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Sentimental-Analyzer-V3

Text Classification
Transformers
PyTorch
English
sentiment-analysis
emotion-classification
multi-label-classification
deberta-v3
nlp
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Sentimental-Analyzer-V3
File size: 2,124 Bytes
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{
    "version": "V3_FINAL_CANDIDATE",
    "model_name": "microsoft/deberta-v3-base",
    "checkpoint": "model/best_v3_model.pt",
    "checkpoint_sha256": "4d3f5a97d352460d41fa884fd337b2de6f58549139135cad1b4b79beefcb2078",
    "max_length": 256,
    "sentiment_classes": 5,
    "emotion_classes": 28,
    "emotions": [
        "admiration",
        "amusement",
        "anger",
        "annoyance",
        "approval",
        "caring",
        "confusion",
        "curiosity",
        "desire",
        "disappointment",
        "disapproval",
        "disgust",
        "embarrassment",
        "excitement",
        "fear",
        "gratitude",
        "grief",
        "joy",
        "love",
        "nervousness",
        "neutral",
        "optimism",
        "pride",
        "realization",
        "relief",
        "remorse",
        "sadness",
        "surprise"
    ],
    "calibration": {
        "strategy": "HYBRID_ISOTONIC_GLOBAL",
        "calibrated_classes": [
            "admiration",
            "anger",
            "annoyance",
            "approval",
            "disappointment",
            "disapproval",
            "disgust",
            "excitement",
            "joy",
            "love",
            "neutral"
        ],
        "raw_classes": [
            "amusement",
            "caring",
            "confusion",
            "curiosity",
            "desire",
            "embarrassment",
            "fear",
            "gratitude",
            "grief",
            "nervousness",
            "optimism",
            "pride",
            "realization",
            "relief",
            "remorse",
            "sadness",
            "surprise"
        ],
        "minimum_support": 10
    },
    "decision": {
        "global_threshold": 0.35,
        "fallback": "highest_calibrated_probability"
    },
    "selection": {
        "selection_dataset": "validation",
        "test_used_for_selection": false,
        "threshold_source": "validation",
        "calibration_source": "validation"
    }
}