repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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spektral | spektral-master/tests/test_layers/convolutional/test_gtv_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GTVConv,
"modes": [MODES["SINGLE"], MODES["BATCH"]],
"kwargs": {
"channels": 8,
"delta_coeff": 1.0,
"epsilon": 0.001,
"activation": "relu",
},
"dense": True,
"sparse": True,... | 385 | 16.545455 | 47 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_general_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GeneralConv,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": 256},
"dense": False,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(config)
config["kwargs"]["activ... | 359 | 17.947368 | 47 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_agnn_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.AGNNConv,
"modes": [MODES["SINGLE"], MODES["MIXED"]],
"kwargs": {"channels": 7, "trainable": True},
"dense": False,
"sparse": True,
"edges": False,
}
def test_layer():
run_layer(config)
config["k... | 371 | 18.578947 | 49 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_arma_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.ARMAConv,
"modes": [MODES["SINGLE"], MODES["BATCH"], MODES["MIXED"]],
"kwargs": {
"channels": 8,
"activation": "relu",
"order": 2,
"iterations": 2,
"share_weights": True,
... | 552 | 19.481481 | 63 | py |
spektral | spektral-master/tests/test_layers/convolutional/test_gat_conv.py | from core import MODES, run_layer
from spektral import layers
config = {
"layer": layers.GATConv,
"modes": [MODES["SINGLE"], MODES["BATCH"], MODES["MIXED"]],
"kwargs": {
"channels": 8,
"attn_heads": 2,
"concat_heads": False,
"activation": "relu",
"attn_kernel_initia... | 653 | 21.551724 | 86 | py |
spektral | spektral-master/tests/test_models/core.py | import numpy as np
import scipy.sparse as sp
import tensorflow as tf
from spektral.data import Dataset, Graph, loaders
tf.keras.backend.set_floatx("float64")
MODES = {"SINGLE": 0, "BATCH": 1, "MIXED": 2, "DISJOINT": 3}
batch_size = 16
n_nodes = 11
n_node_features = 7
n_edge_features = 3
def _get_graph(n_nodes, n_f... | 6,747 | 28.858407 | 87 | py |
spektral | spektral-master/tests/test_models/test_general_gnn.py | from spektral import models
from tests.test_models.core import MODES, run_model
config = {
"model": models.GeneralGNN,
"modes": [MODES["SINGLE"], MODES["DISJOINT"], MODES["MIXED"]],
"kwargs": {"output": 32, "connectivity": "cat", "pool": "sum"},
"edges": False,
"dense": False,
"sparse": True,
}... | 557 | 21.32 | 67 | py |
spektral | spektral-master/tests/test_models/test_gcn.py | from spektral import models
from tests.test_models.core import MODES, run_model
config = {
"model": models.GCN,
"modes": [MODES["SINGLE"], MODES["DISJOINT"], MODES["MIXED"], MODES["BATCH"]],
"kwargs": {"n_labels": 32},
"edges": False,
"dense": True,
"sparse": True,
}
def test_model():
run... | 335 | 20 | 82 | py |
spektral | spektral-master/tests/test_data/test_dataset.py | import numpy as np
from spektral import transforms
from spektral.data.dataset import Dataset
from spektral.data.graph import Graph
n_graphs = 10
Ns = np.random.randint(3, 8, n_graphs)
f = 3
s = 3
def test_dataset():
class TestDataset(Dataset):
def read(self):
return [
Graph(
... | 2,067 | 21 | 81 | py |
spektral | spektral-master/tests/test_data/test_loaders.py | import numpy as np
import scipy.sparse as sp
from spektral.data import BatchLoader, DisjointLoader
from spektral.data.dataset import Dataset
from spektral.data.graph import Graph
