text stringlengths 5 22M | id stringlengths 12 177 | metadata dict | __index_level_0__ int64 0 1.37k |
|---|---|---|---|
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
DATA_DIR=../../data/PubMedQA
prefix=pqal_qcl_ansis
RAW_DATA_DIR=${DATA_DIR}/raw
OUTPUT_DIR=${DATA_DIR}/${prefix}-bin
if [ -d "${OUTPUT_DIR}" ]; then
rm -rf ${OUTPUT_DIR}
fi
python rebuild_data.py ${RAW_DATA_DIR} ${prefix}
cp ${DATA_DIR}/..... | BioGPT/examples/QA-PubMedQA/preprocess.sh/0 | {
"file_path": "BioGPT/examples/QA-PubMedQA/preprocess.sh",
"repo_id": "BioGPT",
"token_count": 725
} | 143 |
# Relation Extraction on KD-DTI
## Data
According to the original [KD-DTI dataset](https://github.com/bert-nmt/BERT-DTI), before processing the data, you should first register a DrugBank account, download the xml dataset and replace the entity id with the entity name in the drugbank.
Then, you can process the data by... | BioGPT/examples/RE-DTI/README.md/0 | {
"file_path": "BioGPT/examples/RE-DTI/README.md",
"repo_id": "BioGPT",
"token_count": 235
} | 144 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import logging
from dataclasses import dataclass, field
from typing import Optional, Dict, List, Tuple
from argparse import Namespace
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from fairseq import... | BioGPT/src/transformer_lm_prompt.py/0 | {
"file_path": "BioGPT/src/transformer_lm_prompt.py",
"repo_id": "BioGPT",
"token_count": 1294
} | 145 |
---
license: mit
---
This is a BitBLAS Implementation for the reproduced 1.58bit model from [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B). We replaced the original simulated Int8x3bit Quantized Inference Kernel with BitBLAS INT8xINT2 Kernel. We also evaluated the model's correctness and per... | BitBLAS/integration/BitNet/README.md/0 | {
"file_path": "BitBLAS/integration/BitNet/README.md",
"repo_id": "BitBLAS",
"token_count": 1775
} | 146 |
#!/bin/bash
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
echo "Check MIT License boilerplate..."
PWD="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
# To source code root
pushd "${PWD}/../../" > /dev/null
EXITCODE=0
for SRC_FILE in $(find . -path './3rdparty' -prune -false -o -path '... | BitBLAS/maint/scripts/check_mit_license.sh/0 | {
"file_path": "BitBLAS/maint/scripts/check_mit_license.sh",
"repo_id": "BitBLAS",
"token_count": 464
} | 147 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""Benifit For BitBLAS Schedule"""
class Block:
def __init__(self, start, end, is_free):
self.start = start
self.end = end
self.is_free = is_free
def size(self) -> int:
return self.end - self.start
de... | BitBLAS/python/bitblas/base/roller/bestfit.py/0 | {
"file_path": "BitBLAS/python/bitblas/base/roller/bestfit.py",
"repo_id": "BitBLAS",
"token_count": 1117
} | 148 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
class BitBLASGenerator:
def __init__(self):
# Initialize the generator with configuration
pass
def generate_cuda_code(self):
pass
def generate_header(self):
pass
| BitBLAS/python/bitblas/generator.py/0 | {
"file_path": "BitBLAS/python/bitblas/generator.py",
"repo_id": "BitBLAS",
"token_count": 104
} | 149 |
# Copyright 2018 The apache/tvm Authors. All Rights Reserved.
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under th... | BitBLAS/python/bitblas/gpu/rmsnorm.py/0 | {
"file_path": "BitBLAS/python/bitblas/gpu/rmsnorm.py",
"repo_id": "BitBLAS",
"token_count": 1961
} | 150 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import tvm
from tvm.target import Target
from bitblas.base.roller.arch.cuda import CUDA
from typing import Any, List, Literal, Optional, Tuple, Union
from .operator import Operator, TransformKind
from .impl.matmul_dequantize_impl import select_imp... | BitBLAS/python/bitblas/ops/matmul_dequantize.py/0 | {
"file_path": "BitBLAS/python/bitblas/ops/matmul_dequantize.py",
"repo_id": "BitBLAS",
"token_count": 4993
} | 151 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import tvm
from typing import Optional, List, Dict, Union
from tvm import IRModule
from bitblas import TileDevice
from tvm.runtime import ndarray
from bitblas.utils import match_global_kernel
import re
import ctypes
import os
import tempfile
impor... | BitBLAS/python/bitblas/wrapper/general.py/0 | {
"file_path": "BitBLAS/python/bitblas/wrapper/general.py",
"repo_id": "BitBLAS",
"token_count": 9565
} | 152 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import pytest
import bitblas
from bitblas.ops.ladder_permutate import LadderPermutate, LadderPermutateConfig
import tvm
target = tvm.target.Target("llvm")
# fmt: off
@pytest.mark.parametrize(
"M,N,datatype,dequantize_bits,storage_dtype,prop... | BitBLAS/testing/python/operators/test_ladder_permutate_ops.py/0 | {
"file_path": "BitBLAS/testing/python/operators/test_ladder_permutate_ops.py",
"repo_id": "BitBLAS",
"token_count": 1273
} | 153 |
from ..datasets import SNLIDataset
from .datamodule_base import BaseDataModule
from collections import defaultdict
class SNLIDataModule(BaseDataModule):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def dataset_cls(self):
return SNLIDataset
@propert... | BridgeTower/src/datamodules/snli_datamodule.py/0 | {
"file_path": "BridgeTower/src/datamodules/snli_datamodule.py",
"repo_id": "BridgeTower",
"token_count": 146
