Hugging Face's logo

Spaces:
rodrigomasini
/
data_only_hallucination_leaderboard
Runtime error

App Files Community
1
data_only_hallucination_leaderboard
File size: 6,364 Bytes 
a8ede2f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
from dataclasses import dataclass
from enum import Enum

import pandas as pd


# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
    name: str
    type: str
    displayed_by_default: bool
    hidden: bool = False
    never_hidden: bool = False
    dummy: bool = False


def fields(raw_class):
    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]


@dataclass(frozen=True)
class AutoEvalColumn:  # Auto evals column
    model_type_symbol = ColumnContent("T", "str", True, never_hidden=True)
    model = ColumnContent("Model", "markdown", True, never_hidden=True)
    average = ColumnContent("Average ⬆️", "number", True)
    arc = ColumnContent("ARC", "number", True)
    hellaswag = ColumnContent("HellaSwag", "number", True)
    mmlu = ColumnContent("MMLU", "number", True)
    truthfulqa = ColumnContent("TruthfulQA", "number", True)
    winogrande = ColumnContent("Winogrande", "number", True)
    gsm8k = ColumnContent("GSM8K", "number", True)
    drop = ColumnContent("DROP", "number", True)
    model_type = ColumnContent("Type", "str", False)
    architecture = ColumnContent("Architecture", "str", False)
    weight_type = ColumnContent("Weight type", "str", False, True)
    precision = ColumnContent("Precision", "str", False)  # , True)
    license = ColumnContent("Hub License", "str", False)
    params = ColumnContent("#Params (B)", "number", False)
    likes = ColumnContent("Hub ❤️", "number", False)
    still_on_hub = ColumnContent("Available on the hub", "bool", False)
    revision = ColumnContent("Model sha", "str", False, False)
    dummy = ColumnContent(
        "model_name_for_query", "str", False, dummy=True
    )  # dummy col to implement search bar (hidden by custom CSS)


@dataclass(frozen=True)
class EvalQueueColumn:  # Queue column
    model = ColumnContent("model", "markdown", True)
    revision = ColumnContent("revision", "str", True)
    private = ColumnContent("private", "bool", True)
    precision = ColumnContent("precision", "str", True)
    weight_type = ColumnContent("weight_type", "str", "Original")
    status = ColumnContent("status", "str", True)


baseline_row = {
    AutoEvalColumn.model.name: "<p>Baseline</p>",
    AutoEvalColumn.revision.name: "N/A",
    AutoEvalColumn.precision.name: None,
    AutoEvalColumn.average.name: 31.0,
    AutoEvalColumn.arc.name: 25.0,
    AutoEvalColumn.hellaswag.name: 25.0,
    AutoEvalColumn.mmlu.name: 25.0,
    AutoEvalColumn.truthfulqa.name: 25.0,
    AutoEvalColumn.winogrande.name: 50.0,
    AutoEvalColumn.gsm8k.name: 0.21,
    AutoEvalColumn.drop.name: 0.47,
    AutoEvalColumn.dummy.name: "baseline",
    AutoEvalColumn.model_type.name: "",
}

# Average ⬆️ human baseline is 0.897 (source: averaging human baselines below)
# ARC human baseline is 0.80 (source: https://lab42.global/arc/)
# HellaSwag human baseline is 0.95 (source: https://deepgram.com/learn/hellaswag-llm-benchmark-guide)
# MMLU human baseline is 0.898 (source: https://openreview.net/forum?id=d7KBjmI3GmQ)
# TruthfulQA human baseline is 0.94(source: https://arxiv.org/pdf/2109.07958.pdf)
# Drop: https://leaderboard.allenai.org/drop/submissions/public
# Winogrande: https://leaderboard.allenai.org/winogrande/submissions/public
# GSM8K: paper
# Define the human baselines
human_baseline_row = {
    AutoEvalColumn.model.name: "<p>Human performance</p>",
    AutoEvalColumn.revision.name: "N/A",
    AutoEvalColumn.precision.name: None,
    AutoEvalColumn.average.name: 92.75,
    AutoEvalColumn.arc.name: 80.0,
    AutoEvalColumn.hellaswag.name: 95.0,
    AutoEvalColumn.mmlu.name: 89.8,
    AutoEvalColumn.truthfulqa.name: 94.0,
    AutoEvalColumn.winogrande.name: 94.0,
    AutoEvalColumn.gsm8k.name: 100,
    AutoEvalColumn.drop.name: 96.42,
    AutoEvalColumn.dummy.name: "human_baseline",
    AutoEvalColumn.model_type.name: "",
}

@dataclass
class ModelTypeDetails:
    name: str
    symbol: str  # emoji


class ModelType(Enum):
    PT = ModelTypeDetails(name="pretrained", symbol="🟢")
    FT = ModelTypeDetails(name="fine-tuned", symbol="🔶")
    IFT = ModelTypeDetails(name="instruction-tuned", symbol="⭕")
    RL = ModelTypeDetails(name="RL-tuned", symbol="🟦")
    Unknown = ModelTypeDetails(name="", symbol="?")

    def to_str(self, separator=" "):
        return f"{self.value.symbol}{separator}{self.value.name}"

    @staticmethod
    def from_str(type):
        if "fine-tuned" in type or "🔶" in type:
            return ModelType.FT
        if "pretrained" in type or "🟢" in type:
            return ModelType.PT
        if "RL-tuned" in type or "🟦" in type:
            return ModelType.RL
        if "instruction-tuned" in type or "⭕" in type:
            return ModelType.IFT
        return ModelType.Unknown


@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


class Tasks(Enum):
    arc = Task("arc:challenge", "acc_norm", AutoEvalColumn.arc.name)
    hellaswag = Task("hellaswag", "acc_norm", AutoEvalColumn.hellaswag.name)
    mmlu = Task("hendrycksTest", "acc", AutoEvalColumn.mmlu.name)
    truthfulqa = Task("truthfulqa:mc", "mc2", AutoEvalColumn.truthfulqa.name)
    winogrande = Task("winogrande", "acc", AutoEvalColumn.winogrande.name)
    gsm8k = Task("gsm8k", "acc", AutoEvalColumn.gsm8k.name)
    drop = Task("drop", "f1", AutoEvalColumn.drop.name)


# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]

EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]

BENCHMARK_COLS = [t.value.col_name for t in Tasks]

NUMERIC_INTERVALS = {
    "?": pd.Interval(-1, 0, closed="right"),
    "~1.5": pd.Interval(0, 2, closed="right"),
    "~3": pd.Interval(2, 4, closed="right"),
    "~7": pd.Interval(4, 9, closed="right"),
    "~13": pd.Interval(9, 20, closed="right"),
    "~35": pd.Interval(20, 45, closed="right"),
    "~60": pd.Interval(45, 70, closed="right"),
    "70+": pd.Interval(70, 10000, closed="right"),
}