Text Classification
Transformers
PyTorch
English
sentiment-analysis
emotion-classification
multi-label-classification
deberta-v3
nlp
Instructions to use Praphull1/Sentimental-Analyzer-V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Praphull1/Sentimental-Analyzer-V3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Praphull1/Sentimental-Analyzer-V3")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Praphull1/Sentimental-Analyzer-V3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Praphull1/Sentimental-Analyzer-V3: direct link, hf CLI and curl.
- Browser
- Download file 2.12 kB
-
https://huggingface.co/Praphull1/Sentimental-Analyzer-V3/resolve/main/config.json
- Command line
-
hf download hf://Praphull1/Sentimental-Analyzer-V3/config.json
-
curl -L -o config.json https://huggingface.co/Praphull1/Sentimental-Analyzer-V3/resolve/main/config.json
2.12 kB
| { | |
| "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" | |
| } | |
| } |