} | 154 |
# 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... | BridgeTower/src/modules/bert_model.py/0 | {
"file_path": "BridgeTower/src/modules/bert_model.py",
"repo_id": "BridgeTower",
"token_count": 35244
} | 155 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from data.base_dataset import BaseDataset, get_params, get_transform
from PIL import Image
import util.util as util
import os
import torch
class FaceTestDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train... | Bringing-Old-Photos-Back-to-Life/Face_Enhancement/data/face_dataset.py/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Face_Enhancement/data/face_dataset.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 1513
} | 156 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn.parallel
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Downsample(nn.Module):
# https://github.com/adobe/antialiased-cnns
def __init__(self, pad_type="reflect", filt_size=3,... | Bringing-Old-Photos-Back-to-Life/Global/detection_models/antialiasing.py/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/detection_models/antialiasing.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 1278
} | 157 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import time
from collections import OrderedDict
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_da_model
import util.util as util
from util.visualizer import Visualizer... | Bringing-Old-Photos-Back-to-Life/Global/train_domain_A.py/0 | {
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/train_domain_A.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 2281
} | 158 |
# TEXT ENCODER CONFIG
text_model: 'bert-base-uncased'
text_len: 100
transformer_embed_dim: 768
freeze_text_encoder_weights: True
# AUDIO ENCODER CONFIG
audioenc_name: 'Cnn14'
out_emb: 2048
sampling_rate: 44100
duration: 5
fmin: 50
fmax: 14000
n_fft: 1028
hop_size: 320
mel_bins: 64
window_size: 1024
# PROJECTION SPACE... | CLAP/msclap/configs/config_2022.yml/0 | {
"file_path": "CLAP/msclap/configs/config_2022.yml",
"repo_id": "CLAP",
"token_count": 178
} | 159 |
.. _Command-line Tools:
Command-line Tools
==================
Fairseq provides several command-line tools for training and evaluating models:
- :ref:`fairseq-preprocess`: Data pre-processing: build vocabularies and binarize training data
- :ref:`fairseq-train`: Train a new model on one or multiple GPUs
- :ref:`fairs... | COCO-LM/fairseq/docs/command_line_tools.rst/0 | {
"file_path": "COCO-LM/fairseq/docs/command_line_tools.rst",
"repo_id": "COCO-LM",
"token_count": 714
} | 160 |
#!/bin/bash
SCRIPTS=mosesdecoder/scripts
TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl
REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
BPEROOT=subword-nmt/subword_nmt
BPE_CODE=wmt18_en_de/code
SUBSAMPLE_SIZE=25000000
LANG=de
OUTDIR=wmt18_${L... | COCO-LM/fairseq/examples/backtranslation/prepare-de-monolingual.sh/0 | {
"file_path": "COCO-LM/fairseq/examples/backtranslation/prepare-de-monolingual.sh",
"repo_id": "COCO-LM",
"token_count": 1519
} | 161 |
# Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
## Pre-trained models
Description | Dataset | Model | Test set(s)
---|---|---|---
Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [downl... | COCO-LM/fairseq/examples/conv_seq2seq/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/conv_seq2seq/README.md",
"repo_id": "COCO-LM",
"token_count": 786
} | 162 |
# Fully Sharded Data Parallel (FSDP)
## Overview
Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and
[Google](https://arxiv.org/abs/2004.13336) has shown that data parallel
training can be made significantly more efficient by sharding the model
parameters and optimizer state across data parallel workers. ... | COCO-LM/fairseq/examples/fully_sharded_data_parallel/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/fully_sharded_data_parallel/README.md",
"repo_id": "COCO-LM",
"token_count": 6076
} | 163 |
# 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 Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incr... | COCO-LM/fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py/0 | {
"file_path": "COCO-LM/fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py",
"repo_id": "COCO-LM",
"token_count": 9896
} | 164 |
# Megatron-11b
Megatron-11b is a unidirectional language model with `11B` parameters based on [Megatron-LM](https://arxiv.org/pdf/1909.08053.pdf). Following the original Megatron work, we trained the model using intra-layer model parallelism with each layer's parameters split across 8 GPUs.
Megatron-11b is trained on... | COCO-LM/fairseq/examples/megatron_11b/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/megatron_11b/README.md",
"repo_id": "COCO-LM",
"token_count": 1857
} | 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 itertools
import os
import csv
from collections import defaultdict
from six.moves import zip
import io
import wget
import sys
from su... | COCO-LM/fairseq/examples/multilingual/data_scripts/download_ted_and_extract.py/0 | {
"file_path": "COCO-LM/fairseq/examples/multilingual/data_scripts/download_ted_and_extract.py",
"repo_id": "COCO-LM",
"token_count": 6376
} | 166 |
#!/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.
"""
Generate n-best translations using a trained model.