from spektral.data.loaders import MixedLoader, PackedBatchLoader, SingleLoader
n_graphs = 10
ns = np.random.randint(3, 8, n_graphs)
f = 3
s... | 5,352 | 26.880208 | 85 | py |
spektral | spektral-master/tests/test_data/test_graph.py | import numpy as np
from spektral.data.graph import Graph
n_nodes = 5
n_node_features = 4
n_edge_features = 3
n_out = 2
def _check_graph(x, a, e, y):
g = Graph() # Empty graph
g = Graph(x=x) # Only node features
g = Graph(a=a) # Only adjacency
g = Graph(x=x, a=a, e=e, y=y, extra=1) # Complete gra... | 1,085 | 22.608696 | 81 | py |
spektral | spektral-master/tests/test_data/test_utils.py | import numpy as np
import scipy.sparse as sp
from spektral.data import Dataset, Graph
from spektral.data.utils import batch_generator, to_batch, to_disjoint
ns = np.random.randint(3, 10, 10)
f = 3
a_list = [sp.csr_matrix(np.ones((n, n))) for n in ns]
x_list = [np.random.rand(n, f) for n in ns]
y = [[0, 1]] * len(ns)
... | 1,521 | 24.79661 | 74 | py |
spektral | spektral-master/tests/test_utils/test_logging.py | import shutil
from spektral.models import GCN
from spektral.utils import logging
def test_logging_functions():
log_dir = logging.init_logging()
logging.log("test")
logging.tic(message="test")
logging.toc(message="test")
model = GCN(1)
model.build([(10, 2), (10, 10)])
logging.model_to_str... | 356 | 18.833333 | 36 | py |
spektral | spektral-master/tests/test_utils/test_misc.py | from spektral.utils import misc
def test_misc():
l = [1, [2, 3], [4]]
flattened = misc.flatten_list(l)
assert flattened == [1, 2, 3, 4]
| 150 | 17.875 | 36 | py |
spektral | spektral-master/tests/test_utils/test_convolution.py | import networkx as nx
import numpy as np
import pytest
import scipy.sparse as sp
from spektral.utils import convolution
g = nx.generators.erdos_renyi_graph(10, 0.2)
adj_sp = nx.adjacency_matrix(g).astype("f")
adj = adj_sp.A.astype("f")
degree = np.diag([d[1] for d in nx.degree(g)])
tol = 1e-6
def _run_dense_op(op, ... | 7,195 | 30.423581 | 84 | py |
spektral | spektral-master/tests/test_transforms/test_transforms.py | import numpy as np
import scipy.sparse as sp
from spektral import transforms as tr
from spektral.data import Graph
N = 10
F = 3
S = 4
n_labels = 2
x = np.ones((N, F))
a = sp.csr_matrix(np.ones((N, N)))
e = np.ones((N * N, S))
y_gl = np.ones(n_labels)
y_nl = np.ones((N, n_labels))
y_sc = 1
g_gl = Graph(x=x, a=a, e=e... | 2,444 | 17.807692 | 39 | py |
spektral | spektral-master/docs/autogen.py | from __future__ import print_function, unicode_literals
import glob
import inspect
import os
import re
import shutil
from spektral import data, datasets, layers, models, transforms, utils
EXCLUDE = {}
# For each class to document, it is possible to:
# 1) Document only the class: [classA, classB, ...]
# 2) Document ... | 20,229 | 32.438017 | 87 | py |
blockchain-satellite | blockchain-satellite-master/DDL/mongodb/script_extractor/bulk_blockchain.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Install pymongo library version 3.5.1: pip install pymongo=3.5.1
# Install Cursor : https://github.com/GijsTimmers/cursor
# pip install --user cursor
#import traceback
from pymongo import MongoClient
from Crypto.Cipher import AES
from os.path import expanduser
from da... | 9,013 | 30.62807 | 136 | py |
NLI4CT | NLI4CT-main/pipeline/prepare_data.py | import json
import pandas as pd
TRAIN_PATH = "data/train.json"
DEV_PATH = "data/dev.json"
TEST_PATH = "data/test.json"
###TASK 1
def generate_nli_data(file_path):
'''
Generates data from clinical trials for Task 1: Textual entailment (NLI).
