualSP",
"token_count": 4224
} | 223 |
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