"""
import os
import subprocess
from contextlib import redi... | COCO-LM/fairseq/examples/noisychannel/rerank_generate.py/0 | {
"file_path": "COCO-LM/fairseq/examples/noisychannel/rerank_generate.py",
"repo_id": "COCO-LM",
"token_count": 7803
} | 167 |
#!/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
from itertools import zip_longest
def replace_oovs(source_in, target_in, vocabulary, source_out, targ... | COCO-LM/fairseq/examples/pointer_generator/preprocess.py/0 | {
"file_path": "COCO-LM/fairseq/examples/pointer_generator/preprocess.py",
"repo_id": "COCO-LM",
"token_count": 1473
} | 168 |
# Finetuning RoBERTa on Winograd Schema Challenge (WSC) data
The following instructions can be used to finetune RoBERTa on the WSC training
data provided by [SuperGLUE](https://super.gluebenchmark.com/).
Note that there is high variance in the results. For our GLUE/SuperGLUE
submission we swept over the learning rate... | COCO-LM/fairseq/examples/roberta/wsc/README.md/0 | {
"file_path": "COCO-LM/fairseq/examples/roberta/wsc/README.md",
"repo_id": "COCO-LM",
"token_count": 2057
} | 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.
from fairseq.modules import LayerNorm, TransformerDecoderLayer, TransformerEncoderLayer
from . import build_monotonic_attention
class Trans... | COCO-LM/fairseq/examples/simultaneous_translation/modules/monotonic_transformer_layer.py/0 | {
"file_path": "COCO-LM/fairseq/examples/simultaneous_translation/modules/monotonic_transformer_layer.py",
"repo_id": "COCO-LM",
"token_count": 932
} | 170 |
#!/usr/bin/env 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.
# Prepare librispeech dataset
base_url=www.openslr.org/resources/12
train_dir=train_960
if [ "$#" -ne 2 ]; then
echo "... | COCO-LM/fairseq/examples/speech_recognition/datasets/prepare-librispeech.sh/0 | {
"file_path": "COCO-LM/fairseq/examples/speech_recognition/datasets/prepare-librispeech.sh",
"repo_id": "COCO-LM",
"token_count": 1499
} | 171 |
[[Back]](..)
# S2T Example: ST on CoVoST
We replicate the experiments in
[CoVoST 2 and Massively Multilingual Speech-to-Text Translation (Wang et al., 2020)](https://arxiv.org/abs/2007.10310).
## Data Preparation
[Download](https://commonvoice.mozilla.org/en/datasets) and unpack Common Voice v4 to a path
`${COVOST_RO... | COCO-LM/fairseq/examples/speech_to_text/docs/covost_example.md/0 | {
"file_path": "COCO-LM/fairseq/examples/speech_to_text/docs/covost_example.md",
"repo_id": "COCO-LM",
"token_count": 2329
} | 172 |
# @package _group_
common:
fp16: true
log_format: json
log_interval: 200
checkpoint:
no_epoch_checkpoints: true
best_checkpoint_metric: wer
task:
_name: audio_pretraining
data: ???
normalize: false
labels: ltr
dataset:
num_workers: 6
max_tokens: 3200000
skip_invalid_size_inputs_valid_test: t... | COCO-LM/fairseq/examples/wav2vec/config/finetuning/base_100h.yaml/0 | {
"file_path": "COCO-LM/fairseq/examples/wav2vec/config/finetuning/base_100h.yaml",
"repo_id": "COCO-LM",
"token_count": 419
} | 173 |
#!/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.
"""
Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset
"""
import argpa... | COCO-LM/fairseq/examples/wav2vec/wav2vec_featurize.py/0 | {
"file_path": "COCO-LM/fairseq/examples/wav2vec/wav2vec_featurize.py",
"repo_id": "COCO-LM",
"token_count": 3207
} | 174 |
/**
* 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 <Python.h>
static PyMethodDef method_def[] = {
{NULL, NULL, 0, NULL}
};
static struct PyModuleDef module_d... | COCO-LM/fairseq/fairseq/clib/libbleu/module.cpp/0 | {
"file_path": "COCO-LM/fairseq/fairseq/clib/libbleu/module.cpp",
"repo_id": "COCO-LM",
"token_count": 293
} | 175 |
# @package _group_
activation: gelu
vq_type: gumbel
vq_depth: 2
combine_groups: true
| COCO-LM/fairseq/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml/0 | {
"file_path": "COCO-LM/fairseq/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml",
"repo_id": "COCO-LM",
"token_count": 35
} | 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 math
import torch
import torch.nn.functional as F
import scipy.stats as stats
import numpy as np
from fairseq import metrics, utils
... | COCO-LM/fairseq/fairseq/criterions/sentence_prediction.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/criterions/sentence_prediction.py",
"repo_id": "COCO-LM",
"token_count": 2879
} | 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 torch.utils.data.dataloader import default_collate
from . import FairseqDataset
class BaseWrapperDataset(FairseqDataset):
def __in... | COCO-LM/fairseq/fairseq/data/base_wrapper_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/base_wrapper_dataset.py",
"repo_id": "COCO-LM",
"token_count": 972
} | 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 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/gpt2_bpe.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/encoders/gpt2_bpe.py",
"repo_id": "COCO-LM",
"token_count": 642
} | 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.
from .block_pair_dataset import BlockPairDataset
from .masked_lm_dataset import MaskedLMDataset
from .masked_lm_dictionary import BertDictiona... | COCO-LM/fairseq/fairseq/data/legacy/__init__.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/legacy/__init__.py",
"repo_id": "COCO-LM",
"token_count": 158
} | 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
from typing import List
logger = logging.getLogger(__name__)
def uniform(dataset_sizes: List[int]):
return [1.0] * len(... | COCO-LM/fairseq/fairseq/data/multilingual/sampling_method.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/multilingual/sampling_method.py",
"repo_id": "COCO-LM",
"token_count": 905
} | 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 numpy as np
from . import BaseWrapperDataset
class SortDataset(BaseWrapperDataset):
def __init__(self, dataset, sort_order):
... | COCO-LM/fairseq/fairseq/data/sort_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/data/sort_dataset.py",
"repo_id": "COCO-LM",
"token_count": 234
} | 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.