Parameters:
file_path (str): Path to the JSON of the dat... | 5,392 | 34.248366 | 98 | py |
NLI4CT | NLI4CT-main/pipeline/task1_entailment.py | import torch
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from transformers import Trainer, TrainingArguments
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from prepare_data import generate_nli_data
TRAIN_PATH = "data/train.json"
DEV_PATH = "data/dev... | 4,407 | 39.814815 | 97 | py |
NLI4CT | NLI4CT-main/pipeline/task2_evidence.py | import torch
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from transformers import Trainer, TrainingArguments
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from prepare_data import generate_evidence_data
TRAIN_PATH = "data/train.json"
DEV_PATH = "dat... | 4,437 | 40.092593 | 97 | py |
NLI4CT | NLI4CT-main/joint/main.py | import torch
import torch.nn as nn
from sklearn.metrics import f1_score, precision_score, recall_score
from tqdm import tqdm
from torch.utils.data import DataLoader
from transformers import AutoModel, AutoTokenizer, get_cosine_schedule_with_warmup, AdamW
from model import ModelForSequenceClassification
from prepare_j... | 11,141 | 39.369565 | 142 | py |
NLI4CT | NLI4CT-main/joint/prepare_joint.py | import json
import pandas as pd
TRAIN_DATA = "data/train.json"
def generate_multi_data(file_path):
df = pd.read_json(file_path)
df = df.transpose()
#Extract the claims and NLI labels (Entailment/Contradiction).
claims = df.Statement.tolist()
nli_labels = df.Label.tolist()
primary_indices = ... | 3,410 | 36.076087 | 94 | py |
NLI4CT | NLI4CT-main/joint/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, hidden_dim, n_labels, hidden_dropout_prob = 0.1):
super().__init__()
self.dense = nn.Linear(hidden_dim, hidden_dim)
... | 9,675 | 42.390135 | 136 | py |
lm-evaluation-harness | lm-evaluation-harness-master/main.py | import argparse
import datetime
import json
import logging
import os
import lm_eval.evaluator as evaluator
from lm_eval.api import utils
logger = logging.getLogger("main")
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_api_name",
required=True,
he... | 7,346 | 33.013889 | 105 | py |
lm-evaluation-harness | lm-evaluation-harness-master/setup.py | from setuptools import setup, find_packages
from setuptools.command.install import install
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
dev_requires = (["black<=21.12b0", "coverage<=6.2", "mock>=4.0.3", "pytest"],)
install_requires = [
"datasets>=2.0.0",
"codecarbon"... | 1,863 | 27.676923 | 90 | py |
lm-evaluation-harness | lm-evaluation-harness-master/templates/new_prompt_source_task.py | # TODO: Remove all TODO comments once the implementation is complete.
"""
TODO: Add the Paper Title on this line.
TODO: Add the paper's PDF URL (preferably from arXiv) on this line.
TODO: Write a Short Description of the task.
Homepage: TODO: Add the URL to the task's Homepage here.
"""
from lm_eval.api.task import P... | 5,870 | 41.854015 | 106 | py |
lm-evaluation-harness | lm-evaluation-harness-master/scripts/make_table_tasks.py | from lm_eval import tasks
from pytablewriter import MarkdownTableWriter
writer = MarkdownTableWriter()
writer.headers = ["Task Name", "Train", "Val", "Test", "Val/Test Docs", "Metrics"]
values = []
def chk(tf):
if tf:
return "✓"
else:
return " "
for tname, Task in tasks.TASK_REGISTRY.items... | 711 | 19.941176 | 88 | py |
lm-evaluation-harness | lm-evaluation-harness-master/scripts/print_table.py | import json
import sys
from pytablewriter import MarkdownTableWriter
json_file = json.load(open(sys.argv[1]))
results = []
for r in json_file["results"]:
metric = [k[:-7] for k in r.keys() if "_stderr" in k][0]
results.append(
[
r["prompt_name"],
metric,
"{0:.5g}... | 710 | 21.21875 | 87 | py |
lm-evaluation-harness | lm-evaluation-harness-master/scripts/cost_estimate.py | import random
import transformers
from typing import List, Tuple
import lm_eval
from lm_eval.api.model import LM
class DryrunLM(LM):
def __init__(self):
self.tokencost = 0
self.tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2")
self.tokenizer.pad_token = "<|endoftext|>"
... | 2,547 | 27.629213 | 229 | py |
lm-evaluation-harness | lm-evaluation-harness-master/scripts/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/scripts/make_gpt2_test_cases.py | import transformers
import torch
import torch.nn.functional as F
import random
from lm_eval.api.utils import set_seed
data = [
"A multilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN)",
"The term MLP is used ambiguously, sometimes loosely to any feedforward ANN, sometimes stri... | 3,587 | 78.733333 | 1,100 | py |
lm-evaluation-harness | lm-evaluation-harness-master/scripts/get_prompts.py | from itertools import islice
from lm_eval import tasks
ct = 3
for (
tname,
Task,
) in tasks.TASK_REGISTRY.items(): # [('record', tasks.superglue.ReCoRD)]:#
task = Task()
print("#", tname)
docs = islice(
task.validation_docs() if task.has_validation_docs() else task.test_docs(), ct
)... | 585 | 22.44 | 86 | py |
lm-evaluation-harness | lm-evaluation-harness-master/scripts/write_out.py | import argparse
import os
import numpy as np
import lm_eval
from lm_eval.api import utils
EXAMPLE_DIVIDER = "!!@@##@@!! -- Example {i}\n"
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--output_base_path", required=True)
parser.add_argument("--task_name", type=str, required=T... | 2,123 | 33.258065 | 87 | py |
lm-evaluation-harness | lm-evaluation-harness-master/scripts/agg2slim.py | import glob
import json
import os
import logging
logger = logging.getLogger(__name__)
def agg2slim(data):
"""Maps results data to a simpler dictionary.
