Hugging Face's logo

Datasets:
applied-ai-018
/
peacock-data-public-datasets-idc-llm_eval

Dataset card Files Community
peacock-data-public-datasets-idc-llm_eval
File size: 6,648 Bytes 
b33c150
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
 
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


logger = get_logger(__name__)


# Metrics
S3_METRICS_BUCKET_PREFIX = "https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
CLOUDFRONT_METRICS_DISTRIB_PREFIX = "https://cdn-datasets.huggingface.co/datasets/metric"
REPO_METRICS_URL = "https://raw.githubusercontent.com/huggingface/evaluate/{revision}/metrics/{path}/{name}"
REPO_MEASUREMENTS_URL = "https://raw.githubusercontent.com/huggingface/evaluate/{revision}/measurements/{path}/{name}"
REPO_COMPARISONS_URL = "https://raw.githubusercontent.com/huggingface/evaluate/{revision}/comparisons/{path}/{name}"

# Evaluation module types
EVALUATION_MODULE_TYPES = ["metric", "comparison", "measurement"]

# Hub
HF_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://huggingface.co")
HF_LIST_ENDPOINT = HF_ENDPOINT + "/api/spaces?filter={type}"
HUB_EVALUATE_URL = HF_ENDPOINT + "/spaces/{path}/resolve/{revision}/{name}"
HUB_DEFAULT_VERSION = "main"

PY_VERSION = version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
PANDAS_VERSION = version.parse(importlib_metadata.version("pandas"))
PYARROW_VERSION = version.parse(importlib_metadata.version("pyarrow"))

USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
USE_JAX = os.environ.get("USE_JAX", "AUTO").upper()

TORCH_VERSION = "N/A"
TORCH_AVAILABLE = False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
    TORCH_AVAILABLE = importlib.util.find_spec("torch") is not None
    if TORCH_AVAILABLE:
        try:
            TORCH_VERSION = version.parse(importlib_metadata.version("torch"))
            logger.info(f"PyTorch version {TORCH_VERSION} available.")
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info("Disabling PyTorch because USE_TF is set")

TF_VERSION = "N/A"
TF_AVAILABLE = False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
    TF_AVAILABLE = importlib.util.find_spec("tensorflow") is not None
    if TF_AVAILABLE:
        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
        for package in [
            "tensorflow",
            "tensorflow-cpu",
            "tensorflow-gpu",
            "tf-nightly",
            "tf-nightly-cpu",
            "tf-nightly-gpu",
            "intel-tensorflow",
            "tensorflow-rocm",
            "tensorflow-macos",
        ]:
            try:
                TF_VERSION = version.parse(importlib_metadata.version(package))
            except importlib_metadata.PackageNotFoundError:
                continue
            else:
                break
        else:
            TF_AVAILABLE = False
    if TF_AVAILABLE:
        if TF_VERSION.major < 2:
            logger.info(f"TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.")
            TF_AVAILABLE = False
        else:
            logger.info(f"TensorFlow version {TF_VERSION} available.")
else:
    logger.info("Disabling Tensorflow because USE_TORCH is set")


JAX_VERSION = "N/A"
JAX_AVAILABLE = False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    JAX_AVAILABLE = importlib.util.find_spec("jax") is not None
    if JAX_AVAILABLE:
        try:
            JAX_VERSION = version.parse(importlib_metadata.version("jax"))
            logger.info(f"JAX version {JAX_VERSION} available.")
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info("Disabling JAX because USE_JAX is set to False")


# Cache location
DEFAULT_XDG_CACHE_HOME = "~/.cache"
XDG_CACHE_HOME = os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
DEFAULT_HF_CACHE_HOME = os.path.join(XDG_CACHE_HOME, "huggingface")
HF_CACHE_HOME = os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

DEFAULT_HF_EVALUATE_CACHE = os.path.join(HF_CACHE_HOME, "evaluate")
HF_EVALUATE_CACHE = Path(os.getenv("HF_EVALUATE_CACHE", DEFAULT_HF_EVALUATE_CACHE))

DEFAULT_HF_METRICS_CACHE = os.path.join(HF_CACHE_HOME, "metrics")
HF_METRICS_CACHE = Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

DEFAULT_HF_MODULES_CACHE = os.path.join(HF_CACHE_HOME, "modules")
HF_MODULES_CACHE = Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

DOWNLOADED_DATASETS_DIR = "downloads"
DEFAULT_DOWNLOADED_EVALUATE_PATH = os.path.join(HF_EVALUATE_CACHE, DOWNLOADED_DATASETS_DIR)
DOWNLOADED_EVALUATE_PATH = Path(os.getenv("HF_DATASETS_DOWNLOADED_EVALUATE_PATH", DEFAULT_DOWNLOADED_EVALUATE_PATH))

EXTRACTED_EVALUATE_DIR = "extracted"
DEFAULT_EXTRACTED_EVALUATE_PATH = os.path.join(DEFAULT_DOWNLOADED_EVALUATE_PATH, EXTRACTED_EVALUATE_DIR)
EXTRACTED_EVALUATE_PATH = Path(os.getenv("HF_DATASETS_EXTRACTED_EVALUATE_PATH", DEFAULT_EXTRACTED_EVALUATE_PATH))

# Download count for the website
HF_UPDATE_DOWNLOAD_COUNTS = (
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Offline mode
HF_EVALUATE_OFFLINE = os.environ.get("HF_EVALUATE_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES


# File names
LICENSE_FILENAME = "LICENSE"
METRIC_INFO_FILENAME = "metric_info.json"
DATASETDICT_JSON_FILENAME = "dataset_dict.json"

MODULE_NAME_FOR_DYNAMIC_MODULES = "evaluate_modules"

HF_HUB_ALLOWED_TASKS = [
    "image-classification",
    "translation",
    "image-segmentation",
    "fill-mask",
    "automatic-speech-recognition",
    "token-classification",
    "sentence-similarity",
    "audio-classification",
    "question-answering",
    "summarization",
    "zero-shot-classification",
    "table-to-text",
    "feature-extraction",
    "other",
    "multiple-choice",
    "text-classification",
    "text-to-image",
    "text2text-generation",
    "zero-shot-image-classification",
    "tabular-classification",
    "tabular-regression",
    "image-to-image",
    "tabular-to-text",
    "unconditional-image-generation",
    "text-retrieval",
    "text-to-speech",
    "object-detection",
    "audio-to-audio",
    "text-generation",
    "conversational",
    "table-question-answering",
    "visual-question-answering",
    "image-to-text",
    "reinforcement-learning",
    "voice-activity-detection",
    "time-series-forecasting",
    "document-question-answering",
]