"""isort:skip_file"""
import logging
from hydra.core.config_store import ConfigStore
from fairseq.dataclass.configs import FairseqConfig
from ... | COCO-LM/fairseq/fairseq/dataclass/initialize.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/dataclass/initialize.py",
"repo_id": "COCO-LM",
"token_count": 894
} | 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.
"""
A standalone module for aggregating metrics.
Metrics can be logged from anywhere using the `log_*` functions defined
in this module. The l... | COCO-LM/fairseq/fairseq/logging/metrics.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/logging/metrics.py",
"repo_id": "COCO-LM",
"token_count": 3561
} | 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.
from fairseq.model_parallel.modules import ModelParallelMultiheadAttention
from fairseq.modules import TransformerDecoderLayer, TransformerEnc... | COCO-LM/fairseq/fairseq/model_parallel/modules/transformer_layer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/model_parallel/modules/transformer_layer.py",
"repo_id": "COCO-LM",
"token_count": 1192
} | 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 importlib
import os
# automatically import any Python files in the models/huggingface/ directory
models_dir = os.path.dirname(__file_... | COCO-LM/fairseq/fairseq/models/huggingface/__init__.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/huggingface/__init__.py",
"repo_id": "COCO-LM",
"token_count": 258
} | 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.
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nat import NATransformerModel, base_architecture
f... | COCO-LM/fairseq/fairseq/models/nat/nat_crf_transformer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/nat/nat_crf_transformer.py",
"repo_id": "COCO-LM",
"token_count": 2094
} | 187 |
#!/usr/bin/env python3
# Copyright (c) 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. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import log... | COCO-LM/fairseq/fairseq/models/speech_to_text/utils.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/models/speech_to_text/utils.py",
"repo_id": "COCO-LM",
"token_count": 6766
} | 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 functools
from typing import Any, Dict, List, Tuple, Union
import torch
import torch.utils.checkpoint as checkpoint
from fairseq impo... | COCO-LM/fairseq/fairseq/modules/checkpoint_activations.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/checkpoint_activations.py",
"repo_id": "COCO-LM",
"token_count": 3538
} | 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.
"""
Layer norm done in fp32 (for fp16 training)
"""
import torch.nn as nn
import torch.nn.functional as F
class Fp32GroupNorm(nn.GroupNorm):... | COCO-LM/fairseq/fairseq/modules/fp32_group_norm.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/fp32_group_norm.py",
"repo_id": "COCO-LM",
"token_count": 294
} | 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.
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
... | COCO-LM/fairseq/fairseq/modules/linearized_convolution.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/linearized_convolution.py",
"repo_id": "COCO-LM",
"token_count": 1912
} | 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 torch
from ..ops import emulate_int
class ActivationQuantizer:
"""
Fake scalar quantization of the activations using a forwa... | COCO-LM/fairseq/fairseq/modules/quantization/scalar/modules/qact.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/quantization/scalar/modules/qact.py",
"repo_id": "COCO-LM",
"token_count": 1304
} | 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.