`data` is expected to have a `results` and `config` fields.
The results should be a list of dictionaries. This function
filters out some of that in... | 1,679 | 27 | 84 | py |
lm-evaluation-harness | lm-evaluation-harness-master/tests/test_version_stable.py | import random
import pytest
import os
import json
import hashlib
import collections
import lm_eval
from lm_eval.api.utils import DEFAULT_SEED, set_seed
def _assert_target(name, ob):
fname = f"tests/testdata/{name}.json"
if os.path.exists(fname):
with open(fname) as fh:
# Use relative tole... | 3,646 | 27.271318 | 92 | py |
lm-evaluation-harness | lm-evaluation-harness-master/tests/test_evaluator.py | import os
import random
import pytest
import lm_eval
import lm_eval.tasks as tasks
import lm_eval.api.model as model
import lm_eval.models as models
import lm_eval.evaluator as evaluator
from lm_eval.api.utils import DEFAULT_SEED, set_seed
# TODO: More fine grained unit tests rather than this big honking integration... | 1,876 | 25.069444 | 77 | py |
lm-evaluation-harness | lm-evaluation-harness-master/tests/test_models_huggingface.py | import unittest.mock as mock
import logging
import pytest
import lm_eval.models
from lm_eval.api.utils import set_seed
logger = logging.getLogger(__name__)
# Only use cpu to avoid non-deterministic CUDA settings.
# See: https://pytorch.org/docs/stable/notes/randomness.html
_DEVICE = "cpu"
@pytest.mark.parametriz... | 14,178 | 42.360856 | 1,045 | py |
lm-evaluation-harness | lm-evaluation-harness-master/tests/test_misc.py | import pytest
import random
import lm_eval.api.metric as metrics
from lm_eval.api.utils import DEFAULT_SEED
def test_bootstrapping():
random.seed(DEFAULT_SEED)
arr = [random.random() for _ in range(1000)]
expected = metrics.mean_stderr(arr)
bootstrapped = metrics.bootstrap_stderr(metrics.mean, arr, i... | 395 | 25.4 | 76 | py |
lm-evaluation-harness | lm-evaluation-harness-master/tests/test_models_openai_completions.py | import pytest
import os
import json
import openai
import mock
import pickle
import hashlib
import logging
import lm_eval.models as models
from lm_eval.api.utils import set_seed
logger = logging.getLogger(__name__)
def _mock_completion(**kwargs):
# Mock completion function
# Loads from a cached+pickled resp... | 7,258 | 44.654088 | 1,045 | py |
lm-evaluation-harness | lm-evaluation-harness-master/tests/test_tasks.py | import logging
import pytest
import numpy as np
from typing import Optional, Tuple
from itertools import islice
from promptsource.templates import Template
import lm_eval.tasks as tasks
from lm_eval.api.task import Task
from lm_eval.api.request import Request
from lm_eval.api.utils import set_seed, DEFAULT_SEED
logg... | 7,964 | 33.331897 | 127 | py |
lm-evaluation-harness | lm-evaluation-harness-master/tests/test_utils.py | import torch
from lm_eval.api.utils import (
get_rolling_token_windows,
make_disjoint_window,
select_continuation_from_batch_left_padding,
split_and_pad_windows,
)
# noinspection DuplicatedCode
def test_get_rolling_token_windows_v1():
gold = [
([-100, 0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2,... | 9,180 | 31.101399 | 87 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/evaluator.py | import collections
import itertools
import json
import logging
import sys
import numpy as np
from tqdm import tqdm
from typing import List, Optional
import lm_eval.models
import lm_eval.tasks
import lm_eval.api.metric
import lm_eval.api.model
from lm_eval.api.utils import DEFAULT_SEED, set_seed
from lm_eval.api.task i... | 13,546 | 37.485795 | 120 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/__init__.py | from .evaluator import evaluate
from .models import get_model, list_model_apis
from .tasks import get_task, get_task_list, list_tasks, get_templates, list_templates
