Hugging Face's logo

Spaces:
UMCU
/
PerplexityViewerPlus
Running

App Files Community
PerplexityViewerPlus
File size: 3,889 Bytes 
297638b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18c81a6
297638b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
# Configuration file for PerplexityViewer

# Default models for different types
DEFAULT_MODELS = {
    "decoder": [
        "gpt2",
        "distilgpt2",
        "microsoft/DialoGPT-small",
        "microsoft/DialoGPT-medium",
        "openai-gpt"
    ],
    "encoder": [
        "bert-base-uncased",
        "bert-base-cased",
        "distilbert-base-uncased",
        "roberta-base",
        "albert-base-v2",
        "UMCU/CardioMedRoBERTa.nl",
        "UMCU/CardioBERTa_base.nl",
        "UMCU/CardioBERTa.nl_clinical",
        "UMCU/CardioDeBERTa.nl",
        "UMCU/CardioDeBERTa.nl_clinical",
        "CLTL/MedRoBERTa.nl",
        "DTAI-KULeuven/robbert-2023-dutch-base",
        "DTAI-KULeuven/robbert-2023-dutch-large"
    ]
}

# Model display settings
MODEL_SETTINGS = {
    "max_length": 512,
    "torch_dtype": "float16",
    "device_map": "auto"
}

# Visualization settings
VIZ_SETTINGS = {
    "max_perplexity_display": 50.0,  # Cap visualization at this perplexity value
    "color_scheme": {
        "low_perplexity": {"r": 46, "g": 204, "b": 113},    # Green for low perplexity (confident)
        "medium_perplexity": {"r": 241, "g": 196, "b": 15},  # Yellow for medium perplexity
        "high_perplexity": {"r": 231, "g": 76, "b": 60},    # Red for high perplexity (uncertain)
        "background_alpha": 0.7,  # Background transparency
        "border_alpha": 0.9       # Border transparency
    },
    "thresholds": {
        "low_threshold": 0.3,    # Below this is low perplexity (green)
        "high_threshold": 0.7    # Above this is high perplexity (red)
    },
    "displacy_options": {
        "ents": ["PP"],
        "colors": {}
    }
}

# Processing settings
PROCESSING_SETTINGS = {
    "epsilon": 1e-10,  # Small value to avoid log(0)
    "default_mask_probability": 0.15,
    "min_mask_probability": 0.05,
    "max_mask_probability": 0.5,
    "default_min_samples": 10,
    "min_samples_range": (5, 50)
}

# UI settings
UI_SETTINGS = {
    "theme": "soft",
    "title": "📈 Perplexity Viewer",
    "description": """
    Visualize per-token perplexity using color gradients.
    - **Red**: High perplexity (model is uncertain)
    - **Green**: Low perplexity (model is confident)

    Choose between decoder models (like GPT) for true perplexity or encoder models (like BERT) for pseudo-perplexity via MLM.
    """,
    "examples": [
        {
            "text": "The quick brown fox jumps over the lazy dog.",
            "model": "gpt2",
            "type": "decoder",
            "mask_prob": 0.15,
            "min_samples": 10
        },
        {
            "text": "The capital of France is Paris.",
            "model": "bert-base-uncased",
            "type": "encoder",
            "mask_prob": 0.15,
            "min_samples": 10
        },
        {
            "text": "Quantum entanglement defies classical physics intuition completely.",
            "model": "distilgpt2",
            "type": "decoder",
            "mask_prob": 0.15,
            "min_samples": 10
        },
        {
            "text": "Machine learning requires large datasets for training.",
            "model": "distilbert-base-uncased",
            "type": "encoder",
            "mask_prob": 0.2,
            "min_samples": 15
        },
        {
            "text": "Artificial intelligence transforms modern computing paradigms.",
            "model": "bert-base-uncased",
            "type": "encoder",
            "mask_prob": 0.1,
            "min_samples": 20
        }
    ]
}

# Error messages
ERROR_MESSAGES = {
    "empty_text": "Please enter some text to analyze.",
    "model_load_error": "Error loading model {model_name}: {error}",
    "processing_error": "Error processing text: {error}",
    "no_tokens_masked": "No tokens were masked during MLM processing.",
    "invalid_model_type": "Invalid model type. Must be 'encoder' or 'decoder'."
}