import torch.nn.functional as F
def unfold1d(x, kernel_size, padding_l, pad_value=0):
"""unfold T x B x C to T x B x C x K"""
if ker... | COCO-LM/fairseq/fairseq/modules/unfold.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/unfold.py",
"repo_id": "COCO-LM",
"token_count": 263
} | 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 types
import torch
def get_fused_adam_class():
"""
Look for the FusedAdam optimizer from apex. We first try to load the
... | COCO-LM/fairseq/fairseq/optim/fused_adam.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/optim/fused_adam.py",
"repo_id": "COCO-LM",
"token_count": 2948
} | 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 argparse
from typing import Callable, List, Optional
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import g... | COCO-LM/fairseq/fairseq/options.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/options.py",
"repo_id": "COCO-LM",
"token_count": 5496
} | 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 os
import warnings
from argparse import Namespace
from typing import Any, Callable, Dict, List
import torch
from fairse... | COCO-LM/fairseq/fairseq/tasks/fairseq_task.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/tasks/fairseq_task.py",
"repo_id": "COCO-LM",
"token_count": 10809
} | 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 datetime
import logging
import time
import torch
from fairseq.data import (
FairseqDataset,
LanguagePairDataset,
ListDatas... | COCO-LM/fairseq/fairseq/tasks/translation_multi_simple_epoch.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/tasks/translation_multi_simple_epoch.py",
"repo_id": "COCO-LM",
"token_count": 8266
} | 197 |
#include "ATen/ATen.h"
#include "ATen/cuda/CUDAContext.h"
#include "ATen/cuda/detail/IndexUtils.cuh"
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdio.h>
#include <cmath>
#include "ATen/TensorUtils.h"
#include "ATen/AccumulateType.h"
#include <THC/THCGeneral.h>
#include "type_shim.h"
template <typename T, t... | COCO-LM/fairseq/fused_ops/csrc/adam/adam_kernel.cu/0 | {
"file_path": "COCO-LM/fairseq/fused_ops/csrc/adam/adam_kernel.cu",
"repo_id": "COCO-LM",
"token_count": 2098
} | 198 |
import torch
from torch.utils import cpp_extension
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
import site
site.ENABLE_USER_SITE = True
# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))
def get_c... | COCO-LM/fairseq/fused_ops/setup.py/0 | {
"file_path": "COCO-LM/fairseq/fused_ops/setup.py",
"repo_id": "COCO-LM",
"token_count": 3664
} | 199 |
#!/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 sys
"""Reads in a fairseq output file, and verifies that the constraints
(C- lines) are present in the outpu... | COCO-LM/fairseq/scripts/constraints/validate.py/0 | {
"file_path": "COCO-LM/fairseq/scripts/constraints/validate.py",
"repo_id": "COCO-LM",
"token_count": 367
} | 200 |
# 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 functools
import random
import unittest
from multiprocessing import Manager
import torch
import torch.nn as nn
from fa... | COCO-LM/fairseq/tests/distributed/test_bmuf.py/0 | {
"file_path": "COCO-LM/fairseq/tests/distributed/test_bmuf.py",
"repo_id": "COCO-LM",
"token_count": 2873
} | 201 |
# 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
import tests.utils as test_utils
import torch
from fairseq.data import (
BacktranslationDataset,
LanguagePairDataset,... | COCO-LM/fairseq/tests/test_backtranslation_dataset.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_backtranslation_dataset.py",
"repo_id": "COCO-LM",
"token_count": 2106
} | 202 |
# 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 copy
import unittest
import tests.utils as test_utils
import torch
from fairseq.criterions.cross_entropy import CrossE... | COCO-LM/fairseq/tests/test_label_smoothing.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_label_smoothing.py",
"repo_id": "COCO-LM",
"token_count": 2270
} | 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 contextlib
import logging
import unittest
from io import StringIO
from unittest.mock import MagicMock, patch
import torch
from fairseq... | COCO-LM/fairseq/tests/test_train.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_train.py",
"repo_id": "COCO-LM",
"token_count": 4760
} | 204 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
## The script is largely adapted from the huggingface transformers library.
""" Load SQuAD dataset. """
from __future__ import absolute_import, division, print_function
import json
import logging
import math
import collections
from io import op... | COCO-LM/huggingface/utils_for_squad.py/0 | {
"file_path": "COCO-LM/huggingface/utils_for_squad.py",
"repo_id": "COCO-LM",
"token_count": 21501
} | 205 |
datadir: /data/CMIP6/HAMMOZ
name: 10m_v_component_of_wind
cmip_name: vas
era_name: v10
run: r1i1p1f1
version: v20190627
res:
- 1.40625
# - 5.625 | ClimaX/snakemake_configs/HAMMOZ/config_10m_v_component_of_wind.yml/0 | {
"file_path": "ClimaX/snakemake_configs/HAMMOZ/config_10m_v_component_of_wind.yml",
"repo_id": "ClimaX",
"token_count": 76
} | 206 |
year_strings = [
'185001010000-186001010000',
'186001010600-187001010000',
'187001010600-188001010000',
'188001010600-189001010000',
'189001010600-190001010000',
'190001010600-191001010000',
'191001010600-192001010000',
'192001010600-193001010000',
'193001010600-194001010000',
'... | ClimaX/snakemake_configs/TaiESM1/Snakefile/0 | {
"file_path": "ClimaX/snakemake_configs/TaiESM1/Snakefile",
"repo_id": "ClimaX",
"token_count": 1426
} | 207 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
from typing import Optional
import numpy as np
import torch
import torchdata.datapipes as dp
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, IterableDataset
from torchvision.transforms import ... | ClimaX/src/climax/global_forecast/datamodule.py/0 | {
"file_path": "ClimaX/src/climax/global_forecast/datamodule.py",
"repo_id": "ClimaX",
"token_count": 3886
} | 208 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import numpy as np
import torch
from scipy import stats
def mse(pred, y, vars, lat=None, mask=None):
"""Mean squared error
Args:
pred: [B, L, V*p*p]
y: [B, V, H, W]
vars: list of variable names
"""
loss... | ClimaX/src/climax/utils/metrics.py/0 | {
"file_path": "ClimaX/src/climax/utils/metrics.py",
"repo_id": "ClimaX",
"token_count": 4576
} | 209 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import importlib
def find_model_using_name(model_name):