| 165 | 40.5 | 85 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/api/task.py | import abc
import logging
import re
from abc import abstractmethod
from typing import Callable, List, Mapping, Optional, Tuple, Union
import datasets
import numpy as np
import promptsource.templates
from lm_eval.api import utils
from lm_eval.api.metric import (
bits_per_byte,
bleu,
mean,
rouge,
sa... | 30,786 | 36.915025 | 110 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/api/utils.py | import collections
import pathlib
import re
import sys
import torch
from typing import Callable, Final, Iterable, List, Optional, Tuple, Union
from collections.abc import MutableMapping
from transformers import set_seed as transformers_set_seed
# General Utils
class ExitCodeError(Exception):
pass
# Reproducib... | 10,944 | 29.572626 | 116 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/api/model.py | import abc
import hashlib
import json
import os
import torch
import torch.nn.functional as F
from tqdm import tqdm
from typing import Iterable, List, Optional, Tuple, Union
from transformers import BatchEncoding
from lm_eval.api import utils
class LM(abc.ABC):
def __init__(self):
self.cache_hook = CacheH... | 17,599 | 37.681319 | 119 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/api/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/api/request.py | from typing import Any, Optional
REQUEST_RETURN_LENGTHS = {
"loglikelihood": 2,
"greedy_until": None,
"loglikelihood_rolling": None,
}
class Request:
def __init__(
self, request_type: str, args: Optional[Any] = None, index: Optional[int] = None
):
if request_type not in REQUEST_R... | 1,564 | 27.981481 | 88 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/api/metric.py | import logging
import math
import random
import numpy as np
import sacrebleu
import sklearn.metrics
from collections.abc import Iterable
from rouge_score import rouge_scorer
from typing import List, Mapping, Optional
from lm_eval.metrics import sari as sari_impl
logger = logging.getLogger(__name__)
def mean(arr):
... | 11,731 | 30.537634 | 115 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/models/huggingface.py | import math
import torch
import torch.nn.functional as F
import transformers
from typing import List, Mapping, NewType, Optional, Tuple, Union
from tqdm import tqdm
from lm_eval.api import utils
from lm_eval.api.model import TokenLM, TokenSequence
_DeviceMapping = NewType("DeviceMapping", Mapping[str, Union[int, str... | 26,068 | 39.860502 | 120 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/models/__init__.py | import logging
from typing import List, Mapping, Optional, Type
import lm_eval.api.utils
from lm_eval.api.model import LM
from . import dummy
from . import openai_completions
from . import huggingface
logger = logging.getLogger(__name__)
MODEL_API_REGISTRY = {
"hf-causal": huggingface.AutoCausalLM,
"hf-se... | 2,481 | 32.093333 | 94 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/models/openai_completions.py | import logging
import os
import time
import transformers
from typing import Iterable, List, Optional, Tuple, Union
from tqdm import tqdm
from lm_eval.api import utils
from lm_eval.api.model import TokenLM, TokenSequence
logging.getLogger("openai").setLevel(logging.WARNING)
def get_result(response: dict, ctxlen: in... | 9,423 | 33.774908 | 114 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/models/dummy.py | import random
from typing import List, Tuple
from lm_eval.api.model import LM
class DummyLM(LM):
def __init__(self):
super().__init__()
def loglikelihood(
self, requests: List[Tuple[str, str]]
) -> List[Tuple[float, bool]]:
res = []
for _ in requests:
res.appe... | 771 | 23.903226 | 84 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/metrics/sari.py | # =======================================================
# SARI -- Text Simplification Tunable Evaluation Metric
# =======================================================
#
# SOURCE: https://github.com/cocoxu/simplification/blob/master/SARI.py
# This is the implementation provided by the author.
#
# Author: Wei Xu (U... | 8,048 | 33.693966 | 115 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/metrics/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/quac/quac.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 4,434 | 36.584746 | 144 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/quac/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/lambada/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/lambada/lambada.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 4,594 | 34.076336 | 198 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/mutual/mutual.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 4,963 | 35.233577 | 120 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/mutual/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/drop/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/drop/drop.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 7,469 | 37.704663 | 116 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/arithmetic/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/arithmetic/arithmetic.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 8,537 | 38.345622 | 604 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/jigsaw_unintended_bias/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/jigsaw_unintended_bias/jigsaw_unintended_bias.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/wikitext/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/wikitext/wikitext.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 10,731 | 41.418972 | 119 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/coqa/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/coqa/coqa.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 9,080 | 35.914634 | 95 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/logiqa/logiqa.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 4,508 | 35.072 | 98 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/logiqa/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/truthfulqa/truthfulqa.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 6,617 | 37.929412 | 130 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/truthfulqa/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/asdiv/asdiv.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 4,102 | 35.633929 | 108 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/asdiv/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/triviaqa/triviaqa.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 6,110 | 38.681818 | 124 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/triviaqa/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/sat_analogies/sat_analogies.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 4,495 | 33.852713 | 231 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/sat_analogies/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/pile/pile.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 4,556 | 34.88189 | 221 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/pile/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/headqa/headqa.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 6,506 | 38.920245 | 579 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/headqa/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/hendrycks_ethics/hendrycks_ethics.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 8,975 | 38.026087 | 173 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/hendrycks_ethics/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/unscramble/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/unscramble/unscramble.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 4,419 | 38.81982 | 604 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/hendrycks_math/hendrycks_math.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 3,968 | 31.268293 | 144 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/hendrycks_math/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/gsm8k/gsm8k.py | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.... | 3,825 | 34.100917 | 146 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/datasets/gsm8k/__init__.py | 0 | 0 | 0 | py | |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/tasks/anli.py | """
Adversarial NLI: A New Benchmark for Natural Language Understanding
https://arxiv.org/pdf/1910.14599.pdf
Adversarial NLI (ANLI) is a dataset collected via an iterative, adversarial
human-and-model-in-the-loop procedure. It consists of three rounds that progressively
increase in difficulty and complexity, and each ... | 1,761 | 24.536232 | 106 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/tasks/superglue.py | """
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
https://w4ngatang.github.io/static/papers/superglue.pdf
SuperGLUE is a benchmark styled after GLUE with a new set of more difficult language
understanding tasks.
Homepage: https://super.gluebenchmark.com/
TODO: WSC requires free-f... | 9,836 | 26.174033 | 160 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/tasks/lince.py | """
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation
https://aclanthology.org/2020.lrec-1.223.pdf
A centralized benchmark for Linguistic Code-switching Evaluation (LinCE) which contains tasks for different
code-switched language pairs. The code below contains evaluation for sentiment analysis ta... | 3,131 | 50.344262 | 1,482 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/tasks/glue.py | """
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
https://openreview.net/pdf?id=rJ4km2R5t7
The General Language Understanding Evaluation (GLUE) benchmark is a collection of
resources for training, evaluating, and analyzing natural language understanding
systems. GLUE consists of... | 8,977 | 29.127517 | 1,597 | py |
lm-evaluation-harness | lm-evaluation-harness-master/lm_eval/tasks/wmt.py | """
WMT: Workshop on Statistical Machine Translation
WMT is the main event for machine translation and machine translation research.
The conference is held annually in connection with larger conferences on natural
language processing.
Homepage: https://machinetranslate.org/wmt
"""
import promptsource.utils
from typin... | 2,904 | 28.948454 | 108 | py |
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