# Given the option --model [modelname],
# the file "models/modelname_model.py"
# will be imported.
model_filename = "models." + model_name + "_model"
model... | CoCosNet-v2/models/__init__.py/0 | {
"file_path": "CoCosNet-v2/models/__init__.py",
"repo_id": "CoCosNet-v2",
"token_count": 479
} | 210 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from .base_options import BaseOptions
class TestOptions(BaseOptions):
def initialize(self, parser):
BaseOptions.initialize(self, parser)
parser.add_argument('--results_dir', type=str, default='./results/', help='saves result... | CoCosNet-v2/options/test_options.py/0 | {
"file_path": "CoCosNet-v2/options/test_options.py",
"repo_id": "CoCosNet-v2",
"token_count": 395
} | 211 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import cv2
import torch
import numpy as np
import math
import random
from PIL import Image
from skimage import feature
from data.pix2pix_dataset import Pix2pixDataset
from data.base_dataset import get_params, get_transform
class DeepFa... | CoCosNet/data/deepfashion_dataset.py/0 | {
"file_path": "CoCosNet/data/deepfashion_dataset.py",
"repo_id": "CoCosNet",
"token_count": 3952
} | 212 |
# 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.architecture import VGG19
from models.networks.correspondence import VGG19_feature_color_torchversion
# Defines the GAN loss which uses either LSGAN or the ... | CoCosNet/models/networks/loss.py/0 | {
"file_path": "CoCosNet/models/networks/loss.py",
"repo_id": "CoCosNet",
"token_count": 2528
} | 213 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import copy
import sys
import torch
from models.networks.sync_batchnorm import DataParallelWithCallback
from models.pix2pix_model import Pix2PixModel
from models.networks.generator import EMA
import util.util as util
class Pix2PixTrain... | CoCosNet/trainers/pix2pix_trainer.py/0 | {
"file_path": "CoCosNet/trainers/pix2pix_trainer.py",
"repo_id": "CoCosNet",
"token_count": 2995
} | 214 |
import random
import torch
from torch.utils.data import Dataset
import os
import pickle
import logging
import json
from tqdm import tqdm
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
while True:
total_length = len(tokens_a) + le... | CodeBERT/CodeExecutor/inference/dataset.py/0 | {
"file_path": "CodeBERT/CodeExecutor/inference/dataset.py",
"repo_id": "CodeBERT",
"token_count": 3399
} | 215 |
import os
import torch
import logging
import argparse
import random
import json
from tqdm import tqdm
import multiprocessing
import time
from itertools import cycle
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data import ConcatDataset
from torch.utils.data.distributed impo... | CodeBERT/CodeReviewer/code/run_finetune_ref.py/0 | {
"file_path": "CodeBERT/CodeReviewer/code/run_finetune_ref.py",
"repo_id": "CodeBERT",
"token_count": 5447
} | 216 |
# Clone Detection (POJ-104)
## Data Download
```bash
cd dataset
pip install gdown
gdown https://drive.google.com/uc?id=0B2i-vWnOu7MxVlJwQXN6eVNONUU
tar -xvf programs.tar.gz
python preprocess.py
cd ..
```
## Dependency
- pip install torch
- pip install transformers
## Fine-Tune
Here we provide fine-tune settings ... | CodeBERT/UniXcoder/downstream-tasks/clone-detection/POJ-104/README.md/0 | {
"file_path": "CodeBERT/UniXcoder/downstream-tasks/clone-detection/POJ-104/README.md",
"repo_id": "CodeBERT",
"token_count": 520
} | 217 |
# Code Summarization
## Data Download
```bash
wget https://github.com/microsoft/CodeXGLUE/raw/main/Code-Text/code-to-text/dataset.zip
unzip dataset.zip
rm dataset.zip
cd dataset
wget https://zenodo.org/record/7857872/files/python.zip
wget https://zenodo.org/record/7857872/files/java.zip
wget https://zenodo.org/record... | CodeBERT/UniXcoder/downstream-tasks/code-summarization/README.md/0 | {
"file_path": "CodeBERT/UniXcoder/downstream-tasks/code-summarization/README.md",
"repo_id": "CodeBERT",
"token_count": 637
} | 218 |
https://allenai.org/data/strategyqa | CodeT/DIVERSE/data/sqa/README.md/0 | {
"file_path": "CodeT/DIVERSE/data/sqa/README.md",
"repo_id": "CodeT",
"token_count": 13
} | 219 |
.PHONY: clean deps install lint pep8 pyflakes pylint test
clean:
find . -name '*.pyc' -print0 | xargs -0 rm -f
find . -name '*.swp' -print0 | xargs -0 rm -f
find . -name '__pycache__' -print0 | xargs -0 rm -rf
-rm -rf build dist *.egg-info .eggs
deps:
pip install -r requirements.txt
install:
python setup.py in... | Cognitive-Face-Python/Makefile/0 | {
"file_path": "Cognitive-Face-Python/Makefile",
"repo_id": "Cognitive-Face-Python",
"token_count": 230
} | 220 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: test_face_list.py
Description: Unittests for Face List section of the Cognitive Face API.
"""
import uuid
import unittest
import cognitive_face as CF
from . import util
class TestFaceList(unittest.TestCase):
"""Unittests for Face List section."""
def... | Cognitive-Face-Python/cognitive_face/tests/test_face_list.py/0 | {
"file_path": "Cognitive-Face-Python/cognitive_face/tests/test_face_list.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 903
} | 221 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: view.py
Description: Base components for Python SDK sample.
"""
import time
import wx
import util
class MyPanel(wx.Panel):
"""Base Panel."""
def __init__(self, parent):
super(MyPanel, self).__init__(parent)
colour_window = wx.SystemSet... | Cognitive-Face-Python/sample/view/base.py/0 | {
"file_path": "Cognitive-Face-Python/sample/view/base.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 4278
} | 222 |
export CUDA_VISIBLE_DEVICES=0
python t5_run_eval.py \
--model_name_or_path ./checkpoint/Com/MainExp_finetune_set1_seed1/checkpoint-50000 \
--subtask Com \
--validation_file test \
--ebatch_size 16 \
--set set1 | ContextualSP/abstraction_probing/code/t5_code/Com_MainExp_test.sh/0 | {
"file_path": "ContextualSP/abstraction_probing/code/t5_code/Com_MainExp_test.sh",
"repo_id": "ContextualSP",
"token_count": 84
} | 223 |
description: Adapter MT-NLU Job on AMLK8s
target:
service: amlk8s
# run "amlt target list amlk8s" to list the names of available AMLK8s targets
name: itpeusp100cl
vc: resrchvc
environment:
image: python:3.6
registry: docker.io # any public registry can be specified here
setup:
- pip install -r requi... | ContextualSP/adaptershare/adapter_train.yaml/0 | {
"file_path": "ContextualSP/adaptershare/adapter_train.yaml",
"repo_id": "ContextualSP",
"token_count": 1269
} | 224 |
# coding=utf-8
# Copyright (c) Microsoft. All rights reserved.
from data_utils import DataFormat
def dump_rows(rows, out_path, data_format):
"""
output files should have following format
:param rows:
:param out_path:
:return:
"""
with open(out_path, "w", encoding="utf-8") as out_f:
... | ContextualSP/adaptershare/experiments/common_utils.py/0 | {
"file_path": "ContextualSP/adaptershare/experiments/common_utils.py",
"repo_id": "ContextualSP",
"token_count": 2070
} | 225 |
# coding=utf-8
# Copyright (c) Microsoft. All rights reserved.
import torch
import math
from torch.nn.init import (
uniform,
normal,
eye,
xavier_uniform,
xavier_normal,
kaiming_uniform,
kaiming_normal,
orthogonal,
)
def linear(x):
return x
def swish(x):
return x * sigmoid(x)
... | ContextualSP/adaptershare/module/common.py/0 | {
"file_path": "ContextualSP/adaptershare/module/common.py",
"repo_id": "ContextualSP",
"token_count": 377
} | 226 |
# Copyright (c) Microsoft. All rights reserved.
from copy import deepcopy
import torch
import logging
import random
from torch.nn import Parameter
from functools import wraps
import torch.nn.functional as F
from data_utils.task_def import TaskType
from data_utils.task_def import EncoderModelType
from .loss import stabl... | ContextualSP/adaptershare/mt_dnn/perturbation.py/0 | {
"file_path": "ContextualSP/adaptershare/mt_dnn/perturbation.py",
"repo_id": "ContextualSP",
"token_count": 2597
} | 227 |
################################
# Assumptions:
# 1. sql is correct
# 2. only table name has alias
# 3. only one intersect/union/except
#
# val: number(float)/string(str)/sql(dict)
# col_unit: (agg_id, col_id, isDistinct(bool))
# val_unit: (unit_op, col_unit1, col_unit2)
# table_unit: (table_type, col_unit/sql)
#... | ContextualSP/awakening_latent_grounding/utils/sql_parser.py/0 | {
"file_path": "ContextualSP/awakening_latent_grounding/utils/sql_parser.py",
"repo_id": "ContextualSP",
"token_count": 11065
} | 228 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# Author: Qian Liu (SivilTaram)
# Original Repo: https://github.com/microsoft/ContextualSP
import torch.nn as nn
import torch.nn.functional as F
import torch
class AttentionUNet(torch.nn.Module):
"""
UNet, down sampling & up sampling fo... | ContextualSP/incomplete_utterance_rewriting/src/attn_unet.py/0 | {
"file_path": "ContextualSP/incomplete_utterance_rewriting/src/attn_unet.py",
"repo_id": "ContextualSP",
"token_count": 1952
} | 229 |
from typing import List, Tuple, Dict, Set, Optional
from copy import deepcopy
from .db_context import SparcDBContext
from .grammar import Grammar, Action, C, T
from .converter import SQLConverter
class SparcWorld:
"""
World representation for spider dataset.
"""
def __init__(self, db_context: SparcD... | ContextualSP/interactive_text_to_sql/src/context/world.py/0 | {
"file_path": "ContextualSP/interactive_text_to_sql/src/context/world.py",
"repo_id": "ContextualSP",
"token_count": 1907
} | 230 |
# LEMON
This repository contains the code and pre-trained models for our EMNLP2022 Findings paper [LEMON: Language-Based Environment Manipulation via Execution-guided Pre-training](https://arxiv.org/pdf/2201.08081.pdf)
Data
-------
The data is in the [release](https://github.com/microsoft/ContextualSP/releases/tag/le... | ContextualSP/lemon/README.md/0 | {
"file_path": "ContextualSP/lemon/README.md",
"repo_id": "ContextualSP",
"token_count": 240
} | 231 |
import json
import logging
import math
import numbers
import os
import platform
import resource
import sys
from collections import MutableMapping
from contextlib import contextmanager
from IPython.core.display import display, HTML
from pyhocon import ConfigFactory
from pyhocon import ConfigMissingException
from pyhoco... | ContextualSP/lemon/executor/gtd/log.py/0 | {
"file_path": "ContextualSP/lemon/executor/gtd/log.py",
"repo_id": "ContextualSP",
"token_count": 1553
} | 232 |
import copy
import numpy as np
import pytest
import tensorflow as tf
from math import exp
from numpy.testing import assert_array_almost_equal
from gtd.ml.model import TokenEmbedder, MeanSequenceEmbedder, ConcatSequenceEmbedder, CandidateScorer, LSTMSequenceEmbedder, \
SoftCopyScorer, Attention, BidiLSTMSequenceEmb... | ContextualSP/lemon/executor/gtd/tests/ml/test_model.py/0 | {
"file_path": "ContextualSP/lemon/executor/gtd/tests/ml/test_model.py",
"repo_id": "ContextualSP",
"token_count": 8384
} | 233 |
from abc import ABCMeta, abstractproperty, abstractmethod
from gtd.utils import cached_property
class Domain(object, metaclass=ABCMeta):
"""Encapsulate all domain-dependent information.
To add a new domain, create a subclass of domain (in a separate file)
and then add it to the get_domain method below.
... | ContextualSP/lemon/executor/strongsup/domain.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/domain.py",
"repo_id": "ContextualSP",
"token_count": 1061
} | 234 |
class Recipe(object):
"""Light-weight class that defines the configs to launch types of
jobs. These jobs are defined for all datasets given by the datasets
property.
Args:
name (string): The name of the config
config_mixins (list[string]): Name of the human-readable configs
base... | ContextualSP/lemon/executor/strongsup/results/recipe.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/results/recipe.py",
"repo_id": "ContextualSP",
"token_count": 3317
} | 235 |
import os
import pytest
from strongsup.predicate import Predicate
from strongsup.value import check_denotation
from strongsup.tables.graph import TablesKnowledgeGraph
from strongsup.tables.executor import TablesPostfixExecutor
from strongsup.tables.structure import Date, NeqInfiniteSet, RangeInfiniteSet, GenericDateIn... | ContextualSP/lemon/executor/strongsup/tests/tables/test_executor.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/tests/tables/test_executor.py",
"repo_id": "ContextualSP",
"token_count": 7918
} | 236 |
import os
import re
from codecs import open
import itertools
from strongsup.example import DelexicalizedContext
from strongsup.evaluation import Evaluation, BernoulliSequenceStat
from strongsup.value import check_denotation
from strongsup.utils import EOU
class Visualizer(object):
"""Subclass around a Decoder,... | ContextualSP/lemon/executor/strongsup/visualizer.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/visualizer.py",
"repo_id": "ContextualSP",
"token_count": 7099
} | 237 |
#!/bin/bash
set -euo pipefail
if [[ ! -f ARC-V1-Feb2018.zip ]]; then
echo Missing file ARC-V1-Feb2018.zip.
echo
echo Download it first: https://s3-us-west-2.amazonaws.com/ai2-website/data/ARC-V1-Feb2018.zip
exit 1
fi
unzip -p ARC-V1-Feb2018.zip ARC-V1-Feb2018-2/ARC-Challenge/ARC-Challenge-Test.jsonl | jq -r ... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/arc/data-challenge/build-dummy-predictions.sh/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/arc/data-challenge/build-dummy-predictions.sh",
"repo_id": "ContextualSP",
"token_count": 174
} | 238 |
# eQASC
This directory has code and data for the eQASC evaluator, as described in the EMNLP 2020 paper [Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering](https://www.semanticscholar.org/paper/Learning-to-Explain%3A-Datasets-and-Models-for-Valid-Jhamtani-Cla... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/eqasc/README.md/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/eqasc/README.md",
"repo_id": "ContextualSP",
"token_count": 603
} | 239 |
from collections import defaultdict
from typing import Dict, List, Tuple
def sentences_from_sentences_file(sentences_filename: str) -> Dict[int, List[str]]:
all_sentences = dict() # type: Dict[Tuple[int, int], str]
with open(sentences_filename) as f:
for line in f:
process_id_str, sentenc... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/process/sentence_file.py/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/process/sentence_file.py",
"repo_id": "ContextualSP",
"token_count": 316
} | 240 |
#!/bin/bash
# This script will test the evaluator, build a docker image, and publish it as
# a Beaker image owned by the Leaderboard user. This is meant to be run by AI2
# after making changes to the QASC evaluator.
set -e
echo --------------------
echo Unit tests
echo --------------------
echo
set -x
python3 test_... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/qasc/evaluator/publish_for_leaderboard.sh/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/qasc/evaluator/publish_for_leaderboard.sh",
"repo_id": "ContextualSP",
"token_count": 432
} | 241 |
<jupyter_start><jupyter_code>import json
import numpy as np
import os
def write(d, f):
json.dump(d, f)
f.write("\n")
# for f in os.listdir("/data_ext/v-xinyupi/PLoGAN/data/gan_corpus/ver_train_src.jsonl"):
current_input = None
is_gold = []
conclusions = []
last_input = None
with open("/data_ext/v-xinyupi/PLoGA... | ContextualSP/logigan/corpus_construction/elastic_search/merge.ipynb/0 | {
"file_path": "ContextualSP/logigan/corpus_construction/elastic_search/merge.ipynb",
"repo_id": "ContextualSP",
"token_count": 1586
} | 242 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.